Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.391

  • Impact Factor 0.8 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Legume Research, volume 44 issue 8 (august 2021) : 894-899

Additive Main Effects and Multiplicative Interactions in Field Pea (Pisum sativum L.) Genotypes Across the Major Agro-climatic Zones in India

Tufleuddin Biswas1,*, Debasis Mazumdar1, Arpita Das2, P. Dinesh Kumar1, Anirban Maji2, A.K. Parihar3, Sanjeev Gupta3
1Department of Agriculture Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur-741 252, Nadia, West Bengal, India.
2Department of Genetics and Plant Breeding, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur-741 252, West Bengal, India.
3AICRP on MULLaRP, Indian Institute of Pulses Research, Kanpur- 208 024, Uttar Pradesh, India.
  • Submitted15-05-2019|

  • Accepted31-08-2019|

  • First Online 09-11-2019|

  • doi 10.18805/LR-4166

Cite article:- Biswas Tufleuddin, Mazumdar Debasis, Das Arpita, Kumar Dinesh P., Maji Anirban, Parihar A.K., Gupta Sanjeev (2021). Additive Main Effects and Multiplicative Interactions in Field Pea (Pisum sativum L.) Genotypes Across the Major Agro-climatic Zones in India . Legume Research. 44(8): 894-899. doi: 10.18805/LR-4166.
In agricultural experimentation, a large number of genotypes are normally evaluated over a wide range of environments for delineating stable genotypes. In this study, fifteen dwarf field pea genotypes were evaluated at six diverse locations under three Agro-climatic zones viz., Central zone, North West peninsular zone and North east peninsular zone for the purpose of identifying stable genotypes through deploying the additive main effects and multiplicative interaction (AMMI) model. The uniqueness of AMMI biplot is to provide comprehensive solution regarding multi-environment evaluation of genotype.  In addition to identification of stable genotypes, this approach facilitates  effective selection of test environment and allows optimum resource allocation in future testing programme. In the present study from the AMMI biplot and the ASV AMMI stability value (ASV), it was detected that genotypes 6 (Pant-P-345), 12 (KPF-14-50) and 4 (KPMR-940) were the stable genotypes amid the tested genotypes. These identified genotypes with wide adaptation would be valuable treasure troves for the breeder for utilizing as a parent in field pea breeding programme of India.
Field pea or dry pea (Pisum sativum L.) is one of the most important cool season food legumes in India with 1.06 million hectares area and 1.01 million tons production (Anonymous, 2017). Grains of field pea are rich source of proteins (21.2-32.9%) with absence of major anti-nutritional factors, thus making it as most preferred sources of digestible protein for both human and livestock use. The quality of both starch and dietary fiber make peas a low-glycaemic index (GI) food and hence it is beneficial for diabetic people also. It is an excellent candidate crop in rice fallow areas and with its nitrogen fixing ability soil health status is also improved. Despite of immense potential, breeding work in field pea is generally concentrated on development of high yielding varieties but information regarding the performance of these high yielding varieties in different agro-climatic zones was meager. 
In agricultural experimentation, large numbers of genotypes are normally evaluated over a wide range of environments for validation of performance regarding quantitative characters. According to  Yan et al., (2007) during this course of genotypic evaluation three factors are important for determining the performance of a genotype namely, genotype main effects (G), environmental main effect (E) and their interaction (GEI). Presence of GEI changes the performance of the genotypes in different environment due to presence of cross-over interaction which further complicates the selection of superior genotypes for a certain target environment. Differential adaptability and buffering capacities of the genotypes are the main cause of this GEI thus resulting inconsistent performance of the genotypes. In agricultural experiment, low level of interaction with uncontrolled variables (weather) and high level of interaction with controlled variables (soil fertility etc.) always give stable genotypic response (Mohebodini et al., 2006). 
The main problem with univariate and nonparametric stability statistics is that they do not have an accurate picture of complete response pattern due to the multivariate nature of the genotype to varying environments (Lin et al., 1986). Therefore, in this situation the AMMI model provides the best solution for explaining GEI. Previous studies confirmed the importance of AMMI model as it can clarify the sum of several multiplicative terms rather than only one multiplicative term in appraising the performance of genotypes in diverse environments (Crossa et al., 1990; Ebdon and Gauch, 2002). AMMI model integrates both ANOVA and principal Component analysis and enabling complete removal of the residual variation from the GEI interaction (Crossa et al., 1990). AMMI model is a simple graphical approach for easy illustration of interpretation and other than two-way data structure it has no specific experimental design requirements (Zobel et al., 1988). Effectiveness of the AMMI methods has been well documented by previous authors (Yan and Rajcan, 2002; Yan et al., 2001; Kaya et al., 2002; Muhe and Assefa, 2010Mahalingam et al., 2006; Banik et al., 2010; Bantayehu, 2009; Rodriguez et al., 2007). Using the same field-pea genotypes a few parametric non-parametric stability methods were used (Biswas et al., 2018).

