Advertisement

Euphytica

, Volume 164, Issue 1, pp 143–161 | Cite as

Genotype × environment interaction for yield and reaction to leaf spot infections in groundnut in semiarid West Africa

Genotype × environment interaction and leaf spot resistance in groundnut
  • Francis Kwame PadiEmail author
Article

Abstract

Capitalizing on the yield potential in available groundnut germplasm, and high stability of kernel yield are important requirements for groundnut producers in semiarid environments. Forty-seven groundnut genotypes were evaluated from 2003 to 2005 at 4 locations representative of the Guinea and Sudan savanna ecologies in Ghana. The objectives were to assess genotypic differences in reaction to early and late leaf spot infections under natural field conditions, assess the extent of genotype × environment (G × E) interaction for kernel yield, and determine the relationship between yield potential and yield stability. Genotypes differed significantly in their reaction to leaf spot infections indicated by the area under disease progress curve (AUDPC). Genotypic AUDPC was negatively correlated with maturity period (P < 0.01), with kernel yield (P < 0.05) at each of the 3 locations in the Guinea savanna ecology but not in the Sudan savanna ecology and with each of four stability parameters (P < 0.05). High or low yielding genotypes were grouped based on Dunnett’s test at P < 0.10. High yielding groups had significantly low AUDPC, high biomass, high partitioning of dry matter for kernel growth, and were later in maturity compared to low yielding genotypes. Significant G × E interaction effect for kernel yield was dominated mainly by the lack of correlation among environments variance (76–78%) relative to the heterogeneity of genotypic variance component (22–24%). Stability of yield assessed through the among-environment variance, Wricke’s ecovalence, and Finlay-Wilkinson regression coefficient revealed that genotypes in the higher yielding group were relatively unstable compared to the low yielding group. Indicated by the Kataoka’s index of yield reliability, however, relatively unstable genotypes in the high yielding group are expected to be more productive even under assumptions of high risk aversion (P = 0.75–0.95) compared to the more stable, low yielding genotypes. The findings indicate that deploying these recently developed germplasm in semiarid regions in West Africa provides a better match to farmers’ risk-averse strategies compared with the use of existing earlier maturing cultivars.

Keywords

Cercospora leaf spot Genotype × environment interaction Groundnut Yield stability 

Notes

Acknowledgements

This work was supported by the Food Crops Development Project (FCDP) under the Ministry of Food and Agriculture, Ghana; and the Agricultural Services sub-Sector Investment Programme (AgSSIP) of the Government of Ghana.

