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Grain yield stability of black soybean lines across three agroecosystems in West Java, Indonesia

  • Acep Atma Wijaya , Haris Maulana , Gatut Wahyu Anggoro Susanto , Dadang Sumardi , Suseno Amien , Dedi Ruswandi and Agung Karuniawan EMAIL logo
Published/Copyright: September 28, 2022
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Open Agriculture
From the journal Open Agriculture Volume 7 Issue 1

Abstract

Black soybean (Glycine soja (L.) Merril) is one of the potential agricultural commodities in Indonesia. The multilocation trial is the primary requirement for variety release before farmers can widely use the new varieties. Various stability measurements on grain yields will provide more accurate information in selecting superior lines. The aims of the study were to: (i) identify the effect of genotype by environment interactions (G×E) on black soybean grain yields; (ii) select the black soybean lines with stable and high grain yields in different agroecosystems; and (iii) determine the best representative environment for testing black soybean lines. The field trials used an augmented design that was applied in three blocks for each location. The research was conducted in West Java, Indonesia, specifically in Sumedang, Indramayu, and Majalengka. The three locations are traditionally soybean production areas in West Java. The results showed that G×E significantly affected grain yields, with a contribution of 38.35%. Based on the results of stability testing using parametric, non-parametric, and genotype plus genotype by environments (GGE) biplot measurements, the G13, G22, G25, and G26 lines were considered the most stable and produced the highest yields in the three agroecosystems. In addition, Sumedang is the most representative location for testing black soybeans in Indonesia. Therefore, the four lines can be proposed as new superior lines for black soybeans with high yields and stability in three environments. Information about the relationship between the yield stability parameters can be used for the most accessible parameter selection.

Keywords: GGE biplot; Glycine soja ; stability analysis; environment

1 Introduction

Soybean is a plant that has many benefits. Black soybean seeds contain anthocyanins, isoflavones, and saponins [1], as well as high protein content (35–40%) when compared to other legumes [2,3]. The presence of these nutrients makes soybeans a functional food. Sumardi et al. [4] reported that the isoflavones contained in black soybeans are in the form of daidzein (0.01–0.21 mg/g) and genistein (0.03–0.21 mg/g). These nutritional contents make black soybean a strategic commodity to be developed.

In Indonesia, soybeans are one of the national strategic commodities. However, soybean production decreases every year. This is due to decreased planting area and other external factors [5]. Java Island is the region that has experienced the most significant decrease in soybean production [6]. Efforts to increase production can be made by developing high-yielding soybeans through plant breeding programs. In addition, the development of soybean lines focused on their adaptability to environmental and climate change, as well as their applicable contents [7]. Susanto and Putri [8] selected soybean lines with high yields in Type C tidal fields. The existence of soybean lines that are adaptive to environmental changes can be used to increase soybean productivity.

The development of black soybean lines was slow compared to yellow soybean as the use of black soybeans was limited to the soy sauce and pharmaceutical industries. Since 2017, Universitas Padjadjaran and Bandung Institute of Technology have collaborated to identify soybean lines with high isoflavone content. Sumardi et al. [4] reported that the UP106 line contained the most stable isoflavones in each environment. This line was used as a cross-bred parent to develop black soybean with a high isoflavone content. The plant material in this study was the F5 lines of black soybean produced from this cross-bred.

The development of black soybean lines was carried out through a systematic and targeted plant breeding program. The purpose of plant breeding programs was to develop plant varieties with wide adaptability and high yields [9]. Information about yield stability is very important in the development of new high-yielding varieties. Andrade et al. [10] reported that the new, improved varieties showed better stability values and lower G×E than the old variety, which had a lower response to changes in environmental conditions. In a previous study, Krisnawati and Adie [11] selected soybean lines with high yield and stability using a genotype plus genotype by environments (GGE) biplot. Liu et al. [12] reported that environmental variance significantly determines soybean yields in several locations. Hence, it was necessary to test the stability and adaptability of lines in many locations. One of the main criteria in plant selection is yield stability and adaptability. Thus, plant selection based on yield stability and adaptability can result in superior cultivars suitable for specific and broad environments.

In multilocation studies, identifying G×E in the measured traits is very important. The occurrence of G×E indicated that various lines could have different responses to environmental changes [13,14]. Several researchers revealed that the emergence of G×E caused the plant breeding process to be less efficient, making the selection process more difficult [10,13,14]. In some cases, soybean grain yields are strongly influenced by G×E due to environmental changes [15,16]. Therefore, G×E analysis is important in evaluating the best line of the black soybean.

Testing the adaptability and yield stability by various methods can provide more comprehensive information than using only one method. The use of a combined analysis of the various stability measurements for selecting stable and high-yielding lines in the multi-environment study is recommended [17]. Researchers have widely used this method to select stable and high-yielding genotypes in multi-environment testing. Rahadi et al. [18] selected chilies that had a high yield and were stable using non-parametric measurements. Shahzad et al. [19] reported that hybrid lines showed a stable appearance in the stability measurement compared to their inbred lines in all characters. In another study, Pinto et al. [20] succeeded in selecting maize with linear regression measurement; Ruswandi et al. [21] succeeded in selecting stable and high-yielding sweet corn based on parametric, non-parametric, and GGE biplot measurements; and Karuniawan et al. [14] also successfully selected stable and high-yielding orange-fleshed sweet potato in West Java, Indonesia, employing parametric, non-parametric, additive main effects and multiplicative interaction, GGE biplot measurements. Milioli et al. [22] reported that the correlation between the measurements could be used for various measurements. In addition, determining a representative environment was necessary for developing lines on a broader scale. Some researchers reported that the environment is very influential on crop yields. Zdziarski et al. [16] reported that environmental changes caused differences in soybean grain yields in Brazil. In another study, high temperatures caused a decrease in crop yields [23,24]. Based on this background, the aims of the study were as follows: (i) to identify the effect of genotype by environment interactions (G×E) on black soybean grain yields; (ii) to select the black soybean lines that have stable and high grain yields in different agroecosystems; and (iii) to determine the best representative environment for testing black soybean lines. The selected new lines help develop new high-yielding black soybean varieties in Indonesia by researchers, growers, and entrepreneurs. Information on the representative environment can be used to develop black soybean varieties.