Keeping these in the backdrop, the objectives of this study was to explain GEI obtained through AMMI analysis regarding yield performances of fifteen field pea genotypes across  six testing environments, visualizing the variation of yield performances across the tested environments based on the biplot and identifying promising stable field pea genotypes for future breeding programme.
Fifteen dwarf field pea genotypes (Table 1) have been considered as experimental material for this study which was maintained under the aegis of All India Coordinated Research Project (AICRP) on MULLARP (Mung bean, Urd bean, Lentil, Lathyrus, Rajmash and Pea). These genotypes were evaluated during the rabi season of 2016-17 across six testing locations in three different Agro-climatic zones (Table 2). From each agroclimatic Zone two locations were selected for evaluation of genotypes. Experimental design deployed was simple randomized complete block design (RCBD) with 3 replications in each location with a gross plot size of 8.0 meters squares. Genotypes were planted maintaining proper plant geometry with 45 cm row to row distance. Standard package of practices were followed to raise the crops. Genotypes across the locations were sown during 1st week of November and harvested from  each plot separately. For recording yield data, five randomly selected plants from the inner rows of each genotype were considered. The AMMI biplot analysis was performed using GEA-R (Genotype × Environment Analysis with R for Windows) version 4.0 (2016-11-30).

Table 1: Climatic variables regarding the locations and corresponding agro climatic zone.


Table 2: List of genotypes.

Construction of AMMI Model
AMMI model is a biplot model which visually illustrates the numerical results and explains the GEI during genotypic appraisal (Gollob, 1968; Zobel et al., 1988,  Gauch, 1993). In AMMI biplot model  the standard ANOVA procedure is utilized to separate the additive variance from the multiplicative procedure. Principal component analysis (PCA) is also performed to extract the GE portion from ANOVA. The AMMI analysis is actually the two-factor ANOVA, where the variance arises due to factor mean deviation from the grand mean is removed and the resulting matrix is further subjected for enumerating Singular value decomposition (SVD).
The mathematical model for AMMI is as follows:    
Yijr    = Yield of ith genotype in jth environment for replicate r,
µ      = Overall mean,
Gi     = Genotypic (ith) main effect,
Ej     = Environmental (jth) main effect,
λn     = Singular value of nth IPCA axis,                         
γin   = Genotypic eigen vector values for nth IPCA axis,
δjn    = Environmental eigen vector values for nth IPCA axis,
ρij     = residual containing all multiplicative terms not included in the model.
eijr = Residual; eijr ~ N (0, σ2)
N = no. of principal component (s) retained in the model.
AMMI stability value (ASV) was calculated for each genotype considering the relative contributions of the principal component axis scores (IPCA1 and IPCA2) to the interaction sum of squares. The AMMI stability value (ASV) proposed by Purchase et al., (1997) was enumerated as follows:

