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Correlations, path coefficient analysis and phenotypic diversity of a West African germplasm of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet]

  • Félicien Akohoue
  • Enoch G. Achigan-DakoEmail author
  • Mariam Coulibaly
  • Julia Sibiya
Research Article
  • 3 Downloads

Abstract

Providing the growing population with quality diet under the changing climate requires renewed efforts on the breeding of orphan grain legumes that need to be adequately characterized for accelerated cultivar development, particularly in sub-Saharan Africa where food and nutritional insecurity remains a critical challenge. In this study, the phenotypic diversity of a West African germplasm of Kersting’s groundnut was determined and the possibility for indirect selection explored. In total, 297 accessions collected across diverse ecological zones in Benin and Togo were assessed using 19 descriptors in two contrasting environments. Correlation and path coefficients analyses were performed to determine association patterns among variables. Hierarchical cluster analysis was carried out to group accessions having similar performance across locations. Based on the results, the diversity panel was categorized into four clusters with clusters 2 and 4 containing the best performing accessions. Significant moderate phenotypic correlation was detected between seed coat colour and days to 50% flowering (r = − 0.63). Likewise, significant and moderate to strong positive genetic correlations were observed between grain yield with number of seeds per pod (rg = 0.60), 100 seed weight (rg = 0.70), number of seeds per plant (rg = 0.96) and number of pods per plant (rg = 0.90). However, significant weak negative correlations were revealed among grain yield, days to 50% flowering (rg = − 0.32) and days to maturity (rg = − 0.30). Moreover, high direct effects were detected among grain yield and number of seeds per plant, 100 seed weight and days to 50% flowering. The findings imply that indirect selection for grain yield in Kersting’s groundnut, using 100 seed weight, number of seeds per plant and days to 50% flowering could be relevant to increase the efficiency of breeding programmes. Accessions 02_AF169, 02_AF51, 02_AF202, 02_AF222, 02_AF196, 02_AF100, 02_AF255, 02_AF216, 02_AF223 and 02_AF199 could also be used as parental lines for the development of high yielding varieties.

Keywords

Accession Indirect selection Macrotyloma geocarpum Phenotypic diversity West africa 

Notes

Acknowledgements

This work was financially supported by the Mobreed project funded by the Intra-Africa Mobility program of the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission. We are grateful to Monique M. Sognigbé, Ulrich Djido, Abdou Rachidi Francisco, Herbaud P.F. Zohoungbogbo, Nouroudine Soulemane, Christel Azon, Xavier C. Matro, Valère Awomenou, Idrissou Ahoudou, Fernand S. Sohindjo, Jacob Houeto, Marie-Michelle Codja, Carmen Bonou, Ardy Hinvi, Eliel B. Sossou, Wenceslas M.S. Ahouangan and Jelila S. Blalogoe from the Laboratory of Genetics, Horticulture and Seed Science (GBioS) of the University of Abomey-Calavi for their assistance during data collection.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

Supplementary material

10722_2019_839_MOESM1_ESM.xlsx (60 kb)
Supplementary file1 (XLSX 60 kb)