2 Materials and methods

2.1 Plant material

The genetic material consisted of 34 F5 lines, resulting in crosses for high isoflavones as treatments and six commercial lines as checks. Information about the lines used is presented in Table 1.

Table 1

Black soybean lines used in the study

No. Code Line Pedigree No. Code Line Pedigree
1 G1 BS4 122 × 106 H15 (1) 18 G18 BS74 122 × 106 B16 (3)
2 G2 BS7 UP 106 19 G19 BS75 122 × 106 B16 (4)
3 G3 BS8 122 × 106 E18 (3) (1) 20 G20 BS67 122 × 106 E10 (4)
4 G4 BS10 122 × 106 E18 (3) (3) 21 G21 BS77 122 × 106 B16 (6)
5 G5 BS20 106 × 122 B4 (7) 22 G22 BS78 122 × 106 B16 (7)
6 G6 BS29 106 × 122 B23 (9) 23 G23 BS79 122 × 106 B16 (8)
7 G7 BS30 106 × 122 B23 (1) (10) 24 G24 BS84 106 × 122 E17 (2) (1)
8 G8 BS37 106 × 122 E17 (2) (2) 25 G25 BS87 106 × 122 E17 (2) (4)
9 G9 BS38 122 × 106 E18 (3) (1) 26 G26 BS99 122 × 106 G8 (9)
10 G10 BS41 122 × 106 H15 (2) 27 G27 BS101 122 × 106 G8 (11)
11 G11 BS42 122 × 106 H15 (3) 28 G28 BS102 122 × 106 G8 (12)
12 G12 BS49 122 × 106 E10 (1) 29 G29 BS108 122 × 106 G8 (F4) (2)
13 G13 BS62 106 × 122 B23 (10) 30 G30 BS111 122 × 106 G8 (F4) (5)
14 G14 BS66 122 × 106 E10 (3) 31 G31 BS114 122 × 106 G8 (F4) (8)
15 G15 BS69 122 × 106 E10 (6) 32 G32 BS129 122 × 106 E10 (2) (7)
16 G16 BS70 122 × 106 E10 (7) 33 G33 BS138 106 × 122 A33 (3)
17 G17 BS72 122 × 106 B16 (1) 34 G34 BS140 106 × 122 A33 (5)

2.2 Experimental design and data collection

Experiments have been carried out in three environments in West Java, Indonesia, in 2019–2020, i.e., Majalengka (October 2019 – January 2020), Sumedang (December 2019 – March 2020), and Indramayu (August – November 2019). The main differences between each agroecosystem in this study are the altitude and land conditions. Majalengka was located at an altitude of 745 m.a.s.l., Sumedang 825 m.a.s.l., and Indramayu 57 m.a.s.l. Majalengka is a dry land with a sandy texture, Sumedang is a paddy field with a sandy loam texture, and Indramayu is a paddy field with a clay texture. Weather data for Majalengka and Indramayu were obtained from Meteorological Station Class III Jatiwangi, Kertajati; while the weather data for Sumedang were taken from the Geophysics Station Class I Bandung. Information about weather and soil conditions are presented in Table 2.

Table 2

Weather and soil conditions in the three locations

Locations Majalengka Sumedang Indramayu
tavg (°C) 29.04 23.90 28.98
RH (%) 73.04 81.90 63.93
RR (mm) 8.96 14.88 1.45
ss (h) 6.44 7.50 8.13
Altitude (masl) 745 825 57
pH 5.61 6.39 6.14
SOC 1.77 2.25 2.42
NT 0.16 0.23 0.45
C/N 11 9.78 5.38
P 57.12 17.30 15.65
K 0.13 0.93 0.79

C/N – soil organic carbon/N-total; K – potassium (%); masl – meters above sea level; NT – N-total (%); P – phosphorous (mg/100 g); RH – relative air humidity (%); RR – daily rainfall (mm); SOC – soil organic carbon (%); ss – solar radiation (h); tavg – temperature average (°C).

Soil analysis was carried out at the Laboratory of Soil Fertility and Plant Nutrition, Faculty of Agriculture, Universitas Padjadjaran. All soil chemical analyses were performed in the air-dried subsamples. Soil pH was determined by a pH meter. Total phosphorus (P) was determined by the Bray method. For potassium (K), a soil subsample was extracted with boiling HCl. Total soil organic carbon (SOC) was determined by the Walkley–Black method [25,26].

The field experiment used an augmented design divided into three experimental blocks. The plot used was 3 × 5 m2 with a spacing of 40 × 15 cm2. The population in one plot was 231 plants. Each row consists of 33 plants. Thirty-four (34) new black soybean lines were used as treatments, and six check cultivars (Detam 1 (C1), Dega (C2), Detam 3 (C3), Detam 4 (C4), Detam 5 (C5), and Mutiara (C6)) were used as controls. Information on soybean lines and check varieties used are presented in Table 1. Grain yield per plot (kg) was measured after harvest (12 weeks after planting). The measurement results are then converted into ton/ha.

2.3 Doses of fertilizers

The fertilizers used in this study were chicken dung and NPK. Chicken dung (5 tons/ha) was applied at the beginning of planting to provide nutrient reserves for the growing period. NPK fertilizers consisting of nitrogen (16%), phosphorus (16%), and potassium (16%) were applied at a dosage of 200 kg/ha or 1.2 g/plant in each location. NPK fertilizer was applied to plants 6 weeks after planting. The dose of fertilizer applied to all environments was the same. This estimates the response of each genotype in different environments to the same treatment.

2.4 Statistical analysis

In the augmented design, G×E estimation was carried out on check varieties [27,28]. The data for each check were adjusted based on the average value of each check used for each block and the overall plot [27]. The estimated G×E was calculated as follows [28]:

(1) Y i j = μ + τ i + ν j + ( τ ν ) i j + ε i j ,

where Y ij is the adjustment value of the ith line in jth environment; μ is the average of grand grain yield; τ i is the effect of the ith line; ν j is the effect of the jth environment; (τν) ij is the interaction effect of the ith line in the jth environment, and ε ij is the combined error on the combined analysis of variance (ANOVA) based on check varieties.

Grain yield stability was estimated using parametric and non-parametric measurements. Equations (2) (b i ) and (3) (S 2 d i ) show the linear regression formula expressed by Eberhart and Russell [29].