SSIPCA1/ SSIPCA2 is nothing but the weight given to the IPCA1value by dividing the IPCA1SS by the IPCA2SS and the IPCA1 and IPCA2 scores are the genotypic scores in the AMMI model.
AMMI analysis of variance for the stability of yield in field pea
In the present study, the AMMI ANOVA  regarding the yield performance of 15 field pea genotypes tested in 6 environments during the period 2016-17 were presented in Table 3. The results revealed that the main effects of genotype (G), environment (E) and G × E interaction were found to be statistically significant (p < 0.01). Further, the division of G × E interaction into 5 PCAs (PCA I to PCA V) accounted for 47.75, 22.06, 13.99, 9.44 and 6.75 per cent of variation respectively. Thus, in the present study, the 5 principle components obtained through SVD of environments explained 100 per cent of the total G × E variation regarding the performance of field pea genotypes in terms of their yield potential. Thus, the GEI of the 15 field pea genotypes tested in 6 diverse environments  was mostly explained by the first two principal components of genotypes and environments. Previous reports confirmed that in most of the cases the maximum GEI could be explained through using the first two PCAs (Yan et al., 2002; Fikere et al., 2008; 2014).

Table 3: AMMI analysis of variance of 15 field pea genotypes for the yield over 6 locations.

From the Table 4 of AMMI analysis it was depicted that the yield performance of the tested field pea genotypes were significantly affected by the environment because of significance variance at 1% level of significant, which explained 47% of the total (G+E+GEI) variation, while GE interaction captured 34.81% of the total sum of squares. For environments there were large sum of squares which indicated that the tested environments of the present study were much diverse which further stated that there were also large differences existed among environmental means responsible for differential genotypic response regarding grain yield.

Table 4: Mean yield (kg), AMMI stability values (ASV) and ranking orders of the 15 genotypes across six environments.

There was high influence of GEI towards differential yield performance of the tested genotypes. It was also observed that the first IPCA axis accounted for 47.76% and the second one was 22.06% (Table 4). Considering the ASV ranking, genotype number 10 (VL-65) had the lowest value thus identified as the most stable genotype whereas, genotype number 15 and 13 (HFP-1302 and RPF-10-05) were identified as most unstable genotypes. Existence of GEI was confirmed by the crossover performance of the field pea genotypes, thus implying importance of multi-environment testing. Presence of cross over interaction is non-additive and non separable in nature and suggesting for breeding of specific adaptation (Gregorius and Namkoong, 1986; Baker, 1990; Singh et al., 1999; Yan and Hunt 2002; Adebayo et al., 2017). Based on the weather parameter of the tested environments genotypes exhibited differential response and changed their mean ranking (Malosetti et al., 2013). Genotypes with genetic homeostasis and differential buffering capacity can withstand variable environmental parameter. Genotypes with more buffering capacity exhibits broader adaptation (Bose et al., 2014; Das et al., 2019).

Table 4: Mean yield (kg), AMMI stability values (ASV) and ranking orders of the 15 genotypes across six environments.