References

  1. Achigan-Dako EG, Vodouhè SR (2006) Macrotyloma geocarpum (Harms) Maréchal & Baudet. In: Brink M, Belay G (eds) Plant resources of tropical Africa 1 cereals and pulses. Backhuys Publishers CTA, PROTA, Wageningen, pp 111–114Google Scholar
  2. Adebooye O, Ajadi S, Fagbohun A (2006) An accurate mathematical formula for estimating plant population in a four dimensional field of sole crop. J Agron 5:289–292CrossRefGoogle Scholar
  3. Adomou A (2005) Vegetation patterns and environmental gradients in Benin. Implications for biogeography and conservation. Wageningen University, WageningenGoogle Scholar
  4. Adu-Gyamfi R, Dzomeku IK, Lardi J (2012) Evaluation of growth and yield potential of genotypes of Kersting’s groundnut (Macrotyloma geocarpum Harms) in Northern Ghana. IRJAS 2:509–515Google Scholar
  5. Akoègninou A, Van der Burg W, Van der Maesen LJG (2006) Flore analytique du Bénin, vol 06.2. Backhuys Publishers, WageningenGoogle Scholar
  6. Akohoué F, Sibiya J, Achigan-Dako EG (2018) On-farm practices, mapping, and uses of genetic resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] across ecological zones in Benin and Togo. Genet Resour Crop Evol 66:1–20Google Scholar
  7. Alake CO, Ayo-Vaughan MA, Ariyo JO (2015) Selection criteria for grain yield and stability in bambara groundnut (Vigna subterranean (L) Verdc) landraces. Acta Agric Scand Sect B Soil Plant Sci 65:433–447Google Scholar
  8. Assogba P, Ewedje E-EBK, Dansi A, Loko YL, Adjatin A, Dansi M, Sanni A (2016) Indigenous knowledge and agro-morphological evaluation of the minor crop Kersting’s groundnut (Macrotyloma geocarpum (Harms) Maréchal et Baudet) cultivars of Benin. Genet Resour Crop Evol 63:513–529CrossRefGoogle Scholar
  9. AVRDC (2015) AVRDC-GRSU characterization record sheet. Crop: Macrotyloma spp. https://seed.worldveg.org/download. Accessed 4 Sept 2018
  10. Ayenan MAT, Ezin VA (2016) Potential of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet] and prospects for its promotion. Agric Food Secur 5:1–10CrossRefGoogle Scholar
  11. Bampuori AH (2007) Effect of traditional farming practices on the yield of indigenous Kersting's Groundnut (Macrotyloma geocarpum Harms) crop in the upper West region of Ghana. J Dev Sustain Agric 2:128–144Google Scholar
  12. Bayorbor T, Dzomeku I, Avornyo V, Opoku-Agyeman M (2010) Morphological variation in Kersting’s groundnut (Kerstigiella geocarpa Harms) landraces from northern Ghana. Agric Biol J N Am 1:290–295CrossRefGoogle Scholar
  13. Bhardu D, Navale P (2011) Correlation and path analysis studies in F3 population of cowpea (Vigna unguiculata (L.) Walp.). Legume Res 34:41–44Google Scholar
  14. Bhatt GM (1973) Significance of path coefficient analysis in determining the nature of character association. Euphytica 22:338–343CrossRefGoogle Scholar
  15. Challinor AJ, Koehler A-K, Ramirez-Villegas J, Whitfield S, Das B (2016) Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat Clim Change 6:954–960CrossRefGoogle Scholar
  16. Cleasby P, Massawe FJ, Symonds RS (2016) Bambara groundnut for food security in the changing African climate. In: Lichtfouse E (ed) Sustainable agriculture reviews, vol 19. Springer, Cham, pp 363–389CrossRefGoogle Scholar
  17. Considine MJ, Siddique KHM, Foyer CH (2017) Nature's pulse power: legumes, food security and climate change. J Exp Bot 68:1815–1818CrossRefGoogle Scholar
  18. Crossa J (2014) META-R—3.5.1. CIMMYT, MexicoGoogle Scholar
  19. Cullis C, Kunert KJ (2017) Unlocking the potential of orphan legumes. J Exp Bot 68:1895–1903PubMedGoogle Scholar
  20. Dansi A, Vodouhè R, Azokpota P, Yedomonhan H, Assogba P, Adjatin A, Loko Y, Dossou-Aminon I, Akpagana K (2012) Diversity of the neglected and underutilized crop species of importance in Benin. Sci World J 2012:1–19CrossRefGoogle Scholar
  21. Daryanto S, Wang L, Jacinthe P-A (2017) Global synthesis of drought effects on cereal, legume, tuber and root crops production: a review. Agric Water Manag 179:18–33CrossRefGoogle Scholar
  22. De Mendiburu F (2017) Agricolae: statistical procedures for agricultural research. R package version 128. https://cran.r-project.org/web/packages/agricolae/index.html
