Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-29T01:01:53.483Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimum replications and locations for cotton cultivar trials under Mediterranean conditions

Published online by Cambridge University Press:  16 November 2017

D. BAXEVANOS ,
J. T. TSIALTAS ,
D. VLACHOSTERGIOS  and
C. GOULAS
D. BAXEVANOS*
Affiliation:
Hellenic Agricultural Organization-‘Demeter’, Institute of Industrial and Fodder Plants, 413 35 Larissa, Greece
J. T. TSIALTAS
Affiliation:
Faculty of Agriculture, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
D. VLACHOSTERGIOS
Affiliation:
Hellenic Agricultural Organization-‘Demeter’, Institute of Industrial and Fodder Plants, 413 35 Larissa, Greece
C. GOULAS
Affiliation:
Faculty of Forest Genetics and Breeding, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The number of replications and locations used in a cultivar evaluation scheme is an important factor affecting the trial heritability (H) and optimum resource allocation. The aim of the present study was to calculate the required number of replications and locations for realizing an optimum H of 0·75 and to identify the most effective test locations for cotton (Gossypium hirsutum L.) in Greece. The data for lint yield, plant height, verticillium wilt (Verticillium dahliae Kleb.) and fibre quality were derived from an 8-year experiment (2000–2007) conducted under irrigated, Mediterranean conditions at 14 locations along the Greek mega-environment. Analysis of variance was performed to calculate H as well as genotype plus genotype × environment (GGE) biplot analysis to determine the location's desirability. It was determined that the four replications currently used in the evaluation of lint yield were sufficient, whereas four locations were proposed as optimum in lieu of the current 8–14 locations used in the evaluation. Two locations excelled as the most effective for lint yield evaluation and one for selection of genotypes tolerant to verticillium wilt using as criteria: the high and consistent across years H (0·75), GGE biplot representativeness and discriminating ability. Moreover, one location was selected as a backup based on average trial failures. Plant height was sufficiently evaluated by four replications and two locations, while verticillium required four replications for realizing lower H (0·60) and three locations for even lower H (0·40). Regarding quality, an increase of replications from the two currently used to four was sufficient for evaluation of all the traits. The advantage of reducing the number of locations for evaluation of lint yield to just four casts no doubt on the evaluation precision of lint percentage, length, strength and elongation but does for micronaire, short fibre index and uniformity, which realized lower H (0·60 or 0·50).

