Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-19T14:46:15.275Z Has data issue: false hasContentIssue false
  • English
  • Français

Genotypic and environmental variability of yield from seven different crops in Croatian official variety trials and comparison with on-farm trends

Published online by Cambridge University Press:  10 November 2016

M. ZORIĆ ,
J. GUNJAČA Open the ORCID record for J. GUNJAČA [Opens in a new window]  and
D. ŠIMIĆ
M. ZORIĆ
Affiliation:
Croatian Centre for Agriculture, Food and Rural Affairs, Institute for Seeds and Seedlings, Usorska 19, Osijek-Brijest, Croatia
J. GUNJAČA*
Affiliation:
University of Zagreb, Faculty of Agriculture, Svetošimunska 25, Zagreb, Croatia Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 25, 10000 Zagreb, Croatia
D. ŠIMIĆ
Affiliation:
Centre of Excellence for Biodiversity and Molecular Plant Breeding, Svetošimunska 25, 10000 Zagreb, Croatia Agricultural Institute Osijek, Južno predgradje 17, Osijek, Croatia
*
*To whom all correspondence should be addressed. Email: jgunjaca@agr.hr
Get access Rights & Permissions [Opens in a new window]

Summary

Assessment of the value for cultivation and use (VCU) of a new cultivar, essential for its official registration, is done through a series of trials carried out over a 2–3-year period and across many locations. In a set of multi-environment VCU trials, evaluation of new genotypes can be a laborious task due to the presence of genotype by environment interactions, which can hide their true genetic value. In an attempt to reveal the true genetic value of new cultivars, a good starting point is investigation of the importance of various genetic and environmental sources of variation, which can be done by estimating relative magnitude of corresponding variance components within the mixed model framework.

Genotype × location × year (G × L × Y) data set for seven crops taken from the 10-year period 2001–10 was used in the present study to estimate the variance components for main effects and their interactions in Croatian VCU trials. Depending on the crop, the most important and least important components were Y or LY, and L or GL, respectively. Genotypic effect was relatively small, ranging from 2·1 to 13·4% of the total variation. The current results are comparable with the relative sizes of the variance components obtained in studies from four- to sixfold larger countries, indicating that the environments within Croatia, if sufficiently widely sampled, can provide as extreme cultivar responses as a geographically more dispersed set of VCU trials. The gap range in different crops is much wider (30–60%) than in Western Europe (up to 30%), but it remained constant over the 10-year period.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 155 , Issue 5 , July 2017 , pp. 804 - 811
Check for updates
Copyright
Copyright © Cambridge University Press 2016 

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

Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., Dai, B., Grothendieck, G. & Green, P. (2012). lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. Vienna, Austria: The R Foundation. Available from: http://CRAN.R-project.org/package=lme4 (verified 31 August 2016).Google Scholar
Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F. X. & Huard, F. (2010). Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119, 201–212.Google Scholar
Croatian Bureau of Statistics (2016). PC-Axis Databases. Agriculture, Hunting, Forestry and Fishing. Zagreb: Croatian Bureau of Statistics. Available from: http://www.dzs.hr/default_e.htm (verified 31 August 2016).Google Scholar
Fischer, R. A. & Edmeades, G. O. (2010). Breeding and cereal yield progress. Crop Science 50 (Suppl. 1), S85–S98.Google Scholar
Fischer, R. A., Byerlee, D. & Edmeades, G. O. (2014). Crop Yields and Global Food Security: will Yield Increase Continue to Feed the World? ACIAR Monograph No. 158. Canberra, Australia: Australian Centre for International Agricultural Research. Available from: http://aciar.gov.au/publication/mn158 (verified 31 August 2016).Google Scholar
Hristov, N., Mladenov, N., Djurić, V., Kondić-Špika, A., Marjanović-Jeromela, A. & Šimić, D. (2010). Genotype by environment interactions in wheat quality breeding programs in south east Europe. Euphytica 174, 315–324.CrossRefGoogle Scholar
Laidig, F., Drobek, T. & Meyer, U. (2008). Genotypic and environmental variability of yield for cultivars from 30 different crops in German official variety trials. Plant Breeding 127, 541–547.Google Scholar
Laidig, F., Piepho, H. P., Drobek, T. & Meyer, U. (2014). Genetic and non-genetic long-term trends of 12 different crops in German official variety performance trials and on-farm yield trends. Theoretical & Applied Genetics 127, 2599–2617.CrossRefGoogle ScholarPubMed
Lobell, D. B., