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Euphytica

, 214:155 | Cite as

AMMI analysis of cassava response to contrasting environments: case study of genotype by environment effect on pests and diseases, root yield, and carotenoids content in Cameroon

  • Apollin Kuate Fotso
  • Rachid Hanna
  • Peter Kulakow
  • Elisabeth Parkes
  • Peter Iluebbey
  • Francis Ajebesone Ngome
  • Christopher Suh
  • Jacques Massussi
  • Ibrahim Choutnji
  • Venasius Lendzemo Wirnkar
Article
  • 93 Downloads

Abstract

Genotype by environment interaction remains a substantial issue in all breeding programs. Crop genotypes are generally developed in a central breeding location, but always require the evaluation of breeding products in different environments. This is particularly relevant in countries that have a wide range of climates. Eighteen cassava genotypes were evaluated in Cameroon in eight environments—varying in seasonal rainfall and temperature patterns and soil characteristics—over two cropping seasons. Soil nutrient content was analyzed and trials were established in a randomized complete block design in three replications. Response of genotypes to major cassava pests and diseases, yield and carotenoids content was evaluated. It was observed that four genotypes did not show cassava mosaic disease (CMD) symptoms irrespective of the environments. The local check had highest CMD incidence and severity across all environments. Average number of whitefly per plant across all environments was highest on TMS 96/0023. Average cassava green mite (CGM) infestation was low on all the genotypes. Fresh root yield of five genotypes ranged between 25 and 30 tons per ha for both years. Significant and positive correlation was found across locations between fresh root yield and soil K, P and Mg. AMMI analysis revealed highly significant differences among genotypes and environments and significant genotype × environment interaction for most of the estimated traits, indicating variability in genotypes performance with environment.

Keywords

Cassava mosaic disease G × E interaction Mega-environment Root yield Soil nutrient Whitefly 

Notes

Acknowledgements

This work was supported by the Agricultural Investment and Market Development Project (AIMDP) jointly funded by the Cameroonian government and the World Bank, and CGIAR Research Program on Roots, Tubers and Bananas (RTB). The administrative and logistic support from IRAD Office is acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

Supplementary material

10681_2018_2234_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 18 kb)