(2) b i − 1 =   ∑ i ( x i j − X ¯ i − X ¯ j + X ¯ … ) ( X ¯ j − X ¯ … ) ∑ j ( X ¯ j − X ¯ … ) 2 ,

(3) S d i 2 = 1 N − 2 ∑ i ( X ¯ i j − X ¯ i − X ¯ j + X ¯ … ) − ( b i − 2 ) 2 ∑ j ( X ¯ j + X ¯ … ) 2 .

The following equation shows the calculation of the mean variance component (θ i ) [30]:

(4) θ i = p 2 ( p − 1 ) ( q − 1 ) ∑ j − 1 q ( x i j − X ¯ i + X ¯ j + X ¯ ⋯ ) 2 + SSGE 2 ( p − 2 ) ( q − 1 ) .

The GE variance component (θ (i)) [31] was calculated based on the following equation:

(5) θ ( i ) = − p ( p − 1 ) ( p − 2 ) ( q − 1 ) ∑ j − 1 q ( x i j − X ¯ i − X ¯ j + X ¯ … ) 2 + SSGE ( p − 2 ) ( q − 1 ) .

The ecovalence value (W i 2) follows [32] and was estimated based on the following equation:

(6) W i 2 = ∑ ( X i j − X ¯ i − X ¯ j + X ¯ . . ) 2 .

The following equation was used to estimate Shukla’s stability variance ( σ 2 i ) [33]:

(7) σ i 2 = p ( p − 2 ) ( q − 1 ) W i 2 −   ∑ W i 2 ( p − 1 ) ( p − 2 ) ( q − 1 ) .

The coefficient of variance (CV i ) [34] was estimated based on the following equation:

(8) CV i = SD l X ¯ ×   100 .

For parametric measurements, X ij is the grain yield total of the ith line in jth environment, X ¯ i is the average of the grain yield total from ith line at all (three) environments, X ¯ j is the mean of the grain yield in the jth environment, X ¯ … is the average of the grain yield total, and N is the number of environments. p and q are the number of environments and lines; SD l is the standard deviation of G×E.

Equations (9)–(12) show the non-parametric stability measurements based on Nassar and Huehn [35] and Huehn [36] (S(i)) as follows:

(9) S i ( 1 ) = 2 ∑ j n − 1 ∑ j ′ = j + 1 n ∣ r i j − r i j ′ ∣ [ N ( n − 1 ) ] ,

(10) S i ( 2 ) =   ∑ j = 1 n ( r i j − r ¯ i ) 2 ( N − 1 ) ,

(11) S i ( 3 ) =   ∑ j = 1 n ( r i j − r ¯ i ) 2 r ¯ i ,

(12) S i ( 6 ) =   ∑ j = 1 n ∣ r i j − r ¯ i ∣ r ¯ i ,

where r ij is the stability rank of the ith line in the jth environment, r ¯ i . is the average rank of ith line in all environments, and N is the number of an environment.

Equations (13)–(16) show the non-parametric stability measurements by Thennarasu [37] as follows:

(13) NP ( 1 ) = ∑ j = 1 n ∣ r i j ⁎ − M d i ⁎ ∣ N ,

(14) NP ( 2 ) = ∑ j = 1 n ∣ r i j ⁎ − M d i ⁎ ∣ / M d i N ,

(15) NP ( 3 ) = ∑ ( r i j ⁎ − r i ⁎ ) 2 N r ¯ i ,

(16) NP ( 6 ) = 2 x ∑ j = 1 n − 1 ∑ j ′ = j + 1 n ∣ r i j ⁎ − r i ⁎ ∣ / r ¯ i N ( N − 1 ) ,

where r i j ⁎ is the stability rank of the ith line in the jth environment (adjusted data), M d i ⁎ is the adjusted data (median rank), M di is the unadjusted data (median ranks of the same parameters), and N is the number of environments. Non-parametric stability measurement by Kang (KR) [38] was an estimate based on the rank of grain yield from each line and the rank of Shukla’s stability variance. The rank of the two measurements was used as a selection index in KR. The line with a high average grain yield and low stability variance was given a rank of 1 in KR, and vice versa. Grain yield stability based on parametric and non-parametric measurements was analyzed using STABILITYSOFT (online software) [39]. The grain yield stability of the lines is used as described in the GGE biplot [40].

3 Results

3.1 Estimation of G×E on grain yields of black soybean lines

The combined ANOVA on check varieties showed significant differences in environment, genotype, and G×E (Table 3). According to You et al. [28], combined ANOVA of check varieties from the adjusted value can be used to assess G×E in the genotypes tested with the augmented design. As shown in Table 3, the environment contributed 20.91%, the genotype contributed 40.73%, and the G×E contributed 38.35%. A significant influence on these three factors indicates that the locations used have different planting environments caused by various factors such as soil conditions, water, rainfall, and irradiation. The emergence of G×E in multilocation research causes the selection process to be carried out at each location and followed by a stability test with stability measurements.

Table 3

Combined ANOVA of check varieties

Source DF SS MS F Prob. % SS
Genotypes (G) 5 58.607 11.721 5.99 <0.001 40.734
Environment (E) 2 30.086 15.043 7.69 0.002 20.911
G×E 10 55.184 5.518 2.82 0.011 38.355
Error 36 70.450 1.957
Total 53 214.326

DF – degree freedom; G×E – genotype by environment interactions; MS – mean of square; SS – sum of square.

Table 4 shows the results of testing the grain yield potential of each line in each environment using the least significant increase (LSI). Based on the LSI test, in Sumedang, 15 genotypes exceeded the check variety. Thirteen lines have potential that exceeds all check varieties, and of the remaining two lines, one line exceeds one check variety and the other line performs better than four check varieties. In Indramayu, 14 lines exceeded the check variety. Seven of them exceeded all check varieties, one line exceeded one check variety, and the other six lines exceeded five check varieties. In Majalengka, four lines exceeded check varieties, including one line that exceeded five check varieties, two lines that exceeded two check varieties, and one line that exceeded three check varieties.

