Open Access

Genotype by environment effects on promiscuous nodulation in soybean (Glycine max L. Merrill)

  • Eric E. Agoyi1Email author,
  • Thomas L. Odong1,
  • John B. Tumuhairwe1,
  • Godfree Chigeza3,
  • Brian W. Diers2 and
  • Phinehas Tukamuhabwa1
Agriculture & Food Security20176:29

https://doi.org/10.1186/s40066-017-0107-7

Received: 13 January 2017

Accepted: 10 March 2017

Published: 17 March 2017

Abstract

Background

Understanding factors influencing the expression of a trait is key in designing a breeding program. Genotype by environment interaction has great influence on most quantitative traits. Promiscuous nodulation is a trait of importance for soybean production in Africa, because of the soil bacteria Bradyrhizobium japonicum not being indigenous in most African soils. Most soybean cultivars require B. japonicum for nodulation leading to the need for seed inoculation before sowing soybean in Africa. Few cultivars have capability to nodulate with Bradyrhizobia spp. that are different from B. japonicum and native in African soils. Such cultivars are termed “promiscuous cultivars.” Field experiments were conducted in six locations in Uganda for two seasons, to investigate the extent of environmental influences on the nodulation ability of promiscuous soybean genotypes.

Results

Additive main effect and multiplicative interaction effects showed highly significant environment and genotype by environment (G × E) interaction effects on all nodulation traits. G × E interaction contributed more to the total variation than genotypes. The genotypes Kabanyolo I and WonderSoya were the most stable for nodules’ dry weight (NDW), which is the nodulation trait the most correlated with grain yield. Genotype UG5 was the most stable for nodules’ number (NN), and Nam II for nodules’ effectiveness (NE). The genotype NamSoy 4M had the highest performance for NN, NFW, and NDW, but was less stable. WonderSoya had the highest NE. Genotype and genotype by environment analysis grouped environments into mega-environments (MEs), and four MEs were observed for NDW, with NamSoy 4M the winning genotype in the largest ME, and Kasese B the ideal environment for that nodulation trait.

Conclusion

This study provides information that can guide breeding strategies. The low genetic effect that led to high environmental and G × E interaction effects raised the need for multi-environments testing before cultivar selection and recommendation. The study revealed genotypes that are stable and others that are high performing for nodulation traits, and which can be used as parental lines in breeding programs.

Keywords

Bradyrhizobium sp. USDA 3456G × ENodulationPromiscuous Soybean

Background

Crop genotypes respond differentially to diverse environments for most quantitative traits [1]. This differential response is known as genotype by environment (G × E) interaction. Chandler et al. [2] reported that it is important to understand the causes of the G × E interaction in order to define breeding objectives. Gauch [3] and Yan and Hunt [4] argued that G × E interaction reduces selection progress in a breeding program and makes it difficult to select high-performing genotypes that are stable across locations. The impact of G × E on genotypes can be described by both their stability and adaptability. Stability is defined as the ability of a genotype to perform consistently in various environments. This applies to both high and low performance. Adaptability refers to the ability of a genotype to perform well in some environments and poorly in other environments [5]. Adaptability is handled by stratifying the production area and release of cultivars specifically adapted to each stratum [6]. Bernardo [7] found G × E interaction as both a problem and an opportunity. The author argued that a genotype with little G × E interaction is stable across environments; however, a genotype with high G × E interaction may outperform all other genotypes in specific environments, hence an opportunity to refine specific genotypes to specific environments. For a plant breeding program to be efficient, breeding strategies must integrate the environmental influence on the expression of genes involved in the traits under selection [8]. This is crucial at both cultivar selection and recommendation stages.

Promiscuous nodulation in soybean is a polygenic trait [9] of importance in Africa’s soybean production. Nodules have ability to fix atmospheric nitrogen [10] which provides the plant with the required nitrogen for normal plant growth and soil improvement. Unfortunately, Bradyrhizobium japonicum, the symbiotic soil bacteria required for normal nodulation of soybean, is not present in most soils in Africa [11]. In addition, most farmers do not have access to B. japonicum inoculant that can be used to inoculate seed [12]. Promiscuous soybean genotypes which have the capability of nodulating with indigenous and readily available Bradyrhizobia [13] proved the best alternative, as they can achieve high yield without prior seed inoculation and thus rule out the need to inoculate seeds before planting.

Breeding for promiscuous soybean cultivars has proven successful. Scientists at the International Institute of Tropical Agriculture (IITA) have released promiscuous soybean lines which were found to efficiently nodulate with Bradyrhizobium spp. that belong to the cowpea “cross-inoculation” group [14, 15]. Abaidoo et al. [16] detected Bradyrhizobium spp. populations in approximately 74% of the African soils; hence, promiscuous soybean cultivars would yield well without seed inoculation and ameliorate soils for subsequent crops in Africa. However, the genetics of promiscuous nodulation is not well documented and the little available literature is from [15]. They performed a backcross analysis of the dry weight of nodules and found that non-promiscuous phenotype was partially dominant and was controlled by four loci. The authors also based their investigation on leaf color score (LCS) and found that non-promiscuous phenotype was almost completely dominant in LCS. The same authors detected the RAPD marker OPB06 (5′-TGCTCTGCCC-3′) which they reported to be consistent with the soybean genotypes segregating for promiscuous nodulation [17].

