Genotype × year interaction of pod and seed mass and stability of Pongamia pinnata families in a semi-arid region

Abstract

Sixteen pongamia families were evaluated in a field experiment for eight consecutive years in dryland conditions to identify stable, high-yielding families. The trial was conducted in a randomized complete block design with three replications. Each family, consisting of nine trees per replication, was planted at a spacing of 3 m × 3 m. Yield stability was analyzed using (1) Eberhart and Russel’s regression coefficient (β _i) and deviation from regression (

S_{\text{d}}^2

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), (2) Wrike’s ecovalence (

W_i

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); (3) Shukla stability variance (

{\sigma }_i^2

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); and (4) Piepho and Lotito’s stability index (

L_i

\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_{i}$$\end{document}

). Families were also analyzed for adaptability and stability using AMMI and GGE biplots graphical methods. The study revealed significant variances due to family and family × year interaction for pod and seed yield. Families performed differently and ranked differently across years. The performance of families was influenced by both genetic factor and environmental conditions in different years. Among families tested, TNMP20, Acc14, TNMP14 and Acc30 were high yielders for pods, and Acc14, Acc30, TNMP6, RAK19 and TNMP14 were high for seed yield. According to the Eberhart and Russell model, Acc30, TNMP14 and TNMP3 were stable across years. In the graphical view of family × year interaction based on AMMI methods, TNMP3, TNMP4 and TNMP14 had greater stability with moderate seed yield, and Acc14 and Acc30 had moderate stability with high seed yield. On the other hand, GGE biplots revealed Acc14, Acc30 and TNMP14 as high yielders with moderate stability. AMMI and GGE biplots were able to capture nonlinear parts of the family × year interaction that were not be captured by the Eberhart and Russel model while also identifying stable families. Based on different methodologies, Acc14, Acc30 and TNMP14 were identified as high yielding and stable families for promoting pongamia cultivation as a biofuel crop for semi-arid regions.

Keywords

Biofuel / Pongamia / Genetic diversity / Stability / AMMI (additive main effects multiplicative interaction) / GGE biplots / Multi-year trial / SVD (singular value decomposition)

References

[1] Ahlawat SP, Kumar RV, Ranjan R, Pandey SK, Joshi DC, Dhyani SK. Morphological and molecular level of genetic diversity among Pongamia [Pongamia pinnata (L.) Pierre] accessions. Ind J Bio, 2016, 15: 85-94.

[2] Allard RW, Bradshaw AD. Implications of genotype-environmental interactions in applied plant breeding. Crop Sci, 1964, 4: 503-508.

[3] Allen ON, Allen EK. The leguminosae, 1981, Madison: The University of Wisconsin Press 812

[4] Al-Ubaidi MO, Al-kaisy AM, Al-issawi MH, Fadhel F, Fuller M. Performance assessment of wheat cultivars under three locations using GGE-biplot. J Gen Env Res Cons, 2013, 1: 262-270.

[5] Arshadi A, Karami E, Sartip A, Zare M, Rezabakhsh P. Genotypes performance in relation to drought tolerance in barley using multi-environment trials. Agr Res, 2018

[6] Bala M, Nag TN, Kumar S, Vyas M, Kumar A, Bhogal NS. Proximate composition and fatty acid profile of Pongamia pinnata, a potential biodiesel crop. J Am Oil Chem Soc, 2010

[7] Baxevanos D, Goulas C, Tzortzios S, Mavromatis A. Interrelationship among and repeatability of seven stability indices estimated from commercial cotton (Gossypium hirsutum L.) variety evaluation trials in three Mediterranean countries. Euphytica, 2008, 161: 371-382.

[8] Changizi M, Choukan R, Heravan EM, Bihamta MR, Darvish R. Evaluation of genotype environment interaction and stability of corn hybrids and relationship among univariate parametric methods. Can J Plant Sci, 2014, 94: 1255-1267.

[9] Cornelius J. The effectiveness of plus-tree selection for yield. For Ecol Manag, 1994, 67: 23-34.

[10] Dehghani H, Sabaghpour SH, Sabaghnia N. Genotype environment interaction for grain yield of some lentil genotypes and relationship among univariate stability statistics. Span J Agric Res, 2008, 6: 385-394.

[11] Dia M, Wehner TC, Arellano C. Analysis of genotype × environment interaction (G × E) using SAS programming. Agron J, 2016, 108: 1838-1852.

[12] Divakara BN, Das R. Variability and divergence in Pongamia pinnata for further use in tree improvement. J of For Res, 2011, 22(2): 193-200.

[13] Ebdon JS, Gauch HG. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci, 2002, 42: 489-496.

[14] Eberhart SA, Russel WA. Stability parameters for comparing varieties. Crop Sci, 1966, 6: 36-40.

[15] Elanchezhiyan M, Rajarajan S, Rajendran P, Subramanian S, Thyagarajan SP. Antiviral properties of the seed extract of an Indian medicinal plant, Pongamia pinnata. Linn., against herpes simplex viruses: in vitro studies on Vero cells. J Med Microbiol, 1993, 38: 262-264.

[16] Fan XM, Kang MS, Chen H, Zhang Y, Tan J, Xu C. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agr J, 2007, 99: 220-228.

[17] Farshadfar E, Mohammadi R, Aghaee M, Vaisi Z. GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Aus J of Crop Sci, 2012, 6: 1074.

[18] Fisher RA. Design of experiments (8th ed., 1966), 1935, New York: Hafner Press.

[19] Gauch HG. Statistical analysis of regional yield trials: AMMI analysis of factorial designs, 1992, Amsterdam: Elsevier.