References

  1. Annicchiarico P (2002) Genotype × environment interactions: challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Protection and Production Papers-174. 126 pp Available online: http://www.fao.org/documents/pub_dett.asp?pub_id=103142&lang=en Accessed November 11, 2006
  2. Asafo-Adjei B, Singh BB, Atuahen-Amankwah G (2005) Registration of ‘Asontem’ cowpea. Crop Sci 45:2649CrossRefGoogle Scholar
  3. Baker RJ (1990) Crossover genotype-environmental interaction in spring wheat. In: Kang MS (ed) Genotype-by-environment interaction and plant breeding. Louisiana State University, Baton Rouge, LA, pp 42–51Google Scholar
  4. Basford KE, Cooper M (1998) Genotype × environment interactions and some considerations of their implications for wheat breeding in Australia. Aust J Agric Res 49:153–174CrossRefGoogle Scholar
  5. Becker WA (1975) Manual of quantitative genetics. Washington State University Press, Pullman, USA, 170 ppGoogle Scholar
  6. Becker HC, Léon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23CrossRefGoogle Scholar
  7. Blum A (1989) Breeding methods for drought resistance. In: Jones FG, Flowers TJ, Jones MB (eds) Plants under stress. Cambridge University Press, Cambridge, UK, pp 197–216Google Scholar
  8. Byth DE, Eissmann RL, Delacy IH (1976) Two-way pattern analyses of a large data set to evaluate genotypic adaptation. Heredity 37:215–230CrossRefGoogle Scholar
  9. Calderini DF, Slafer GA (1999) Has yield stability changed with genetic improvement of wheat yield? Euphytica 107:51–59CrossRefGoogle Scholar
  10. Ceccarelli S (1989) Wide adaptation: how wide? Euphytica 40:197–205Google Scholar
  11. Ceccarelli S, Grando S (1991) Selection environment and environmental sensitivity in barley. Euphytica 57:157–167CrossRefGoogle Scholar
  12. Cooper M, Delacy IH, Basford KE (1996) Relationships among analytical methods used to study genotypic adaptation in multi-environment trials. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CABI Publishing, Wallingford, UK, pp 193–224Google Scholar
  13. Craufurd PQ, Prasad PV, Summerfield RJ (2002) Dry matter production and rate of change of harvest index at high temperature in groundnut. Crop Sci 42:146–151PubMedCrossRefGoogle Scholar
  14. Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85CrossRefGoogle Scholar
  15. Dickerson GE (1962) Implications of genetic-environmental interaction in animal breeding. Anim Prod 4:47–63CrossRefGoogle Scholar
  16. Duncan WG, McGraw RL, Boote KJ (1978) Physiological aspects of groundnut yield improvement. Crop Sci 18:1015–1020CrossRefGoogle Scholar
  17. Ebdon JS, Gauch Jr HG (2002) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials. I. Interpretation of genotype × environment interaction. Crop Sci 42:489–496CrossRefGoogle Scholar
  18. Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40CrossRefGoogle Scholar
  19. Ekbohm G (1981) A test for the equality of variances in the paired case with incomplete data. Biom J 23:261–265CrossRefGoogle Scholar
  20. Flores F, Moreno MT, Cubero JJ (1998) A comparison of univariate and multivariate methods to analyze G × E interaction. Field Crops Res 56:271–286CrossRefGoogle Scholar
  21. Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 14:742–754CrossRefGoogle Scholar
  22. Frimpong A, Padi FK, Kombiok J, Salifu AB, Marfo KO (2006a) Registration of ‘Edorpo-Munikpa’ groundnut. Crop Sci 46:1396–1397CrossRefGoogle Scholar
  23. Frimpong A, Padi FK, Kombiok J (2006b) Registration of foliar disease resistant and high yielding groundnut varieties, ICGV 92099 and ICGV 90084. Int Arachis Newslett 26:22–24Google Scholar
  24. Gauch HG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Sci 37:311–326CrossRefGoogle Scholar
  25. Greenberg DC, Williams JH, Ndunguru BJ (1992) Differences in yield determining processes of groundnut (Arachis hypogaea L.) genotypes in varied drought environments. Ann Appl Biol 120:557–566CrossRefGoogle Scholar
  26. Gupta SS (1965) On some multiple decision (selection and making) rules. Technometrics 7:225–246CrossRefGoogle Scholar
  27. Holbrook CC, Dong W (2005) Development and evaluation of a mini core collection for the U.S. groundnut germplasm collection. Crop Sci 45:1540–1544CrossRefGoogle Scholar
  28. Holland JB (2006) Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci 46:642–654CrossRefGoogle Scholar
  29. Jackson PA, McRae TA (1998) Gains from selection of broadly adapted and specifically adapted sugarcane families. Field Crops Res 59:151–162CrossRefGoogle Scholar
  30. Kang MS (1998) Using genotype-by-environment interaction for crop cultivar development. Adv Agron 62:199–252CrossRefGoogle Scholar
  31. Kataoka S (1963) A stochastic programming model. Econometrika 31:181–196CrossRefGoogle Scholar
  32. Knapp SJ, Stroup WW, Ross WM (1985) Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:192–194CrossRefGoogle Scholar
  33. Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: where do we stand? Crop Sci 26:894–900CrossRefGoogle Scholar
  34. Marfo KO, Padi FK (1999) Yield stability of some groundnut accessions in Northern Ghana. Ghn J Agric Sci 32:137–144Google Scholar
  35. McCloud DE, Duncan WE, McGraw RL, Sibale PK, Ingram KT, Dreyer J, Campbell IS (1980) Physiological basis for increased yield potential in groundnuts. In: Gibbons, RW (ed), Proceedings of international workshop on groundnuts. International crops research institute for the semi-arid tropics, Patancheru, Andhra Pradesh, India, pp 125–131Google Scholar
  36. McDonald D, Subrahmanyam P, Gibbons RW, Smith DH (1985) Early and late leaf spots of groundnuts. Information Bulletin No. 21. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Andhra Pradesh, India, 19 ppGoogle Scholar
  37. Nigam SN, Dwivedi SL, Gibbons RW (1991) Groundnut breeding, constraints, achievements and future possibilities. Plant Breeding Abstr 61:1127–1136Google Scholar
  38. Padi FK (2004) Relationship between stress tolerance and grain yield stability in cowpea. J Agric Sci Camb 142:431–443CrossRefGoogle Scholar
  39. Padi FK, Frimpong A, Kombiok J, Salifu AB, Marfo KO (2006) Registration of ‘Nkatiesari’ groundnut. Crop Sci 46:1397–1398CrossRefGoogle Scholar
  40. Pande S, Bandyopadhyay R, Blümmel J, Narayana Rao J, Thomas D, Nasi SS (2003) Disease management factors influencing yield and quality of sorghum and groundnut residues. Field Crops Res 84:89–103CrossRefGoogle Scholar
  41. Sinebo W (2005) Trade off between yield increase and yield stability in three decades of barley breeding in a tropical highland environment. Field Crops Res 92:35–52CrossRefGoogle Scholar
  42. Singh AK, Mehan VK, Nigam SN (1997) Sources of resistance to groundnut fungal and bacterial diseases: an update and appraisal. Information Bulletin No. 50. International crops research institute for the semi-arid tropics, Patancheru, Andhra Pradesh, India, 44 ppGoogle Scholar
  43. Steel RGD, Torrie JH (1960) Principles and procedures of statistics. McGraw-Hill, New YorkGoogle Scholar
  44. Subrahmanyam P, McDonald D, Waliyar F, Reddy LJ, Nigam SN, Gibbons RW, Ramanatha Rao V, Singh AK, Pande S, Reddy PM, Subba Rao PV (1995) Screening methods and sources of resistance to rust and late leaf spot of groundnut. Information Bulletin No. 47. International crops research institute for the semi-arid tropics, Patancheru, Andhra Pradesh, India, 24 ppGoogle Scholar
  45. Tollenaar M, Lee EA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crops Res 75:161–169CrossRefGoogle Scholar
  46. Wricke G (1962) Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen Z. Pflanzenzüchtg 47:92–96Google Scholar
  47. Wu R, Stettler RF (1997) Quantitative genetics of growth and development in Populus. II. The partitioning of genotype × environment interaction in stem growth. Heredity 78:124–134Google Scholar
  48. Yates S, Cochran WG (1938) The analyses of groups of experiments. J Agric Sci 28:556–580Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.CSIR, Savanna Agricultural Research InstituteTamaleGhana

Personalised recommendations