AMMI 1 Biplot analysis
The scatter of genotype points in AMMI1 biplot represented in Fig 1 revealed that the interaction of environments was highly varied. Among the different testing environments one location from North-west peninsular zone (Biplot code: NWPZZ) has low interaction, whereas, the location from North west peninsular zone (Biplot Code: NWPZ) and one location from North east peninsular zone (Biplot Code: NEPZ) were highly interactive. All six locations were almost favorable for growing field pea genotypes, though one location each from of North east peninsular zone (Biplot Code: NEPZ) and Central Zone (Biplot Code: CENTRZZ) were least favorable for growing these field pea genotypes.  Amid the tested field pea genotypes, seven genotypes viz. 9 (Pant-P-347), 5 (IPFD-16-3), 6 (Pant-P-345), 13 (RFP-10-05), 7 (RFP-11-09), 4 (KPMR-940) and 14 (IPFD-16-4) have differences only in main (additive) effects. Conversely the two groups of genotypes viz. 4 (KPMR-940), 12 (KPF-14-50) and 11 (HFP-1307), 14 (IPFD-16-4) separately have differences only in interaction effects. While the genotypes like 4 (KPMR-940), 10 (VL-65), 11 (HFP-1307), 2 (HUDP-1601) etc. separately have differences both in main and interaction effects. Therefore it can be stated that the genotypes 2 (HUDP-1601), 10 (VL-65) and 3 (Pant-P-340), 8 (RFP-2010-11) were rather similar with respect to both main and interaction effects. The genotype 4 (KPMR-940), 12 (KPF-14-50), 6 (Pant-P-345) and 14 (IPFD-16-4) had low interaction effect and hence they were detected as stable genotypes. Among them, the genotype 14 (IPFD-16-4) had high mean yield and therefore, it would be recommended for all the tested environments. The other genotypes 2 (HUDP-1601), 10 (VL-65), 11 (HFP-1307) and 1 (NDPD-2016-22) having high interaction effect with the environments were suitable for specific environments. The genotypes 11 (HFP-1307), 15 (HFP-1302), 3 (Pant-P-340) and 8 (RFP-2010-11) with high mean and positive interaction were suited for similar type of interacting environments viz., environment 1 (Biplot Code: NWPZ) and environment 6 (Biplot Code: CENTZZ) respectively. The angle between environment 1 (Biplot Code: NWPZ) and environment 6 (Biplot Code: CENTZZ) was acute hence these two environments exhibited positive correlation thus, having close proximity with each other. Genotype 14 (IPFD-16-4) could perform well either of these two environments. Similarly, genotype 1 (NDPD-2016-22) and genotype 10 (VL-65) could perform better at both environment 3 (Biplot Code: NEPZ) and environment 5 (CENTRLZ). Genotype 9 (Pant-P-347) and 8 (RFP-2010-11) with high interaction effect were not suitable enough at none of these tested environments. Different stability parameters are frequently used by the plant breeders for identifying stable genotypes with broad adaptation. In AMMI biplot, beside mean performance of the genotype, the ASV score also represents the stability of the genotype.  Genotype with low ASV score are considered more stable while genotypes with high values are less stable and suitable for specific adaptation (Purchase et al., 2000; Bavandpori et al., 2014). In field pea in corroboration with the present study ASV score was deployed for identification of stable genotypes (Taye et al., 2000; Tolessa et al., 2013; Rezene et al., 2014; Fikare et al., 2014).

Fig 1: AMMI 1 biplot of main effects and G × E interaction of 15 field pea genotypes across 6 locations.

AMMI 2 biplot analysis
In the AMMI 2 biplot, IPCA 1 and IPCA2 values were used to draw the graph. The biplot 2 provides on the G×E interaction only and not like AMMI 1 as the AMMI biplot 1 included main effect also. From AMMI 2 biplot analysis (Fig 2), it was observed that the genotypes with less interaction in both axes are positioned near the origin and vice versa. Hence, the genotypes nearer to the origin were considered as stable when compared to others. Those genotypes falling apart form the origin were termed as highly interacting genotypes. In the present study, the genotypes 4 (KPMR-940), 2 (HUDP-1601), 10 (VL-65) and 12 (KPF-14-50) with less interaction effect was detected as highly stable. Among the tested environments, environment 6 (Biplot Code: CENTZZZ) was the less interacting environment.

Fig 2: AMMI 2 biplot of G × E interaction of 15 field pea genotypes across 6 locations.

In the present study GEI exhibited towards the differential and incoherent performance of the genotype which justified the need of evaluating the field pea genotypes in diverse environment. Deployment of AMMI model successfully identified stable genotypes across the tested environments. In the present study, genotypes 6 (Pant-P-345), 12(KPF-14-50) and 4(KPMR-940) were identified as stable genotypes based on biplot and ASV values thus could be recommended for general cultivation of the tested areas.
Authors are thankful to the in-charges of the test locations for conducting trials.

  1. Project Coordinator’s Report. (2017). All India Co-ordinated Research Project on MULLaRP (Mung bean,Urd Bean, Lentil, Lathyrus, Rajmash and Pea), IIPR, Kanpur, India. 

  2. Adjebeng-Danquah, J., Manu-Aduening, J., Gracen, V. E., Asante, I. K., and Offei, S. K. (2017). AMMI stability analysis and estimation of genetic parameters for growth and yield components incassava in the forest and Guinea savannah ecologies of Ghana.    International J. Agron. 1-10.