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 155 , Issue 10 , December 2017 , pp. 1553 - 1564
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Annicchiarico, P. (2002). Genotype × Environment Interactions: Challenges and Opportunities for Plant Breeding and Cultivar Recommendations. Plant Production and Protection Paper 174. Rome, Italy: FAO.Google Scholar
Baxevanos, D., Goulas, C., Tzortzios, S. & Mavromatis, A. (2006). Grouping Greek cotton (Gossypium hirsutum L.) trial locations into mega-environments. Agricultural Research 29, 48–58. (In Greek with English Summary).Google Scholar
Baxevanos, D., Goulas, C., Rossi, J. & Braojos, E. (2008). Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE biplots. Agronomy Journal 100, 1230–1236.Google Scholar
Blanche, S. B. & Myers, G. O. (2006). Identifying discriminating locations for cultivar selection in Louisiana. Crop Science 46, 946–949.Google Scholar
Bowman, D. T., Bourland, F. & Kuraparthy, V. (2016). Measuring maturity in cotton cultivar trials. Journal of Cotton Science 20, 40–45.Google Scholar
Brancourt-Hulmel, M., Lecomte, C. & Meynard, J.-M. (1999). A diagnosis of yield-limiting factors on probe genotypes for characterizing environments in winter wheat trials. Crop Science 39, 1798–1808.CrossRefGoogle Scholar
Cooper, M., Messina, C. D., Podlich, D., Radu Totir, L., Baumgarten, A., Hausmann, N. J., Wright, D. & Graham, G. (2014). Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop & Pasture Science 65, 311–336.Google Scholar
Cotton And Products Annual (2014). Greece Cotton and Products Annual 2014. Gain Report Number IT1425. Washington, DC: USDA Foreign Agricultural Service. Available from: http://gain.fas.usda.gov/Recent%20GAIN%20Publications/Cotton%20and%20Products%20Annual_Rome_Greece_4-1-2014.pdf (verified 29 September 2017).Google Scholar
DeLacy, I. H., Basford, K. E., Cooper, M., Bull, J. K. & McLaren, C. G. (1996). Analysis of multi-environment trials – an historical perspective. In: Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L), pp. 39–124. Wallingford, UK: CAB International.Google Scholar
Frutos, E., Galindo, M. P. & Leiva, V. (2014). An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment 28, 1629–1641.Google Scholar
Garber, R. H., Devay, J. E., Goodell, P. B. & Roberts, P. A. (1996). Cotton diseases and nematodes. In Cotton Production Manual (Eds Hake, S. Johnson, Kerby, T. A. & Hake, K. D.), pp. 150–174. Publication 3352. Oakland, California: University of California.Google Scholar
Green, C. C. & Culp, T. W. (1990). Simultaneous improvements of yield, fiber quality, and yarn strength in upland cotton. Crop Science 30, 66–69.Google Scholar
Hanson, W. D. & Brim, C. A. (1963). Optimal allocation of test material for two-stage testing with an application to evaluation of soybean lines. Crop Science 3, 43–49.CrossRefGoogle Scholar
Kalivas, D. & Kollias, V. (2001). Effects of soil, climate and cultivation techniques on cotton yield in central Greece, using different statistical methods. Agronomie 21, 73–89.Google Scholar
Kechagia, U. E. & Xanthopoulos, F. P. (1998). Degrees of verticillium wilt infection and the relative damage in fiber quality parameters. In New Frontiers in Cotton Research, Proceedings of the 2nd World Cotton Research Conference, September 6–12, 1998 (Eds ICAC), p. 336. Athens, Greece: ICAC.Google Scholar
Lancashire, P. D., Bleiholder, H., Van Den Boom, T., Langelüddecke, P., Stauss, R., Weber, E. & Witzenberger, A. (1991). A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119, 561–601.Google Scholar
May, O. L. & Jividen, G. M. (1999). Genetic modification of cotton fiber properties as measured by single- and high-volume instruments. Crop Science 39, 328–333.CrossRefGoogle Scholar
Meredith, W. R. Jr (1984). Quantitative genetics. In Cotton (Eds Kohel, R. J. & Lewis, C. F.), pp. 131–150. Agronomy 24. Madison, Wisconsin: ASA, CSSA, SSSA.Google Scholar
Meredith, W. R. Jr (2003). Thirty-six years of regional high quality variety tests. In Proceedings of the Beltwide Cotton Conferences (Eds National Cotton Council of America), pp. 2561–2566. Memphis, TN: National Cotton Council of America.Google Scholar
SAS Institute (2002). JMP Statistical Discovery Software. Cary, NC: SAS Institute.Google Scholar
Sprague, G. F. & Federer, W. T. (1951). A comparison of variance components in corn yield trials. II. Error, year × variety, location × variety and variety components. Agronomy Journal 43, 535–541.CrossRefGoogle Scholar
Tsaliki, E. (2005). Cotton Situation in Greece. Thessaloniki, Greece: NAGREF – Cotton and Industrial Plants Institute of Greece.Google Scholar
Xu, N.-Y., Jin, S.-Q. & Li, J. (2016). Design of test location number and replicate frequency in regional cotton variety trials in China. Acta Agronomica Sinica 42, 47–54.Google Scholar
Yan, W. & KanG, M. S. (2003). GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. Boca Raton, FL: CRC Press.Google Scholar
Yan, W., Fregeau-Reid, J., Martin, R., Pageau, D. & Mitchell-Fetch, J. (2015). How many test locations and replications are needed in crop variety trials for a target region? Euphytica 202, 361–372.Google Scholar