Table 4

LSI analysis for the black soybean lines

Lines Sumedang Indramayu Majalengka
MY Adj. MY Adj. MY Adj.
G1 8.78 8.33 — 7.78 7.32 — 11.18 10.73 —
G2 2.22 1.77 — 6.85 6.40 — 9.38 8.92 —
G3 5.14 4.69 — 8.25 7.80 — 10.25 9.80 —
G4 3.60 3.15 — 7.40 6.95 — 10.28 9.82 —
G5 4.95 4.50 — 9.00 8.55 — 12.85 12.40 ad
G6 5.97 5.51 — 7.50 7.05 — 8.91 8.45 —
G7 4.05 3.60 — 6.13 5.68 — 9.00 8.54 —
G8 7.50 7.05 — 3.44 2.99 — 5.75 5.30 —
G9 6.00 5.55 — 9.90 9.45 — 5.88 5.42 —
G10 7.00 6.55 — 6.79 6.33 — 5.12 4.66 —
G11 8.40 7.95 — 4.80 4.35 — 5.24 4.79 —
G12 9.23 9.34 — 2.85 2.96 — 4.51 4.62 —
G13 20.31 20.42 a–f 15.53 15.64 a–f 12.13 12.24 ad
G14 10.17 10.28 — 33.00 33.11 a–f 4.44 4.54 —
G15 12.00 12.11 — 14.11 14.22 abcdf 5.99 6.10 —
G16 34.00 34.11 a-f 18.62 18.72 a–f 2.82 2.93 —
G17 31.03 31.13 a–f 15.10 15.21 a–f 3.90 4.01 —
G18 15.50 15.61 a–f 13.24 13.34 abcdf 1.90 2.01 —
G19 24.00 24.11 a–f 10.88 10.99 — 4.12 4.23 —
G20 22.43 22.53 a–f 9.68 9.78 — 2.76 2.87 —
G21 25.64 25.74 a–f 11.84 11.95 d 2.34 2.44 —
G22 19.50 19.61 a–f 12.61 12.72 abcdf 3.93 4.03 —
G23 9.50 9.85 — 21.05 21.40 a–f 1.98 2.32 —
G24 17.49 17.83 a–f 14.10 14.45 abcdf 1.47 1.82 —
G25 23.34 23.68 a–f 15.16 15.51 a–f 2.57 2.92 —
G26 16.13 16.47 a–f 11.40 11.75 — 2.97 3.32 —
G27 11.23 11.58 — 9.60 9.95 — 4.91 5.25 —
G28 12.60 12.95 A 16.24 16.59 a–f 2.83 3.18 —
G29 9.30 9.65 — 9.15 9.50 — 5.26 5.61 —
G30 16.05 16.40 a–f 4.87 5.21 — 3.87 4.21 —
G31 10.75 11.10 — 12.53 12.87 abcdf 7.97 8.32 —
G32 20.72 21.06 a–f 6.43 6.77 — 14.31 14.65 a–e
G33 12.12 12.46 — 13.11 13.46 abcdf 9.39 9.74 —
G34 14.50 14.85 abde 10.83 11.18 — 13.29 13.64 abd
LSI 1.52 2.34 2.11
C1 + LSI 12.79 12.09 12.21
C2 + LSI 13.95 12.69 13.41
C3 + LSI 15.26 12.38 14.50
C4 + LSI 14.02 11.33 11.84
C5 + LSI 13.81 14.91 14.52
C6 + LSI 15.51 12.07 18.08

For lines code, see Table 1. a – exceed check 1; b – exceed check 2; c – exceed check 3; d – exceed check 4; e – exceed check 5; f – exceed check 6; Adj. – adjusted data; MY – mean yield.

3.2 Estimation of grain yield stability of black soybean lines in three environments using parametric and non-parametric stability measurements and GGE biplot

The stability analysis results using parametric and non-parametric measurements are presented in Table 5, while the stability ranking is presented in Table 6. Based on Tables 5 and 6, G10 line was declared the most stable based on NP(1), and G13 line based on S(6), NP(3), NP(4), and KR measurements. The G14 line was declared the most stable based on θᵢ measurement, followed by the G16 and G17. The G26 line was declared the most stable according to S 2 d i measurement, followed by G7 and G6. The G27 line was stated to be the most stable according to the S(1), S(2), S(3), W i 2, σ²ᵢ, b i , and θ(ᵢ) measurements, but this line has a low grain yield. The C1 line was declared the most stable according to CV i measurement, followed by C5 and C2. The C2 line was declared the most stable according to NP(2) measurement, followed by C4 and C5.

Table 5

Yield stability of black soybean lines based on parametric and non-parametric measurements