  3. Akbarpour, O., Dehghani, H., Sorkhi, B. and Gauch Jr, H.G., (2014). Evaluation of Genotype × Environment Interaction in Barley (Hordeum vulgare L) based on AMMI model using developed SAS Program. J. Agr. Sci. Tech. 16: 909-920.

  4. Akcura, M., Kaya, Y., Taner, S. and Ayranci, R., (2006). Parametric stability analyses for grain yield of durum wheat. Plant Soil Env. 52(6):254-261.

  5. Annicchiarico, P., (2002). Genotype environment interactions challenge and opportunities for plant breeding and cultivar recommendations. Food and Agriculture Organization, 174p.

  6. Baker, R. J. (1990). Crossover genotype-environmental interaction in spring wheat. Genotype-by-environment interaction and plant breeding. Baton Rouge: Louisiana State Univ. 

  7. Banik, B.R., Khaldun, A.B., Mondal, A.A., Islam, A. and Rohman, M.M., (2010). Assessment of genotype-by environment interaction using additive main effects and multiplicative interaction model (AMMI) in maize (Zea mays L) Hybrids. Acad. J. Plant Sci., 3(4): 134-139.

  8. Bantayehu, M., (2009).Analysis and correlation of stability parameters in malting barley. African Crop Sci. J. 17(3): 145-153.

  9. Bavandpori, F., Ahmadi, J., and Hossaini, S. (2014). Yield stability analysis of bread wheat lines using AMMI model. Agri. Commun.

  10. 3(1): 8-15.

  11. Biswas, T., Mazumder, D. and Das, A., (2018).Discriminating parametric and non-parametric methods for stability and adaptability analysis. Rashi. 3(1): 39-45.

  12. Bose, L. K., Jambhulkar, N. N., Pande, K., and Singh, O. N. (2014). Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chilean J. of Agri. Res. 74(1): 3-9.

  13. Crossa, J., Fox, P.N., Pfeiffer, W.H., Rajaram, S. and Gauch, H.G., (1990). AMMI adjustment for statistical analysis of international wheat yield trial. Theor. Applied Gen. 81: 27-37.

  14. Das, A., Gupta, S., Parihar, A. K., Saxena, D., Singh, D., Singha, K. D. and Chandra, S. (2019). Deciphering genotype-by-environment interaction for targeting test environments and rust resistant genotypes in field pea (Pisum sativum L.). Frontiers Plant Sci. 10: 825. doi: 10.3389/fpls.2019.00825

  15. Ebdon, J.S. and Gauch, H.G., (2002). Additive main effect and multiplicative interaction analysis of national turfgrass performance trials II cultivar recommendations. Crop Sci. 42: 497-506.

  16. Fikere, M., Bing, D. J., Tadesse, T., and Ayana, A. (2014). Comparison of biometrical methods to describe yield stability in field pea (Pisum sativum L.) under south eastern Ethiopian conditions. African J. Agri. Res. 9(33): 2574-2583.

  17. Gauch, H.G., Piepho, H.P. and Annicchiarico, P., (2008). Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations. Crop Sci. 48(3): 866-889.

  18. Gauch, H.G. and Zobel, R.W., (1996). AMMI analyses of yield trials In: Genotype by environment interaction. Kang MS and Gauch HG (eds) CRC, Boca Raton, Florida, p. 85-122.

  19. Gauch, H.G., (1993). Statistical Analysis of Regional Yield Trials: AMMI analysis of factorial designs. Elsevier, London.

  20. Gollob, H.F., (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika. 33: 73-115.

  21. Gregorius, H. R. and Namkoong, G. (1986). Joint analysis of genotypic and environmental effects. Theor. Applied Genet. 72 : 413-422.

  22. Ilker, E., Tonk, F.A., Caylak, O., Tosun, M. and Ozmeni, I., (2009). Assessment of genotype x environment interactions for grain yield in maize hybrids using AMMI and GGE biplot analyses. Turkish J. Field Crops. 14(2): 123–135.