Several statistical methods are available to assess G × E interaction. These include analysis of variance (ANOVA), site regression analysis (SREG), genotype and genotype by environment (GGE) model, joint regression analysis (JREG), factorial regression analysis (FREG), shifted multiplicative model (SHMM), and additive main effect and multiplicative interaction effects (AMMI). The choice of a model for G × E interaction analyses depends on the objectives of the investigator [18]. Literature search shows that AMMI and GGE are currently the two most frequently used methods for G × E analyses. Several arguments have been advanced for or against the use of AMMI and GGE models. Gauch [19] showed that the advantage of AMMI over GGE resides in the fact that AMMI can distinguish the effects of the genotype and the environment and then assess the G × E interaction in a reduced space with minimum error. Gauch and Zobel [20] judged that AMMI was able to increase two to five times the number of replications, hence suitable for experiments with few replicates. Kandus et al. [18] stated that GGE has been widely used because it allows the visualization of genotype performance in each environment. Shrestha et al. [21] judged GGE as the best approach because it clearly shows the “which-won-where” pattern of the genotypes. Naroui Rad et al. [22] demonstrated that both AMMI and GGE are suitable for the demarcation of mega-environments as these authors reported similar results for both models. Moreover, [23] found that the AMMI model, the GGE model and the SHMM were equal in gaining accuracy in research.

The objective of this study was to investigate the magnitude of the G × E effect on promiscuous nodulation in soybean.

Methods

Genetic material

The study involved 12 soybean genotypes that were shown to be responsive to Bradyrhizobium sp. strain USDA 3456 (cowpea-type inoculant) in an earlier study [24] (Table 1).
Table 1

Description of genotypes used in the study

Genotypes

Pedigree

Released

Current use status

Source

Nam2

TGM 79

1992

Parental line

NARO, Uganda

MakSoy 3N

Gc00138-29 × Duiker

2010

Commercial

Mak, Uganda

NamSoy 4M

Nam2 × Gc00138-29

2004

Commercial

NARO, Uganda

NamSoy 3

Kabanyolo I × Nam I

1995

Parental line

NARO, Uganda

MakSoy 2N

MakSoy 1N × Duiker

2008

Commercial

Mak, Uganda

MakSoy 5N

Nam2 × Gc00138-29

2013

Commercial

Mak, Uganda

Kabanyolo I

Mutant of Clark 63

Parental line

Mak, Uganda

WonderSoya

Parental line

IITA

Bulindi 48C

Mak, Uganda

Soprano

Zimbabwe

K-local

Uganda

UG5

Uganda

NARO National Agricultural Research Organization, MAK Makerere University, IITA International Institute for Tropical Agriculture

Experimental sites

The study was conducted in six sites representing the major agroecologies in Uganda (Table 2):
Table 2

Agro-climatic description of the six experimental sites used in the study

Sites

Latitude

Longitude

Elevation (masl)

Mean rainfall (mm)

Temperature(°C)

Soil type

Min

Max

(MUARIK) Kabanyolo

0.45

32.61

1300

1255

21.9

28.1

Ferralitic soils

Mubuku (Kasese)

0.18

30.0833

930

1200

18

31

Peaty, sands and clays

Kamwenge

0.18

30.45

1300

1300

20

25

Ferralsols, acrisols, nitosols

Nakabango (Jinja)

0.42

33.20

1178

1400

15

26

Crystalline basic

Iki Iki (Budaka)

1.09

34.00

1156

1200

15

28

Sandy

Ngetta (Lira)

53.69

22.93

1300

1483

19

29

Sandy loam

masl meters above sea level

  • The Ngetta Zonal Agricultural Research and Development Institute (NZARDI) in Lira District, Northern Uganda.

  • Nakabango Technology Verification Center in Jinja District, Eastern Uganda.

  • Iki Iki Technology Verification Center in Budaka District, Eastern Uganda.

  • Mubuku Irrigation and Resettlement Center in Kasese District, Western Uganda.

  • Makerere University Agricultural Research Institute of Kabanyolo (MUARIK), Central Uganda.

  • On-farm trial in Kamwenge District, Western Uganda.