Lines Y S⁽¹⁾ S⁽²⁾ S⁽³⁾ S⁽⁶⁾ NP⁽¹⁾ NP⁽²⁾ NP⁽³⁾ NP⁽⁴⁾ KR Wᵢ² σ²ᵢ s²dᵢ bᵢ CV i θ₍ᵢ₎ θᵢ
G1 9.25 14.00 147.00 16.33 1.56 15.33 0.50 0.60 0.78 48.00 43.16 21.81 0.33 −0.44 18.90 34.64 28.68
G2 6.15 16.67 166.33 28.51 2.46 16.00 1.05 1.28 1.43 67.00 89.41 46.16 0.37 −1.10 59.04 34.02 40.54
G3 7.88 15.33 139.00 18.53 1.73 14.00 0.71 0.82 1.02 59.00 64.12 32.84 0.14 −0.79 32.68 34.36 34.05
G4 7.09 18.00 196.33 29.45 2.35 16.67 0.85 1.04 1.35 63.00 83.16 42.87 0.17 −1.04 47.24 34.11 38.94
G5 8.93 20.67 254.33 29.35 2.04 19.33 0.65 0.90 1.19 59.00 99.90 51.67 0.12 −1.24 44.22 33.88 43.23
G6 7.46 12.00 89.33 13.40 1.60 13.33 0.82 0.80 0.90 53.00 42.25 21.33 0.02 −0.46 19.71 34.66 28.45
G7 6.39 14.67 148.00 26.91 2.55 16.00 1.00 1.12 1.33 62.00 63.26 32.39 0.01 −0.79 38.88 34.37 33.84
G8 5.56 12.00 82.33 15.94 1.87 10.00 0.93 0.99 1.16 52.00 20.66 9.97 1.09 0.19 36.60 34.95 22.91
G9 7.26 9.33 54.33 7.09 1.09 12.67 0.89 0.67 0.61 51.00 25.10 12.31 1.45 0.13 31.50 34.89 24.05
G10 6.30 6.67 30.33 5.69 1.19 3.00 0.78 0.28 0.63 41.00 9.38 4.03 0.02 0.32 16.34 35.10 20.02
G11 6.15 10.00 56.33 10.90 1.48 4.33 0.81 0.53 0.97 45.00 10.04 4.38 0.55 0.44 31.95 35.09 20.19
G12 5.53 9.33 54.33 11.64 1.79 9.00 1.18 1.07 1.00 50.00 16.75 7.91 2.01 0.63 59.86 35.00 21.91
G13 15.99 2.00 2.33 0.14 0.10 8.00 0.18 0.16 0.06 6.00 3.47 0.92 0.29 1.27 25.70 35.18 18.50
G14 15.87 17.33 217.00 18.87 1.48 25.33 0.43 0.78 0.75 44.00 412.48 216.19 57.85 1.62 95.21 29.66 123.38
G15 10.70 9.33 54.33 4.41 0.68 13.67 0.37 0.40 0.38 30.00 12.29 5.57 1.73 1.09 39.38 35.06 20.77
G16 18.48 22.00 342.33 24.16 1.51 19.33 0.29 0.56 0.78 40.00 312.00 163.31 1.44 4.91 84.36 31.02 97.61
G17 16.68 18.67 223.00 15.93 1.21 22.00 0.28 0.57 0.67 40.00 225.60 117.84 3.31 4.20 81.75 32.18 75.46
G18 10.21 18.67 244.00 24.40 1.80 10.33 0.50 0.53 0.93 43.00 36.51 18.31 0.70 2.27 71.35 34.73 26.98
G19 13.00 16.00 145.33 11.95 1.04 20.33 0.34 0.59 0.66 43.00 104.12 53.90 3.15 3.04 77.75 33.82 44.31
G20 11.62 19.33 217.00 22.84 1.68 18.67 0.49 0.69 1.02 48.00 99.91 51.68 2.84 3.01 85.84 33.88 43.23
G21 13.27 22.67 297.33 26.24 1.65 19.67 0.40 0.63 1.00 45.00 151.93 79.06 2.56 3.61 88.27 33.18 56.57
G22 12.01 13.33 112.00 9.33 1.00 9.67 0.28 0.38 0.56 36.00 43.81 22.15 0.13 2.47 64.95 34.64 28.85
G23 10.84 24.00 340.33 36.46 2.18 20.67 0.52 0.78 1.29 55.00 140.75 73.18 19.06 1.61 88.59 33.33 53.70
G24 11.02 20.67 310.33 29.09 1.91 11.67 0.46 0.62 0.97 42.00 57.72 29.48 0.58 2.65 76.61 34.45 32.41
G25 13.69 20.67 310.33 24.50 1.61 11.67 0.29 0.54 0.82 42.00 107.27 55.55 0.03 3.33 76.43 33.78 45.12
G26 10.17 14.00 120.33 11.28 1.16 6.00 0.29 0.37 0.66 42.00 24.85 12.18 0.00 2.12 65.57 34.89 23.99
G27 8.58 1.33 1.00 0.13 0.13 4.33 0.71 0.30 0.08 29.00 0.34 -0.73 0.04 1.04 38.24 35.22 17.70
G28 10.56 19.33 211.00 18.35 1.30 13.33 0.41 0.46 0.84 46.00 45.87 23.24 4.86 1.77 65.69 34.61 29.38
G29 7.90 4.00 10.33 1.35 0.48 7.33 0.77 0.34 0.26 32.00 3.08 0.72 0.17 0.69 28.98 35.19 18.40
G30 8.26 16.67 170.33 23.77 2.05 17.00 0.68 0.92 1.16 49.00 41.14 20.75 4.19 1.77 81.83 34.67 28.16
G31 10.42 7.33 31.00 2.82 0.55 11.33 0.41 0.38 0.33 29.00 9.55 4.12 0.73 0.53 22.06 35.10 20.06
G32 13.82 22.00 316.33 24.03 1.54 10.33 0.30 0.55 0.84 36.00 94.57 48.87 13.26 0.70 51.79 33.95 41.86
G33 11.54 6.00 22.33 1.76 0.42 9.67 0.23 0.28 0.24 20.00 7.62 3.11 0.37 0.50 16.70 35.12 19.57
G34 12.87 11.33 74.33 5.19 0.65 10.33 0.18 0.33 0.40 26.00 22.58 10.98 0.97 0.11 14.53 34.92 23.40
C1 10.84 8.67 49.00 4.26 0.70 7.00 0.20 0.22 0.38 31.00 16.85 7.96 0.02 0.08 3.42 35.00 21.93
C2 11.82 7.33 34.33 2.61 0.51 7.67 0.14 0.21 0.28 26.00 18.34 8.75 0.11 0.06 5.35 34.98 22.32
C3 12.52 9.33 54.33 3.93 0.60 11.67 0.19 0.33 0.34 26.00 22.30 10.83 0.63 0.05 11.93 34.93 23.33
C4 10.88 8.67 42.33 3.63 0.57 5.00 0.17 0.19 0.37 26.00 12.04 5.43 0.33 0.30 13.14 35.06 20.70
C5 12.88 10.67 65.33 4.40 0.58 7.00 0.17 0.27 0.36 26.00 23.95 11.70 0.07 −0.09 4.33 34.90 23.75
C6 13.70 14.00 114.33 8.07 0.82 19.67 0.27 0.51 0.49 32.00 59.12 30.21 1.78 −0.54 22.01 34.43 32.77

For lines code, see Table 1. Cv i  – coefficient of variance; NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾ – Thennarasu; S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁶⁾ – Nassar and Huhn; s²dᵢ, bᵢ – linear regression; Wᵢ² – Wricke’s ecovalence; Y – grain yield; θ₍ᵢ₎ – GE variance component; θᵢ – mean variance component; σ²ᵢ – Shukla’s stability variance; KR – Kang rank.