References

  1. Akinbade S, Hanna R, Nguenkam A et al (2010) First report of the East African cassava mosaic virus-Uganda (EACMV-UG) infecting cassava (Manihot esculenta) in Cameroon. New Dis Rep 21:22CrossRefGoogle Scholar
  2. Akinwale MG, Akinyele BO, Odiyi AC, Dixon AGO (2011) Genotype × environment interaction and yield performance of 43 improved cassava (Manihot esculenta Crantz) genotypes at three agro-climatic zones in Nigeria. Br Biotechnol J 1:68–84CrossRefGoogle Scholar
  3. Benesi I, Labuschagne M, Dixon A, Mahungu N (2005) Genotype × enviroment interaction effects on native cassava starch quality and potential for starch use in the commercial sector. Afr Crop Sci J 12:205–216.  https://doi.org/10.4314/acsj.v12i3.27880 CrossRefGoogle Scholar
  4. Boateng SA, Boadi S (2010) Cassava yield response to sources and rates of potassium in the forest–savanna transition zone of Ghana. Afr J Root Tuber Crop 8:1–5Google Scholar
  5. Bradbury G, Potts B, Beadle C (2011) Genetic and environmental variation in wood properties of Acacia melanoxylon. Ann For Sci 68:1363–1373CrossRefGoogle Scholar
  6. Byju G, Nedunchezhiyan M, Ravindran CS et al (2012) Modeling the response of cassava to fertilizers: a site-specific nutrient management approach for greater tuberous root yield. Commun Soil Sci Plant Anal 43:1149–1162.  https://doi.org/10.1080/00103624.2012.662563 CrossRefGoogle Scholar
  7. Cach NT, Lenis JI, Perez JC et al (2006) Inheritance of useful traits in cassava grown in subhumid conditions. Plant Breed 125:177–182.  https://doi.org/10.1111/j.1439-0523.2006.01192.x CrossRefGoogle Scholar
  8. Callist Kundy A (2015) Effect of G × E interaction on yield and yield components of cassava genotype by environment interaction. LAP LAMBERT Academic Publishing, SaarbrückenGoogle Scholar
  9. Chikoti PC, Shanahan P, Melis R (2009) Evaluation of cassava genotypes for resistance to cassava mosaic disease and agronomic traits. Am J Plant Sci 7:1122–1128.  https://doi.org/10.4236/ajps.2016.77107 CrossRefGoogle Scholar
  10. Coe R (2012) Multi-environment trials: an overview. Stat Serv Centre, Univ Read UK World Agrofor Centre, Kenya 1–13Google Scholar
  11. Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85.  https://doi.org/10.1016/S0065-2113(08)60818-4 CrossRefGoogle Scholar
  12. Dixon AG, Nukenine EN (1998) Genotype × environment interaction and optimum resource allocation for yield and yield components of cassava. Afr Crop Sci J 8:1–10Google Scholar
  13. Dixon A, Ssemakula G (2008) Prospects for cassava breeding in Sub-Saharan Africa in the next decade. J Food Agric Environ 6(4):256–262Google Scholar
  14. Easwari ACS, Sheela MN (1998) Genetic analysis in a diallel cross of inbred lines of cassava. Madras Agric J 85:264–268Google Scholar
  15. Egesi CN, Onyeka TJ, Asiedu R (2009) Environmental stability of resistance to anthracnose and virus diseases of water yam (Dioscorea alata). Afr J Agric Res 4:113–118Google Scholar
  16. Ekanayake IJ, Ortiz R, of Tropical Agriculture II (2000) Genotype × environment interaction analysis of IITA mandate crops in Sub-Saharan Africa. International Institute of Tropical Agriculture, IbadanGoogle Scholar
  17. Fu J, Jiang D, Huang Y et al (2014) Evaluating the marginal land resources suitable for developing bioenergy in Asia. Adv Meteorol 2014:1–9.  https://doi.org/10.1155/2014/238945 CrossRefGoogle Scholar
  18. IITA (1990) Cassava in tropical Africa: a reference manual. IITA, IbadanGoogle Scholar
  19. Jalata Z (2011) GGE-biplot analysis of multi-environment yield trials of barley (Hordeum vulgare L.) genotypes in southeastern Ethiopia Highlands. Int J Plant Breed Genet 5:59–75.  https://doi.org/10.3923/ijpbg.2011.59.75 CrossRefGoogle Scholar
  20. Kota S, Singh S, Mohapatra T et al (2013) Genotype × environment interaction analysis for grain yield in new plant type (npt) wheat derivatives. SABRAO J Breed Genet 45:382–390Google Scholar
  21. Kulakow PA, Parkes EY, Friedrich SK et al (2015) Linearity, reproducibility and comparison of iCheckTM carotene with spectrophotometer and HPLC for evaluation of total carotenoids in cassava roots. Eur J Nutr Food Saf 136:57881.  https://doi.org/10.13140/2.1.4534.4007 CrossRefGoogle Scholar
  22. Manrique LA (1992) Growth and yield performance of cassava grown at three elevations in Hawaii. Commun Soil Sci Plant Anal 23:129–141.  https://doi.org/10.1080/00103629209368576 CrossRefGoogle Scholar
  23. Manrique K, Hermann M (1999) CIP program report effect of G × E interaction on root yield and beta- carotene content of selected sweetpotato (Ipomoea batatas (L) Lam.) varieties and breeding clones. CIP Program Report 281–287Google Scholar