Preparation of inoculum, planting, and data collection

At each site, plots of land not previously artificially inoculated with Bradyrhizobia were selected and prepared for sowing following common land preparation methods [25]. Seeds of the 12 genotypes were inoculated with Bradyrhizobium sp. strain USDA 3456 (cowpea-type inoculant). Bradyrhizobium spp. are reported to effectively nodulate promiscuous soybean genotypes [14, 15]. Inoculant was obtained from Biofix (Kenya), purified, and incubated in the Soil Science Biological Nitrogen Fixation (BNF) Laboratory at Makerere University. The most probable number (MPN) through serial dilution technique described in [26] was used to grow Bradyrhizobium to 7.91 × 109 cells g−1 and then formulated into inoculum using steam-sterilized peat soil as a carrier. Ten grams of sugar was dissolved into 300 ml of clean lukewarm water to produce a sticking agent. The inoculant was mixed with the sticking agent and directly applied on seeds to enhance association between plant and rhizobium. To be sure that inoculants were viable, fresh culture was made at each planting season, and after mixing with peat soil, packets were refrigerated at 4 °C until planting date.

The field experiments were arranged in a randomized complete block design (RCBD) with three replicates at each site. Each plot was three rows wide and 5 m in length. The rows were 0.6 m apart with a spacing of 0.1 m between plants within rows, giving an average of 153 plants per plot (170,000 plants/ha). Experiments were conducted for two consecutive seasons: first rainy season (2015A) and second rainy season (2015B) of 2015 at each site, resulting in 12 testing environments (see Table 3). Fields were weeded three times in a season.
Table 3

Planting and sampling dates, pH, and nutrient contents of soil sampled from experiment sites during seasons 2015A and 2015B

Sites

Seasons

Planting date (dd/mm)

Sampling date (dd/mm)

pH

o.m. (% age)

N (mg/kg)

Av. P (C mol/kg)

K

Na

Ca

Mg

Kabanyolo

2015A

01/04

23/05

4.92

2.64

0.26

4.48

0.38

0.20

2.80

0.84

2015B

18/09

12/11

4.86

2.66

0.24

4.51

0.37

0.21

2.70

0.81

Ngetta

2015A

12/04

05/06

4.78

1.93

0.19

4.20

0.21

0.17

2.50

0.75

2015B

20/09

14/11

6.11

1.91

0.14

12.31

0.76

0.60

4.56

1.50

Kamwenge

2015A

03/04

26/05

4.95

2.29

0.23

4.48

0.18

0.20

3.13

0.94

2015B

14/10

10/12

5.45

2.50

0.18

7.45

0.32

0.05

3.12

1.03

Kasese

2015A

02/04

25/05

5.07

3.34

0.33

17.82

0.32

0.09

5.32

1.60

2015B

13/10

09/12

5.08

3.29

0.31

17.77

0.33

0.11

5.29

1.58

Iki Iki

2015A

09/04

02/06

5.39

1.76

0.18

4.41

0.25

0.24

2.19

0.66

2015B

26/09

30/11

5.41

1.77

0.19

4.45

0.27

0.23

2.21

0.70

Nakabango

2015A

08/04

01/06

5.12

3.52

0.35

4.55

0.42

0.20

5.00

1.50

2015B

25/09

29/11

5.17

3.55

0.34

4.55

0.43

0.22

4.87

1.46

Av. P available phosphorus, o.m. organic matter, dd/mm day/month

At each of the six sites, 6–7 weeks after emergence (see planting and sampling dates in Table 3), ten plants per plot were randomly dug up, the root system from each plant was carefully washed, and all nodules were harvested and counted to determine the number of nodules (NN), and later weighed to determine the fresh weight of nodules per plant (NFW). Thereafter, all the nodules were split and opened to assess their effectiveness. The percentage of effective nodules (NE) per plant was calculated based on the presence of brownish or pinkish pigmentation inside nodules. Nodules were then oven-dried at 65 °C for 4 days [15] and weighed to determine the total nodule dry weight (NDW) per plant.

Data analysis

ANOVA was performed for each site separately followed by a combined analysis across sites in GenStat 14th edition (VSN International Ltd., Hemel Hempstead, UK) [27]. No data transformation was needed.

The models for analyses were:
$$ \begin{aligned} Y_{ijk} & = \, \mu + G_{i} + S_{j} + GS_{ij} + S/r_{jk} + \varepsilon_{ijk} \quad ({\text{for}}\,{\text{single}}\,{\text{site}}\,{\text{analysis}}) \\ Y_{ijhk} & = \mu + G_{i} + L_{h} + S_{j} + GL_{ih} + GS_{ij} \\ & \quad + LS_{hj} + GLS_{ihj} + E/r_{jhk} + \varepsilon_{ijhk} \quad ({\text{for}}\,{\text{across}}\,{\text{environments}}\,{\text{analysis}}) \\ \end{aligned} $$
where Y ijk is the observed value from each experimental unit, µ population mean, L h effect of the hth site, S j effect of the jth season, S/r jk effect of the kth replicate nested to the jth season, E/r jhk effect of the kth replicate nested to the jhth environment (environment = location by season), G i effect of ith genotype, GS ij interaction effect of ith genotype and the jth season, LS jh effect of the jth season nested to the hth location, GLS ijh interaction effect of ith genotype and the jhth environment (site per season) and ε ijkh experimental error.