  23. Lin, C.S., Binns, M.R. and Lefkovitch, L.P., (1986). Stability analysis: Where do we stand? Crop Science. 26: 894–900.

  24. Mahalingam, L., Mahendran, S., Chandra, B.R. and Atlin, G., (2006). AMMI analysis for stability of grain yield in rice (Oryza sativa L). International J. Bot. 2(2): 104-106.

  25. Malosetti, M., Ribaut, J. M., and van Eeuwijk, F. A. (2013). The statistical analysis of multi-environment data: modeling genotype-by-    environment interaction and its genetic basis. Frontiers Physiol. 4: 44.

  26. Mohebodini, M., Dehghani, H. and Hossain, S., (2006). Stability of performance in lentil (Lensculinaris medik) genotypes in Iran. Euphytica. 149: 343–352.

  27. Misra, R.C., Das, S. and Patnaik, M.C., (2009). AMMI model analysis of stability and adaptability of late duration finger millet (Eleusine coracana). World Applied Sci. J. 6(12): 1650-1654.

  28. Muhe, K. and Assefa, A., (2010). Genotypes × environment interaction in bread wheat (Triticum aestivum L) cultivar development in Ethiopia International Research. J. Plant Sci. 2(10): 317-322.

  29. Pacheco, A., Vargas, M., Alvarado, G., Rodríguez, F., López, M., Crossa, J. and Burgueño, J., (2016). GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.0.CIMMYT Research Data and Software Repository Network.

  30. Purchase, J. L., Hatting, H., and Van Deventer, C. S. (2000). Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African J. Plant and Soil. 17(3): 101-107.

  31. Rezene, Y., Bekele, A., and Goa, Y. (2014). GGE and AMMI biplot analysis for field pea yield stability in SNNPR state, Ethiopia.    International J. Sustainable Agri. Res. 1(1): 28-38.

  32. Rodriguez, M., Rau, D., Papa, R. and Attene, G., (2007). Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica. 163(2): 231-247.

  33. Sadeghi, S.M., Samizadeh, H., Amiri, E. and Ashouri, M., (2011). Additive main effects and multiplicative interactions (AMMI) analysis of dry leaf yield in tobacco hybrids across environments. African J. Biotech. 10(21): 4358-4364.

  34. Singh, M., Ceccarelli, S. and Grando, S. (1999). Genotype x environment interaction of crossover type: detecting its presence and estimating the crossover point. Theor. Applied Genet. 99: 988-995.

  35. Taye, G., Getachew, T., and Bejiga, G. (2000). AMMI adjustment for yield estimate and classification of genotypes and environments in field pea (Pisum sativum L.). Journal Genet. 54(3): 183-191.

  36. Tolessa, T. T., Keneni, G., Sefera, T., Jarso, M. and Bekele, Y. (2013). Genotype× environment interaction and performance stability for grain yield in field pea (Pisum sativum L.) genotypes. International J. Plant Breed. 7(2): 116-123.

  37. Yan, W. and Kang, M.S., (2002). GGE biplot analysis: A graphical tool for breeders’ geneticists and agronomists. CRC Press, Boca Raton, Florida.

  38. Yan, W., Cornelius, P.L., Crossa, J. and Hunt, L.A., (2001). Two types of GGE biplots far analyzing multi-environment trial data. Crop Sci. 41: 656-663.

  39. Yan, W. and Hunt, L. A. (2002). Biplot analysis of diallel data. Crop Sci. 42: 21-30. doi:10.2135/cropsci2002.2100.

  40. Yan, W. and Rajcan, I., (2002). Biplots analysis of the test sites and trait relations of soybean in Ontario. Crop Sci. 42: 11-20.

  41. Yan, W., Kang, M.S., Ma, B., Woods, S. and Cornelius, P.L., (2007). GGE biplot vs AMMI analysis of genotype-by-environment data. Crop Sci. 47(2):643–655.

  42. Zobel, R.W., Wright, M.J. and Gauch, H.G., (1988). Statistical analysis of a yield trial. Agron. J. 80(3): 388-393. 

Editorial Board

View all (0)