  24. Maroya NG, Kulakow P, Dixon A, Maziya-Dixon B (2012) Genotype × environment interaction of mosaic disease, root yields and total carotene concentration of yellow-fleshed cassava in Nigeria. Int J Agron 2012:1–8.  https://doi.org/10.1155/2012/434675 CrossRefGoogle Scholar
  25. Mkumbira J, Mahungu NM, Gullberg U (2003) Grouping locations for efficient cassava evaluation in Malawi. Exp Agric 39:167–179.  https://doi.org/10.1017/S0014479702001199 CrossRefGoogle Scholar
  26. Mtunguja MK, Laswai HS, Kanju E et al (2016) Effect of genotype and genotype by environment interaction on total cyanide content, fresh root, and starch yield in farmer-preferred cassava landraces in Tanzania. Food Sci Nutr 4:791–801.  https://doi.org/10.1002/fsn3.345 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Nassir AL, Ariyo OJ (2011) Genotype × environment interaction and yield-stability analyses of rice grown in tropical inland swamp. Not Bot Hort Agrobot Cluj 39:220–225CrossRefGoogle Scholar
  28. Ngeve JM (1994) Yield stability parameters for comparing cassava varieties. Acta Hortic.  https://doi.org/10.17660/ActaHortic.1994.380.21 CrossRefGoogle Scholar
  29. Njoroge MK, Kilalo DC, Miano DW et al (2016) Whiteflies species distribution and abundance on cassava crop in different agro-ecological zones of Kenya. J Entomol Zool Stud 258:258–262Google Scholar
  30. Noerwijati K, Budiono R (2015) Yield and yield components evaluation of cassava (manihot esculenta crantz) clones in different altitudes. Energy Procedia 65:155–161.  https://doi.org/10.1016/j.egypro.2015.01.050 CrossRefGoogle Scholar
  31. Okao-Okuja G, Legg JP, Traore L, Alexandra Jorge M (2004) Viruses associated with cassava mosaic disease in Senegal and Guinea Conakry. J Phytopathol 152:69–76.  https://doi.org/10.1046/j.1439-0434.2003.00797.x CrossRefGoogle Scholar
  32. Otoo JA, Dixon AGO, Asiedu R et al (1994) Genotype × environment interaction studies with cassava. Acta Hortic.  https://doi.org/10.17660/ActaHortic.1994.380.22 CrossRefGoogle Scholar
  33. Pacheco Á, Vargas M, Alvarado G et al (2015) GEA-R (Genotype × environment analysis with R for Windows) Version 3.0—CIMMYT Research Software Dataverse—CIMMYT Dataverse NetworkGoogle Scholar
  34. Pariyo A, Baguma Y, Alicai T et al (2015) Stability of resistance to cassava brown streak disease in major agro-ecologies of Uganda. J Plant Breed Crop Sci 7:67–78.  https://doi.org/10.5897/JPBCS2013.0490 CrossRefGoogle Scholar
  35. SAS (2009) JMP® Version 8.0.2. SAS Institute, Cary, NCGoogle Scholar
  36. Ssemakula G, Dixon A (2007) Genotype × environment interaction, stability and agronomic performance of carotenoid-rich cassava clones. Sci Res Essay 2:390–399Google Scholar
  37. Tan SL, Mak C (1995) Genotype × environment influence on cassava performance. Field Crop Res.  https://doi.org/10.1016/0378-4290(95)00016-J CrossRefGoogle Scholar
  38. Temegne NC, Ajebesone FN, Fotso KA (2015) Influence de la composition chimique du sol sur la teneur en éléments nutritifs et le rendement du manioc (Manihot esculenta Crantz, Euphorbiaceae) dans deux zones agro-écologiques du Cameroun. Int J Biol Chem Sci 9:2776–2788.  https://doi.org/10.4314/ijbcs.v9i6.21 CrossRefGoogle Scholar
  39. Teye E, Asare AP, Amoah RS, Tetteh JP (2011) Determination of the dry matter content of cassava (Manihot esculenta Crantz) tubers using specific gravity method. ARPN J Agric Biol Sci 6:23–28Google Scholar
  40. Thresh JM, Cooter RJ (2005) Strategies for controlling cassava mosaic virus disease in Africa. Plant Pathol 54:587–614.  https://doi.org/10.1111/j.1365-3059.2005.01282.x CrossRefGoogle Scholar
  41. van Mölken T, Stuefer JF (2011) The potential of plant viruses to promote genotypic diversity via genotype × environment interactions. Ann Bot 107:1391–1397.  https://doi.org/10.1093/aob/mcr078 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton.  https://doi.org/10.1201/9781420040371 CrossRefGoogle Scholar
  43. Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645.  https://doi.org/10.4141/P05-169 CrossRefGoogle Scholar
  44. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE Biplot. Crop Sci 40:597.  https://doi.org/10.2135/cropsci2000.403597x CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Apollin Kuate Fotso
    • 1
  • Rachid Hanna
    • 1
  • Peter Kulakow
    • 2
  • Elisabeth Parkes
    • 2
  • Peter Iluebbey
    • 2
  • Francis Ajebesone Ngome
    • 3
  • Christopher Suh
    • 3
  • Jacques Massussi
    • 3
  • Ibrahim Choutnji
    • 3
  • Venasius Lendzemo Wirnkar
    • 3
  1. 1.International Institute of Tropical Agriculture (IITA)-CameroonMessa-YaoundéCameroon
  2. 2.International Institute of Tropical Agriculture (IITA)-IbadanIbadanNigeria
  3. 3.Institute of Agricultural Research for Development (IRAD)-CameroonYaoundéCameroon

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