AMMI analysis was performed on each nodulation trait to determine the contribution of the genotypes, environments, as well as their interaction to the total variation.

GGE analysis was also performed to determine the mega-environments and visualize the “which-won-where” pattern. These were done using breeding view graphical user interface with a statistical analysis package [28] embedded in Breeding Management System (BMS) Version 3.0.9.

Results

Analysis of variance

The single site ANOVA (Table 4) showed significant differences (p < 0.05) among genotypes for all nodulation traits measured at most of the sites, except at Iki Iki where genotype effects were nonsignificant for all nodulation traits. Percentage of effective nodules was not significant (p > 0.05) at Kabanyolo and Kamwenge.
Table 4

Summary of ANOVA results of single site analysis for all the nodulation traits measured during seasons 2015A and 2015B

Sites

Sources

df

m.s.

NN

NE

NFW

NDW

Iki Iki

Seasons

1

2485.12**

425.0ns

3.692**

0.061*

Genotypes

11

38.74ns

434.8ns

0.057ns

0.002ns

Genotypes × seasons

11

57.16ns

479.9ns

0.069ns

0.004*

Error

44

36.73

299.6

0.047

0.002

Mean

 

10

57.6

0.43

0.074

CV (%)

 

60

30

50

55

SEM

 

2.02

5.77

0.072

0.0014

Kabanyolo

Seasons

1

3.65ns

244.96ns

1.5851**

0.1910***

Genotypes

11

566.53***

150.76ns

0.1001***

0.0061**

Genotypes × seasons

11

119.63ns

115.38ns

0.0221ns

0.0020ns

Error

44

98.26

87.59

0.0286

0.0018

Mean

 

31

68.3

0.404

0.101

CV(%)

 

32

13.7

41.86

42

SEM

 

3.3

3.12

0.056

0.014

Kamwenge

Seasons

1

7.35ns

7171*

0.0007ns

0.0005ns

Genotypes

11

1.31*

2690ns

0.0187***

0.0002***

Genotypes × seasons

11

1.03ns

381ns

0.0082**

0.0001ns

Error

44

0.57

1242

0.0033

0.0001

Mean

 

1

66.8

0.039

0.0049

CV (%)

 

75

52.75

147.29

204.08

SEM

 

0.25

11.74

0.019

0.0033

Kasese

Seasons

1

3389.39ns

1241.49**

2.3852*

0.3966**

Genotypes

11

523.13***

175.95*

0.7319***

0.0288**

Genotypes × seasons

11

471.16***

78.58ns

0.3612**

0.0074ns

Error

44

96.54

85.91

0.1460

0.0115

Mean

 

30

82.1

1.214

0.254

CV (%)

 

32.75

11.28

31.47

42.21

SEM

 

3.27

3.08

0.12

0.035

Ngetta

Seasons

1

95.22*

155.5ns

0.1982**

0.0076***

Genotypes

11

33.44***

1960.0**

0.0703***

0.0023***

Genotypes × seasons

11

23.40***

3869.6***

0.0360***

0.0012***

Error

44

4.01

548.8

0.0067

0.0002

Mean

 

2

62.4

0.1223

0.0187

CV (%)

 

100

37.54

66.92

75.62

SEM

 

0.66

7.80

0.11

0.045

Nakabango

Seasons

1

14,308.7**

452.2*

11.4899**

0.6253**

Genotypes

11

638.6*

587.7**

0.3470**

0.0173**

Genotypes × seasons

11

206.8ns

276.2ns

0.1241ns

0.0056ns

Error

44

279.8

179.3

0.1022

0.0059

Mean

 

39

64.8

1.017

0.2346

CV (%)

 

42.89

20.66

31.43

32.74

SEM

 

5.57

4.46

0.106

0.0256

df degree of freedom, m.s. mean square, NN number of nodules, NE percentage of effective nodules, NFW fresh weight of nodules, NDW dry weight of nodules

* Significant at 0.05

** Significant at 0.01

*** Significant at 0.001

Season effects were significant (p < 0.05) for all nodulation traits at Nakabango. There were no significant (p > 0.05) season effects for the percentage of NE at Iki Iki, Kabanyolo, and Ngetta, while season effects for NN were nonsignificant at Kabanyolo, Kamwenge, and Kasese. Season had significant effects on dry weight of nodules at all sites except at Kamwenge.

There was significant genotype by season effects for NN at Kasese and Ngetta. For NE, significant genotype by season effects was only obtained at Ngetta, while NFW had significant genotype by season effects at Kamwenge, Kasese, and Ngetta. Significant genotype by season effects for NDW was also obtained at Iki Iki and Ngetta. Ngetta exhibited significant genotype by season effects on all nodulation traits. The interaction between genotypes and seasons was not significant for any nodulation trait at Kabanyolo and Nakabango.