  23. Gbaguidi A, Dansi A, Dossou-Aminon I, Gbemavo D, Orobiyi A, Sanoussi F, Yedomonhan H (2018) Agromorphological diversity of local Bambara groundnut (Vigna subterranea (L.) Verdc.) collected in Benin. Genet Resour Crop Evol 65:1159–1171CrossRefGoogle Scholar
  24. Grum M, Atieno F (2007) Statistical analysis for plant genetic resources: clustering and indices in R made simple. Handbooks for Genebanks no. 9. Bioversity International, RomeGoogle Scholar
  25. Hongyu K, García-Peña M, de Araújo LB, dos Santos Dias CT (2014) Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction. Biom Lett 51:89–102CrossRefGoogle Scholar
  26. Komsta L, Komsta ML (2011) Package ‘outliers’. Medical University of Lublin, LublinGoogle Scholar
  27. Kouelo FA, Badou A, Houngnandan P, Francisco FMM, Gnimassoun J-BC, Sochime JD (2012) Impact du travail du sol et de la fertilisation minérale sur la productivité de Macrotyloma geocarpum (Harms) Maréchal & Baudet au centre du Bénin. J Appl Biosci 51:3625–3632Google Scholar
  28. Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25:1–18CrossRefGoogle Scholar
  29. Leng G, Huang M (2017) Crop yield response to climate change varies with crop spatial distribution pattern. Sci Rep 7:1–10CrossRefGoogle Scholar
  30. Lopes K, Teodoro P, Silva F, Silva M, Fernandes R, Rodrigues T, Faria T, Corrêa A (2017) Genetic parameters and path analysis in cowpea genotypes grown in the Cerrado/Pantanal ecotone. Gene Conserve 16:1–11Google Scholar
  31. Mashilo J, Shimelis H, Odindo A (2016) Correlation and path coefficient analyses of qualitative and quantitative traits in selected bottle gourd landraces. Acta Agric Scand Sect B Soil Plant Sci 66:558–569Google Scholar
  32. Mergeai G (1993) Influence des facteurs sociologiques sur la conservation des ressources phytogénétiques. Le cas de la lentille de terre (Macrotyloma geocarpum (Harms) Marechal & Baudet) au Togo. Bull rech agron 28:487–500Google Scholar
  33. Mwale S, Azam-Ali S, Massawe F (2007) Growth and development of bambara groundnut (Vigna subterranea) in response to soil moisture: 1. Dry matter and yield. Eur J Agron 26:345–353CrossRefGoogle Scholar
  34. Neisse AC, Kirch JL, Hongyu K (2018) AMMI and GGE Biplot for genotype × environment interaction: a medoid-based hierarchical cluster analysis approach for high-dimensional data. Biometrical Lett:1–25Google Scholar
  35. Nelson GC, Rosegrant MW, Koo J, Robertson R, Sulser T, Zhu T, Ringler C, Msangi S, Palazzo A, Batka M (2009) Climate change: impact on agriculture and costs of adaptation, vol 21. International Food Policy Research Institute (IFPRI), WashingtonGoogle Scholar
  36. Ntundu W, Shillah S, Marandu W, Christiansen JL (2006) Morphological diversity of bambara groundnut [Vigna subterranea (L.) Verdc.] landraces in Tanzania. Genet Resour Crop Evol 53:367–378CrossRefGoogle Scholar
  37. Olivoto T, de Souza VQ, Nardino M, Carvalho IR, Ferrari M, de Pelegrin AJ, Szareski VJ, Schmidt D (2017) Multicollinearity in path analysis: a simple method to reduce its effects. Agron J 109:131–142CrossRefGoogle Scholar
  38. Oyetayo FL, Ajayi OB (2011) Kersting's nut (Kerstingiella Geocarpa): a source of food and medicine. In: Preedy RV, Watson RR, Patel BV (eds) Nuts and seeds in health and disease prevention. Elsevier, Oxford, pp 693–698CrossRefGoogle Scholar
  39. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
  40. Saka JO, Ajibade SR, Adeniyan ON, Olowoyo RB, Ogunbodede BA (2004) Survey of underutilized grain legume production systems in the southwest agricultural zone of Nigeria. J Agric Food Inf 6:93–108CrossRefGoogle Scholar
  41. Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet 9:255–266CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Laboratory of Genetics, Horticulture and Seed Science, Faculty of Agronomic SciencesUniversity of Abomey-CalaviCotonouRepublic of Benin
  2. 2.School of Agricultural, Earth and Environmental SciencesUniversity of KwaZulu-NatalPietermaritzburgRepublic of South Africa

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