Table 6

Rank of yield stability of black soybean lines based on parametric and non-parametric measurements

Rank Y S⁽¹⁾ S⁽²⁾ S⁽³⁾ S⁽⁶⁾ NP⁽¹⁾ NP⁽²⁾ NP⁽³⁾ NP⁽⁴⁾ KR Wᵢ² σ²ᵢ s²dᵢ bi CV i θ₍ᵢ₎ θᵢ AR RAR SD
G1 26 20 24 24 25 27 24 25 22 28 22 22 16 17 9 22 19 21.88 19 4.54
G2 37 26 26 36 39 28 39 40 40 40 30 30 19 4 25 30 11 29.41 38 10.18
G3 31 24 22 26 30 26 30 33 33 36 28 28 12 6 17 28 13 24.88 28 8.09
G4 34 29 28 39 38 30 35 37 39 39 29 29 14 2 23 29 12 28.59 36 10.24
G5 27 34 34 38 35 33 27 34 36 36 32 32 10 7 22 32 9 28.12 34 9.77
G6 32 17 18 21 26 23 34 32 26 34 21 21 3 15 10 21 20 22.00 20 8.13
G7 35 23 25 35 40 28 38 39 38 38 27 27 2 5 20 27 14 27.12 32 11.25
G8 39 17 17 23 33 14 37 36 34 33 13 13 26 24 18 13 28 24.59 25 9.11
G9 33 10 12 14 16 22 36 28 15 32 18 18 28 25 15 18 23 21.35 18 7.60
G10 36 5 5 13 18 1 32 6 16 18 5 5 5 20 7 5 36 13.71 11 11.20
G11 38 14 14 17 22 2 33 18 28 25 7 7 20 16 16 7 34 18.71 17 10.00
G12 40 10 12 19 31 11 40 38 30 31 10 10 31 11 26 10 31 23.00 21 11.42
G13 3 2 2 2 1 10 5 1 1 1 3 3 15 8 13 3 38 6.53 1 8.93
G14 4 28 30 27 21 40 21 30 20 24 40 40 40 19 40 40 1 27.35 33 11.96
G15 21 10 10 11 11 25 17 15 11 10 9 9 29 3 21 9 32 14.88 13 7.81
G16 1 37 40 31 23 33 14 22 21 16 39 39 27 40 36 39 2 27.06 31 12.53
G17 2 30 32 22 19 39 11 23 19 16 38 38 35 39 34 38 3 25.76 29 12.17
G18 24 30 33 32 32 15 24 19 27 22 19 19 23 32 30 19 22 24.82 27 5.60
G19 9 25 23 20 15 37 16 24 18 22 34 34 34 36 33 34 7 24.76 26 9.40
G20 15 32 30 28 29 32 23 29 32 28 33 33 33 35 37 33 8 28.82 37 7.14
G21 8 39 35 34 28 35 18 27 30 25 37 37 32 38 38 37 4 29.53 39 10.19
G22 13 19 19 16 14 12 10 14 14 14 23 23 11 33 27 23 18 17.82 15 6.06
G23 19 40 39 40 37 38 26 31 37 35 36 36 39 18 39 36 5 32.41 40 9.54
G24 17 34 36 37 34 19 22 26 29 19 25 25 21 34 32 25 16 26.53 30 6.76
G25 7 34 36 33 27 19 12 20 23 19 35 35 6 37 31 35 6 24.41 23 11.01
G26 25 20 21 18 17 5 13 12 17 19 17 17 1 31 28 17 24 17.76 14 7.24
G27 28 1 1 1 2 2 29 8 2 8 1 1 7 1 19 1 40 8.94 2 11.94
G28 22 32 29 25 20 23 19 16 25 27 24 24 37 22 29 24 17 24.41 24 5.17
G29 30 3 3 3 4 8 31 11 4 12 2 2 13 10 14 2 39 11.24 6 11.12
G30 29 26 27 29 36 31 28 35 35 30 20 20 36 23 35 20 21 28.29 35 5.74
G31 23 6 6 6 6 18 20 13 6 8 6 6 24 13 12 6 35 12.59 9 8.38
G32 5 37 38 30 24 15 15 21 24 14 31 31 38 9 24 31 10 23.35 22 10.26
G33 16 4 4 4 3 12 8 7 3 2 4 4 18 14 8 4 37 8.94 3 8.49
G34 11 16 16 12 10 15 4 10 12 3 15 15 25 26 6 15 26 13.94 12 6.66
C1 20 8 9 9 12 6 7 4 10 11 11 11 4 28 1 11 30 11.29 7 7.62
C2 14 6 7 5 5 9 1 3 5 3 12 12 9 29 3 12 29 9.65 4 7.96
C3 12 10 11 8 9 19 6 9 7 3 14 14 22 30 4 14 27 12.88 10 7.44
C4 18 8 8 7 7 4 2 2 9 3 8 8 17 21 5 8 33 9.88 5 7.82
C5 10 15 15 10 8 6 3 5 8 3 16 16 8 27 2 16 25 11.35 8 7.10
C6 6 20 20 15 13 35 9 17 13 12 26 26 30 12 11 26 15 18.00 16 7.85

For lines code, see Table 1. AR – average rank; Cv i  – coefficient of variance; NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾ – Thennarasu; RAR – Rank of Average Rank; S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁶⁾ – Nassar and Huhn; s²dᵢ, bᵢ – linear regression; SD – Standard Deviation; SR – sum rank; Wᵢ² – Wricke’s ecovalence; Y – grain yield; θ₍ᵢ₎ – GE variance component; θᵢ – mean variance component; σ²ᵢ – Shukla’s stability variance; KR – Kang rank.

Cluster analysis (hierarchical cluster analysis [HCA]) was used to classify the black soybean lines. The results of cluster analysis of the tested lines are presented in Figure 1. In this figure, the lines were divided into several groups, namely, the unstable lines with low yield, which contain G2, G4, G7, G3, G5, G18, G24, G28, G30, and G32. The second group contains unstable lines but has high yields (G14, G16, G17, G19, G21, and G25) and medium yields (G20 and G23). The third group contains unstable genotypes with low grain yields, namely, G10, G11, G8, G12, G1, G6, and G9. While the fourth group contains stable lines with high yield, namely, G34, C3, C2, C4, C1, C5, G27, G29, G13, G33, G15, G31, G22, G26, and C6. The second and fourth groups were ideal groups.

Figure 1 Dendrogram (Euclidean distance) of grain yield stability of black soybean lines based on parametric and non-parametric measurements.
Figure 1

Dendrogram (Euclidean distance) of grain yield stability of black soybean lines based on parametric and non-parametric measurements.