In the analysis across all 12 environments, genotype by environment effects was highly significant for NN, NE, NFW (p < 0.001), and NDW (p < 0.05) (Table 5). Highly significant (p < 0.001) genotypic effects were also obtained for all nodulation traits except NE, where there was no significant difference between genotypes across environments. Highly significant environmental effects were observed for all nodulation traits (p < 0.001). The interaction of genotypes × sites × seasons was highly significant (p < 0.01) for all nodulation traits except for NDW. Sites showed significant differences only for NN and NE, while seasons showed significant difference only for NE. The interaction of sites × seasons was highly significant (p < 0.001) for all nodulation traits, except for NE. There was significant interaction between genotypes and sites for NFW and NDW, while no significant interaction was observed between genotypes and seasons for any of the nodulation traits measured.
Table 5

Summary of ANOVA results across environments for all the nodulation traits measured during seasons 2015A and 2015B

Source

df

m.s.

NN

NE

NFW

NDW

Environment

11

10,730.32***

2333.2***

9.20***

0.49***

 Locations

5

19,548.83*

4416.34**

16.36ns

0.82ns

 Seasons

1

6.85ns

2696.6*

0.54ns

0.05ns

 Locations × seasons

5

4056.51***

177.38ns

3.76***

0.24***

Reps (environment)

24

308.53***

461.66*

0.11**

0.01**

Genotypes

11

837.6***

749.91ns

0.53***

0.02***

Genotypes × environment

121

167.57***

603.66***

0.13***

0.005*

 Genotypes × locations

55

192.82ns

600.80ns

0.16*

0.006**

 Genotypes × seasons

11

186.51ns

335.92ns

0.14ns

0.003ns

Genotypes × locations × seasons

55

138.54**

672.61***

0.09**

0.003ns

Error

264

85.98

277.57

0.05

0.003

df degree of freedom, m.s. mean square, NN number of nodules, NE percentage of effective nodules, NFW fresh weight of nodules, NDW dry weight of nodules

* Significant at 0.05

** Significant at 0.01

*** Significant at 0.001

Additive main effect and multiplicative interaction (AMMI) analysis

The AMMI ANOVA (Table 6) showed significant effects of genotypes and environments as well as their interactions for all nodulation traits (p < 0.05–p < 0.001), except for the percentage of effective nodules, where genotypic effects were not significant. The first two interaction principal component axes (IPCA) were significant at (p < 0.01) for all nodulation traits. For all nodulation traits, the first IPCA explained more than 50% of the total variation. IPCA1 explained 56.8% of the total variation in NN, 69.86% for NE, 61.13% for NFW and 72.11% for NDW, while IPCA2 explained 43.2% for NN, 30.14% for NE, 38.87% for NFW and 27.89% for NDW.
Table 6

Additive main effect and multiplicative interaction (AMMI) analysis of variance for all the nodulation traits measured during seasons 2015A and 2015B

Source

df

NN

NE

NFW

NDW

s.s.

m.s.

Explained (%)

s.s.

m.s.

Explained (%)

s.s.

m.s.

Explained (%)

s.s.

m.s.

Explained (%)

Total

224

50,829

  

71,425

  

42.571

  

2.1372

  

Genotypes

11

3071

279.2***

6.04

2787

253.3ns

3.90

1.945

0.1768***

4.57

0.0888

0.008***

4.15

Environments

11

39,346

3576.9***

77.41

9272

842.9*

12.98

33.721

3.0656***

79.21

1.7907

0.163***

83.79

Interactions

121

6320

52.2***

12.43

45,745

378.1***

64.05

5.189

0.0429***

12.19

0.1925

0.002***

9.01

 IPCA 1

21

2402

114.4***

56.80

22,441

1068.6***

69.86

2.123

0.1011***

61.13

0.0918

0.004***

72.11

 IPCA 2

19

1827

96.2***

43.20

9683

509.6***

30.14

1.35

0.071***

38.87

0.0355

0.002**

27.89

 Residuals

81

2092

25.8

 

13,621

168.2

 

1.716

0.0212

 

0.0652

0.0008

 

df degree of freedom, s.s. sum of squares, m.s. mean square, NN number of nodules, NE percentage of effective nodules, NFW fresh weight of nodules, NDW dry weight of nodules

* Significant at 0.05

** Significant at 0.01

*** Significant at 0.001

G × E interaction had a substantial contribution to the total variation for all nodulation traits, varying from 9 to 64% (Table 6). The highest G × E effect was observed for NE, while the lowest G × E effect was observed for NDW. Environments had the highest contribution (13–84%) to the total sum of squares for all nodulation traits except for NE, where the highest contribution (64%) was from the G × E interaction. Genotypes had the lowest contribution (3.9–6.0%) to the total variation, suggesting low genetic effect and high environmental effect in the expression of the nodulation traits measured.