[20] Gauch HG. Statistical analysis of yield trials by AMMI and GGE. Crop Sci, 2006, 46: 1488-1500.

[21] Goel VL, Behl HM. Genetic selection and improvement of hard wood tree species for fuelwood production on sodic soil with particular reference to Prosopis juliflora. Biomass Bioenerg, 2001, 20: 9-15.

[22] Hamayoon R, Khan H, Naz L, Munir I, Arif M, Khalil IA, Khan AZ. Performance of chickpea genotypes under two different environmental conditions. Afr J of Biotechnol, 2011, 10: 1534.

[23] Harrington KJ. Chemical and physical properties of vegetable oil esters and their effect on diesel fuel performance. Biomass, 1986, 9: 1-17.

[24] Jaisankar I, Sankaran M, Singh DR, Damodaran V. Genetic variability and divergence studies in pod and seed traits of Pongamia pinnata (L.) Pierre, accessions in Bay Islands. J For Res, 2014, 25(2): 351-358.

[25] Kang MS, Pham HN. Simultaneous selection for high yielding and stable crop genotypes. Agron J, 1991, 83: 161-165.

[26] Kaushik N, Kumar S, Kumar K, Beniwal RS, Kaushik N, Roy S. Genetic variability and association studies in pod and seed traits in Pongamia pinnata (L.) Pierre in Haryana India. Genet Resour Crop Evol, 2007, 54: 1827-1832.

[27] Kesari V, Rangan L. Development of Pongamia pinnata as an alternative biofuel. J Crop Sci and Biotech, 2010, 13(3): 127-137.

[28] Lin CS, Binns MR. Concepts and methods for analyzing regional yield trial data for cultivar and location selection. Plant Breed Rev, 1994, 12: 271-297.

[29] Mohammadi R, Amri A. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 2008, 159: 419-432.

[30] Mohammadi R, Roostaei M, Ansari Y, Aghaee H, Amri A. Relationships of phenotypic stability measures for genotypes of three cereal crops. Can J Plant Sci, 2010, 90: 819-830.

[31] Mukta N, Sreevalli Y. Propagation techniques, evaluation and improvement of the biodiesel plant, Pongamia pinnata (L.) Pierre—a review. Ind Crops and Prod, 2010, 31: 1-12.

[32] Mukta N, Murthy IYLN, Sripal P. Variability assessment in Pongamia pinnata (L.) Pierre germplasm for biodiesel traits. Ind Crop Prod, 2009, 29: 536-540.

[33] Patil VK, Naik GR. Variability in pod and seed traits of Pongamia pinnata Pierre ecotypes in North Karnataka, India. J For Res, 2016, 27(3): 557-567.

[34] Piepho HP, Lotto S. Rank correlation among parametric and nonparametric measures of phenotypic stability. Euphytica, 1992, 64: 221-225.

[35] Pourdad SS. Repeatability and relationships among parametric and non-parametric yield stability measures in safflower (Carthamus tinctorius L.) genotypes. Crop Breed J, 2011, 1: 109-118.

[36] Punitha R, Manoharan S. Antihyperglycemic and antilipidper- oxidative effects of Pongamia pinnata (Linn.) Pierre flowers in alloxan induced diabetic rats. J Ethnopharmacol, 2006, 105: 39-46.

[37] Raddad EY, Luukkanen O. Adaptive genetic variation in water-use efficiency and gum yield in Acacia senegal provenances grown on clay soil in the Blue Nile region, Sudan. For Ecol Manag, 2006, 226: 219-229.

[38] Rakshit S, Ganapathy KN, Gomashe SS, Rathore A, Ghorade RB, Nagesh Kumar MV, Ganesmurthy K, Jain SK, Kamtar MY, Sachan JS, Ambekar SS, Ranwa BR, Kanawade DG, Balusamy M, Kadam D, Sarkar A, Tonapi VA, Patil JV. GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica, 2012, 185: 465-479.

[39] Rameshthangam R, Ramasamy P. Antiviral activity of bis (2-methylheptyl) phthalate isolated from Pongamia pinnata leaves against white spot syndrome virus of Penaeus monodon. Fabr Virus Res, 2007, 126: 38-44.

[40] Rao CR. Estimation of heteroscedastic variances in linear models. J Am Stat Assoc, 1970, 65: 161-172.

[41] Sarkar B, Sharma RC, Verma RPS, Sarkar A, Sharma I. Identifying superior feed barley genotypes using GGE biplot for diverse environments in India. Ind J Gen Pl Breed, 2014, 74(1): 26-33.

[42] Shukla GK. Some aspects of partitioning genotype-environmental components of variability. J Hered, 1972, 28: 237-245.

[43] Wanyancha W, Mills R, Gwaze DP. Genetic variation in Acacia albida (Faidherbia albida) and its agroforestry potential in Zimbabwe. For Ecol Manag, 1994, 64: 127-134.

[44] Wricke G. On a method of understanding the biological diversity in field research. Z Pfl Zucht, 1962, 47: 92-146.

[45] Yan W, Kang MS. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists, 2003, Boca Raton: CRC Press.

[46] Yan W, Tinker NA. Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci, 2006, 86: 623-645.

[47] Yan W, Hunt LA, Sheng WQ, Szlavnics Z. Cultivar evaluation and mega environment investigation based on the GGE biplot. Crop Sci, 2000, 40: 597-605.

[48] Zobel B, Talbert J. Applied forest tree improvement, 1984, New York: Wiley.

[49] Zobel RW, Wright MJ, Gauch HG. Statistical-analysis of a yield trial. Agron J, 1988, 80: 388-393.