The relationship between each stability measurement and grain yields was estimated using principal component analysis (PCA). The first three PCs with an eigenvalue >1 accounted for 88.89% of the grain yield of the total variation using numerical stability measurements (parametric and non-parametric; Table 7). The PCA biplot was obtained based on PC 1 and PC 2 (Figure 2). Figure 2 shows that the stability measurements of grain yields were divided into four groups, i.e., the first group contains Y and NP(2); the second group contains measurements of S(1), S(2), S(3), S(6), NP(1), NP(3), NP(4), KR, CV i , W i 2, σ²ᵢ, b i , and θ(ᵢ); the third group contains measurements S 2 d i and b i ; and the fourth group contains only one measurement, which was far from all groups (negatively correlated with other measurements).

Table 7

PCA of the stability measurements on the black soybean yields

PC PC1 PC2 PC3
Eigenvalue 10.31 3.78 1.02
Variance (%) 60.65 22.26 5.99
Cumulative (%) 60.65 82.91 88.89

PC – principal component.

Figure 2 PCA of stability measurements for black soybean grain yield; Cv i  – coefficient of variance; NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾ – Thennarasu; S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁶⁾ – Nassar and Huhn; s²dᵢ, bᵢ – linear regression; Wᵢ² – Wricke’s ecovalence; Y – Grain yield; θ₍ᵢ₎ – GE variance component; θᵢ – mean variance component; σ²ᵢ – Shukla’s stability variance; KR – Kang rank.
Figure 2

PCA of stability measurements for black soybean grain yield; Cv i  – coefficient of variance; NP⁽¹⁾, NP⁽²⁾, NP⁽³⁾, NP⁽⁴⁾ – Thennarasu; S⁽¹⁾, S⁽²⁾, S⁽³⁾, S⁽⁶⁾ – Nassar and Huhn; s²dᵢ, bᵢ – linear regression; Wᵢ² – Wricke’s ecovalence; Y – Grain yield; θ₍ᵢ₎ – GE variance component; θᵢ – mean variance component; σ²ᵢ – Shukla’s stability variance; KR – Kang rank.

The stability analysis results using the GGE biplot are presented in Figures 3 and 4. Figure 3 shows the “mean vs stability” of the GGE biplot. There were 16 lines identified in the black soybean population tested that had grain yields more than the total average grain yield (to the right of the Y-axis), namely G13, G14, G15, G16, G17, G18, G19, G20, G21, G22, G23, G24, G25, G26, G28, and G32. In comparison, 23 other genotypes had lower yields (below the overall mean). The G13, G18, G22, G24, G25, and G26 lines were the most stable lines (closest distance to the X-axis), while the G14, G15, G16, G17, G19, G20, G21, G23, G28, and G32 lines were unstable. The G25 is an ideal line (close to the ideal point and X-axis), so it has high yield performance (above overall average grain yield) in various environments (marginal and optimal).

Figure 3 GGE biplot “mean vs stability” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.
Figure 3

GGE biplot “mean vs stability” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.

Figure 4 GGE biplot “which-won-where” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.
Figure 4

GGE biplot “which-won-where” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.

Figure 4 shows the “which-won-where” display of the GGE biplot. The analysis showed that the environment was divided into seven sectors. Each environment was in a different sector. Peak lines in each environment indicate that these lines have the highest yields in each environment. For example, G2 was at the peak of the Majalengka sector, G14 was at the peak of the Indramayu sector, and G16 was at the peak of the Sumedang sector. In addition, some lines are at the top of the sector outside the environment, namely G12 and G32.

3.3 Determine representative locations for testing black soybean lines

The resulting PC value was 85.7%, which means that the analysis results provide a high enough contribution to describe the test results. Figure 5 shows the “representative vs discriminative” of the GGE biplot; each location’s vector lengths showed the difference. Majalengka has a shorter vector, so it was less representative for the test location, while Sumedang has the most extended vector, so it was a representative location and suitable to be used as a test location and select a superior line. Indramayu has a long vector but has a larger angle so that it can be used as a location for specific location lines.

Figure 5 GGE biplot “representativeness vs discriminativeness” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.
Figure 5

GGE biplot “representativeness vs discriminativeness” for black soybean grain yield in West Java. SMD – Sumedang, IDM – Indramayu, MJL – Majalengka. For lines code, see Table 1.

4 Discussions

The results of the combined ANOVA showed that genotype, environment, and genotype-by-environment interactions (G×E) significantly affected black soybean yields. The emergence of G×E in multilocation testing causes the selection process to be less efficient [9,14,41]. The main effect of the genotype was very significant, with a high contribution (40,734%). This shows that differences in genotype performance in various environments result in G×E. G×E in multilocation testing requires researchers to identify stable and superior genotypes [42]. The results also showed that the variation due to genotype was more significant than that of G×E, which means that the differences between genotypes varied greatly in each planting environment, as presented in Table 4. Similar observations were obtained by Tolorunse et al. [42] on soybeans.

The yield variation shown by the environment indicates that the test environment used was varied. Although the temperature distribution was relatively uniform and favorable in the three agroecosystems during the planting period, the rainfall in each environment was quite variable, which could be the main cause of variation in grain yields across environments [16,23]. Likewise, the high-performance lines in the Sumedang and Indramayu neighborhoods can be traced to the similar rainfall patterns shown in the two neighborhoods. Genotype and environmental interactions (G×E) play an important role in breeding black soybeans adaptable to a wide range of environments [9,14,41,43]. This interaction was validated by very real differences in the black soybean yields. These results were in line with the findings of Vaezi et al. [17] on barley in Iran, which reported that barley yields were strongly influenced by genotype, environmental, and G×E. Therefore, choosing a representative location was needed for future soybean development.

The grain yields in Sumedang and Indramayu are relatively higher than in Majalengka. However, environmental factors in each location can make a difference in the grain yields of each line. For example, some researchers reported that soil conditions affect soybean yields, especially nitrogen [15,44,45,46]. In this study, the soil nitrogen content in Sumedang and Indramayu was more significant than in Majalengka. As a result, the soil testing results align with the grain yields at each location, where Majalengka has lower average yields than Sumedang and Indramayu. This is similar to Gai et al. [45], who stated that providing nitrogen in the soil can increase soybean yields. Thus, differences in soil and other environmental conditions affect variations in soybean grain yields.