The AMMI biplots (Fig. 1) showed the genotype NamSoy 4M with the highest NN across environments (29 nodules per plant), while K-local had the lowest performance (11 nodules per plant). These two genotypes were both unstable as they are far from the origin with regard to the y axis, while the genotype UG5 proved the most stable.
Fig. 1

AMMI1 Biplots for nodulation traits of soybean genotypes showing genotypes’ means (in green color) and environments (in blue color) plotted against their IPCA1 scores. Mak 3N MakSoy 3N, Nam 4M NamSoy 4M, Nam 3 NamSoy3, Mak 2N MakSoy 2N, Mak 5N MakSoy 5N, Kab I Kabanyolo I, Wonder WonderSoya, Bul Bulindi 48C, Sop Soprano, K-Loc K-local, IkiA Iki Iki season A, IkiB Iki Iki season B, KabA Kabanyolo season A, KabB Kabanyolo season B, Kam A Kamwenge season A, KamB Kamwenge season B, KasA Kasese season A, KasB Kasese season B, NgtA Ngetta season A, NgtB Ngetta season B, NkgA Nakabango season A, NkgB Nakabango season B

As for the percentage of NE, the most stable genotypes were Nam II, Kabanyolo I, and NamSoy 3. MakSoy 2N was the most unstable, far apart from the origin, and had a relatively high performance across environments (72% of effective nodules). The genotype WonderSoya had the highest performance across environments (74% of effective nodules), coupled with a relatively high stability, while the genotype Soprano had the lowest performance (56% of effective nodules) and was relatively unstable.

With respect to NFW, NamSoy 3 and WonderSoya proved the most stable (closest to the origin), the most unstable genotype was K-local, as it had the longest vector to the origin. The genotype NamSoy 4M had the best performance (744 mg) and was relatively stable, while the genotype Soprano had the lowest performance (347 mg) and was unstable.

For NDW, the genotypes WonderSoya and Kabanyolo I were closest to the origin and thus were the most stable, while genotypes K-local and Bulindi 48C had the longest vector to the origin and thus were the least stable. Genotype NamSoy 4M had the highest performance (151 mg), while K-local had the lowest performance (72 mg).

Focusing on the environments, Nakabango B was the environment with the highest nodule number, while Kamwenge A and B, as well as Ngetta B, had the lowest NN, followed by Ngetta A and Iki iki B, respectively. With respect to the NE, Kasese A had the greatest percentage of effective nodules, while Iki iki B had the lowest percentage. The highest fresh weight of nodules was found in Nakabango B and Kasese A, while Kamwenge A and B had the lowest performance. The highest fresh weight of nodules was observed in Kasese A and Nakabango B, while Kamwenge A and B had the lowest performance.

Genotype and genotype by environment (GGE) biplot analysis

The mean performance of genotypes for each nodulation trait across environments was subjected to the GGE analysis. The GGE biplots (Fig. 2) plotted the scores of genotypes and environments on the first (PC1) against the second (PC2).
Fig. 2

GGE biplots for nodulation traits of soybean genotypes showing the “which-won-where” pattern and mega-environments. Mak 3N MakSoy 3N, Nam 4M NamSoy 4M, Nam 3 NamSoy3, Mak 2N MakSoy 2N, Mak 5N MakSoy 5N, Kab I Kabanyolo I, Wonder WonderSoya, Bul Bulindi 48C, Sop Soprano, K-Loc K-local, IkiA Iki Iki season A, IkiB Iki Iki season B, KabA Kabanyolo season A, KabB Kabanyolo season B, Kam A Kamwenge season A, KamB Kamwenge season B, KasA Kasese season A, KasB Kasese season B, NgtA Ngetta season A, NgtB Ngetta season B, NkgA Nakabango season A, NkgB Nakabango season B

The first axis (PC1) explained 56% of the variation in NN, while the second axis (PC2) explained 20%. In total, the first two axes explained 76% of the total variation. The sector convex hull showed three sectors indicating three mega-environments: Kasese A, Nakabango A and B, Kabanyolo A and B, Iki Iki A, and Ngetta A made one mega-environment with NamSoy 4M the winning genotype. Kasese B represents the second mega-environment with MakSoy 3N the highest performing genotype. The third mega-environment included Ngetta B, Iki Iki B, and Kamwenge A and B with K-local the best genotype in that mega-environment.

With respect to the percentage of effective nodules, the first two PCs explained 98.5% of the total variation of which PC1 had 97% and the PC2 1.5%. Three mega-environments were demarcated: Ngetta B represented one mega-environment with MakSoy 2N as winning genotype. Ngetta A and Kamwenge A and B formed the second mega-environment with Bulindi 48C and MakSoy 3N as winning genotypes. The rest of the environments formed the third mega-environment with Soprano, NamSoy3 and UG5 as best genotypes.

For NFW, the first two PCs explained 75% of the total variation of which the PC1 carried 53%. Two mega-environments were demarcated with Nakabango A and B, and Kasese B forming the first mega-environment with MakSoy 3N as best genotype, and the rest of the environments constitute the second mega-environment in which NamSoy 4M was the best genotype.

The two first PCs explained 75% of the total variation in NDW. Four mega-environments were demarcated with interference between them. The largest mega-environment included Kasese A, Kabanyolo A and B, Ngetta A and B, Kamwenge A and B, and Iki Iki A and B. NamSoy 4M was the winning genotype in that mega-environment. However, Iki Iki A and Kabanyolo A also belong to the second mega-environment which they formed together with Kasese B and Nakabango A and B; Bulindi 48C and MakSoy 3N were the winning genotype in this mega-environment. The last two mega-environments were contiguous. They were formed with Kamwenge A and Iki Iki B for one and Ngetta B and Kamwenge B for the other.

Overall Nakabango B proved the ideal environment for NN (Fig. 3a), Ngetta B for NE (Fig. 3b), while Kasese A proved the ideal environment for NFW (Fig. 3c) and Kasese B for NDW (Fig. 3d). These environments had the longest vector, so were the most discriminating; besides, they were the closest to the horizontal axis, so were representative of the other environments.
Fig. 3

Discriminating biplots for nodulation traits of soybean genotypes showing the “ideal” environments. a Nodules number (NN), b nodules’ effectiveness (NE), c nodules fresh weight (NFW), d nodules fresh weight (NDW). Mak 3N MakSoy 3N, Nam 4M NamSoy 4M, Nam 3 NamSoy 3, Mak 2N MakSoy 2N, Mak 5N MakSoy 5N, Kab I Kabanyolo I, Wonder WonderSoya, Bul Bulindi 48C, Sop Soprano, K-Loc K-local, IkiA Iki Iki season A, IkiB Iki Iki season B, KabA Kabanyolo season A, KabB Kabanyolo season B, Kam A Kamwenge season A, KamB Kamwenge season B, KasA Kasese season A, KasB Kasese season B, NgtA Ngetta season A, NgtB Ngetta season B, NkgA Nakabango season A, NkgB Nakabango season B

Discussion

The study showed highly significant interactions between genotypes and environments for all the nodulation traits measured. This is an indication that the response of promiscuous soybean genotypes to Bradyrhizobium sp. USDA 3456 is highly dependent on the site where the genotypes are grown and seasonal variation of temperature and rainfall. For most of the measured nodulation traits, the contribution of the environment to the total variation was higher than the effect of the genotypes and genotype by environment (G × E) interaction, except for percent of effective nodules where the G × E interaction effects contributed more to the total variation than the genotypes and environments effects. A similar trend was reported earlier on 31 Argentinean soybean genotypes by Salvucci et al. [9], who observed 41.9% contribution from environments against 28% from genotypes and 10.7% from interactions to the total variations on NN. However, the same authors reported more contribution from genotypes (30.5%) than environments and G × E interaction, 14.6 and 13.5%, respectively, in the total variation on NDW. The low contribution of the genotypes to the total variation observed in this study suggests that selection in a single environment may fail to achieve sufficient gain. Multi-environment selection is the best strategy suggested by quantitative geneticists to achieve selection gain with traits that exhibit low genetic effect [7, 8]. One possible reason for genotypes accounting for relatively lower percentage of variability is the few genotypes (12) tested in this study; low genotype number leading to underestimation of genetic contribution has been pointed out by quantitative geneticists such as [7, 8]. Increasing the number of genotypes might result in a higher genetic contribution to the total observed variability.

Several authors have reported the importance of the environmental effect on nodulation in diverse legume plants [2931]. Van et al. [32] advocate that the interaction between soybean and Bradyrhizobia can be represented by the disease triangle, whereby a functioning equilibrium between the host plant, microorganism and the environment is important to achieve effective nodulation. Yusuf et al. [33] emphasized that nodulation in soybean is a result of the effect of these three components (genotypes, environment, and Bradyrhizobia) plus their interaction.

The GGE analysis showed that different seasons at the same site differ such that a site may belong to different mega-environments, depending on the season. This emphasizes the extent to which the environmental influences are important in the expression of these nodulation traits. Yan and Rajcan [34] defined an ideal environment as having high discriminating power (large PC1 scores) and being most representative of other environments (small PC2 scores). For the NN, Nakabango B was the most discriminating and representative environment, while Kasese B was ideal for NDW. A similar result was earlier reported on soybean yield in Uganda by Tukamuhabwa et al. [35]. These ideal environments are known to resolve accurate genotypic differences, thereby providing breeders with necessary information for selection [35].