The stability analysis results on black soybean based on parametric and non-parametric measurements showed that the tested lines were divided into clear groups. Vaezi et al. [17] and Karuniawan et al. [14] also expressed a similar point. Figure 1 shows an ideal group, namely high and stable yields in three environments. They were the lines G14, G16, G17, G19, G21, G25, G34, G27, G29, G13, G33, G15, G31, G22, G26, C1, C2, C3, C4, C5, and C6. In other studies, parametric and non-parametric measurements have also succeeded in selecting stable and high-yield genotypes, including Ahmadi on grass pea [47], Vaezi on barley [17], Karuniawan on orange-fleshed sweet potato [14], and Ruswandi on maize hybrids [48] Thus, lines in the ideal group can be recommended as hopeful genotypes with high and stable yields.

Figure 2 reveals the stability measurement groupings. According to Vaezi et al. [17], measurements in the same group with yields show a dynamic stability model, where these measurements can select genotypes in a favorable environment. Conversely, measurements in different groups represent a static stability model, meaning they can select genotypes in an unfavorable environment. For example, NP(2) was a dynamic stability measurement model in this study because it was in the same group as the harvest yield. Therefore, this measurement was used to select black soybean genotypes in a favorable environment. Meanwhile, other stability measurements are included in static stability measurements.

In GGE biplot analysis on the black soybean population, PC1 explained 62.800% of the total variation, and PC2 explained 22.900% of the total variation, contributing to 85.700% of the total variation for black soybean yields (Figures 3–5). The “mean vs stability” biplot is presented in Figure 3. In Figure 3, there are two main axes: the X-axis, which shows the stability of the grain yield soybean lines, and the Y-axis, which shows the mean grain yield performance of the soybean lines [40]. Sixteen lines had yields above the overall average (located on the right side of the Y-axis), while the other 24 lines had smaller yields (below the overall average). The G13, G18, G22, G24, G25, and G26 lines were the most stable lines (grain yield was above average). The genotype that has the closest distance to the ideal genotype value is G25. This line could produce high yields in all environments (both marginal and optimal). In addition, G25 could produce maximum production in two planting environments. In another study, the GGE biplot successfully selected stable sweet potatoes in diverse environments in Tanzania [49].

The “which won where” pattern indicates that the three locations have seven sectors with different winning lines (Figure 4). The peak line was G2 in Majalengka, G14 in Indramayu, and G16 in Sumedang. The genotypes at the top of each sector indicate that these genotypes are adaptive to specific environments [9,40,43,50,51]. Genotypes approaching the sector center point have a low and stable G×E effect [40]. In this study, the genotypes approaching the sector center point are C5, G31, G33, C2, C3, G26, and G27. However, these genotypes were more dominant, yielding below the overall average. Ruswandi et al. [41] reported that maize hybrids near the sector axis had stable yields but did not guarantee high yields.

According to the “representative vs discriminative” of the GGE biplot (Figure 5), the test sites can be classified into three types [40]. The Type I environment has a short vector and provides little information about the tested genotype, so it should not be used as a test environment. The Type II environment has a long vector and a small angle with an average abscissa environment, making it ideal for selecting superior genotypes. Finally, the Type III environment has a significant vector of length and angle with the mean abscissa environment, so it cannot be used to select the ideal genotype but helps select unstable (adaptive) genotypes.

The research results on black soybean lines showed that Majalengka is a Type I environment and should not be used as a test environment. Sumedang is the ideal location (Type II) to select a superior genotype because of its high distinguishing and representativeness and a smaller angle to the abscissa. Indramayu is a Type III environment and should not be used to select superior genotypes but can be used to select adaptive or region-specific genotypes. In addition, the ideal genotype is a genotype that has high yield performance and a high level of stability; a higher yield performance and stability are closer to the vertices in the GGE biplot. According to our results, the ideal genotype in this line is G25.

5 Conclusion

Genotype by environment interactions (G×E) significantly affected black soybean yields with 38.35% of the total variation. Overall, parametric, non-parametric, and GGE biplot measurements yielded consistent results in selecting stable and high-yielding lines. The G14, G16, G17, G19, G21, G25, G34, G27, G29, G13, G33, G15, G31, G22, G26, C1, C2, C3, C4, C5, and C6 lines were selected as having high yields and stable based on parametric and non-parametric measurements. Meanwhile, the GGE biplot selects G13, G18, G22, G24, G25, and G26 lines. Lines selected by slices of the parametric, non-parametric, and GGE biplot measurements were G13, G22, G25, and G26. Thus, the four lines can be proposed as new superior lines for black soybeans, which have high yields and are stable in the three environments. Therefore, Sumedang is an ideal location for testing and developing black soybeans.

Acknowledgments

The authors thank the Rector of Universitas Padjadjaran for providing research facilities and thank the field assistants of Universitas Majalengka during the activity.

  1. Funding information: This multilocation yield trial was funded by Indonesian Soybean Consortia and Indonesian Legumes and Tuber Crops Research Institute, Malang, East Java, Indonesia, with the Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture. This research was also funded by Lembaga Pengelola Dana Pendidikan, Ministry of Finance of the Republic of Indonesia to A.A.W. with Registration No. 2020022130259.

  2. Author contributions: A.K., A.A.W., D.R., and S.A. – conceptualization, methodology, A.K. – resources; A.A.W. and H.M. – writing – original draft, field trials, data curation, formal analysis, software, visualization; G.W.A.S., D.S., S.A., D.R., and A.K. – writing – review and editing; S.A., D.R., and A.K. – supervision. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: The datasets generated and/or analyzed during this study are available from the corresponding author upon reasonable request.

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Received: 2022-05-19
Revised: 2022-08-14
Accepted: 2022-08-30
Published Online: 2022-09-28

© 2022 Acep Atma Wijaya et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2022-0137
Keywords for this article
GGE biplot; Glycine soja; stability analysis; environment
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Foliar application of boron positively affects the growth, yield, and oil content of sesame (Sesamum indicum L.)
  3. Impacts of adopting specialized agricultural programs relying on “good practice” – Empirical evidence from fruit growers in Vietnam
  4. Evaluation of 11 potential trap crops for root-knot nematode (RKN) control under glasshouse conditions
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  49. Assessment of the factors contributing to the lack of agricultural mechanization in Jiroft, Iran
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