The average performances of environments showed Kamwenge A, B, Ngetta A, B and Iki Iki B as the poorest performing. The consistently poor performance at Kamwenge and Ngetta needs further investigations. We speculated that the poor performance of these two sites (Kamwenge and Ngetta) could be attributed to soil characteristics and environmental factors. Factors such as soil acidity (pH lower than 5.6), waterlogging, high temperatures, soil salinity, low available phosphorus and calcium, too much nitrogen, anoxia, etc. have been reported to affect nodulation in soybean [36, 37]. However, no obvious differences in nutrient content and average rainfall between these two (Kamwenge and Ngetta) and other sites were observed. There is therefore no evidence linking weather or soil conditions to the poor performance of Kamwenge and Ngetta. Besides, authors reported that competition between indigenous and introduced strains can lead to failure in maximizing nodulation potential of soybeans [38]. Hence, we recommend that further research consider the characterization of soil Bradyrhizobia, especially at these two sites to explain the low performance of all the genotypes and suggest solutions to solve that. In Ngetta, galls due to infection by root knot nematodes (Meloidogyne incognita) were frequently observed on plants’ root system. This could explain the relatively low nodulation recorded in that site, as competition for space and nutrients might have occurred. We also recommend that investigations be made on the interaction between Bradyrhizobia and root knot nematode.

Conclusion

This study revealed information that can guide breeding strategies. The nodulation scores measured were number of nodules (NN), percent of effective nodules (NE), fresh weight of nodules (NFW), and dry weight of nodules (NDW). The effect of the environment was high on nodulation traits measured, and the same applies to the G × E interaction. The genetic effect tends to be low compared to the environment effect and the G × E interaction. The genotypes UG5, Kabanyolo I, and WonderSoya, which each showed stability for some of the nodulation traits measured, can be recommended as parental lines to initiate a breeding program focusing on promiscuous nodulation in soybean. The low genetic contribution, high environmental effect, and G × E interaction observed in this study provide very important knowledge that will be insightful for further soybean breeding programs, as multi-environment testing of the genotypes for both cultivar selection and cultivar recommendation is necessary in such situations. In other hand, the study revealed that as far as promiscuous nodulation in soybean is concerned, the sites where this study was conducted should not stand alone as testing environments as it was shown that the same site can behave as different environments depending on the season.

Abbreviations

AMMI: 

additive main multiplicative interaction

GGE: 

genotype and genotype by environment

ME: 

mega-environment

NN: 

nodules’ number

NE: 

nodules’ effectiveness

NFW: 

nodules’ fresh weight

NDW: 

nodules’ dry weight

G × E: 

genotypes by environment

USDA: 

United States Department of Agriculture

IPCA: 

interaction principal component axes

PC: 

principal component

BMS: 

Breeding Management System

RCBD: 

randomized complete block design

BNF: 

biological nitrogen fixation

MPN: 

most probable number (MPN)

MUARIK: 

Makerere University Agricultural Research Institute of Kabanyolo

NZARDI: 

Ngetta Zonal Agricultural Research and Development Institute

NARO: 

National Agricultural Research Organization

MAK: 

Makerere University

IITA: 

International Institute for Tropical Agriculture

SHMM: 

shifted multiplicative model

JREG: 

joint regression

FREG: 

factorial regression

SREG: 

site regression

RAPD: 

randomly amplified polymorphic DNA

Declarations

Authors’ contributions

EEA, TLO, JBT, GC, BWD, and PT conceived the study. EEA, JBT, and PT collected data. EEA and TLO analyzed data. All authors read and approved the final manuscript.

Acknowledgements

Our acknowledgment goes to Intra-ACP SHARE for its supports, to the BNF laboratory at Makerere University for the technical assistance, to Boris Alladassi, Fréjus Sodedji, and Symphorien Agbahoungba for their proofreading and fruitful discussions.

The authors do hereby declare that with the exception of references to past and current studies consulted which have been duly acknowledged, the work presented was carried out from April 2015 to January 2016 in Uganda.

Competing interests

The authors declare that they have no competing interests.

Availability of supporting data

The data generated and analyzed in this study are available to readers as in the manuscript.

Funding

EEA is a fellow of the Norman E. Borlaug Leadership Enhancement in Agriculture Program funded by USAID. Support for this research was provided in part by the Borlaug Leadership Enhancement in Agriculture Program (Borlaug LEAP) through a grant to the University of California-Davis by the United States Agency for International Development. The opinions expressed herein are those of the authors and do not necessarily reflect the views of USAID. Support for this research was made possible through a capacity building competitive grant Training the next generation of scientists provided by Carnegie Cooperation of New York through the Regional Universities Forum for Capacity Building in Agriculture (RUFORUM).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Agricultural Production, Makerere University
(2)
Department of Crop Science, University of Illinois
(3)
International Institute of Tropical Agriculture IITA-Zambia

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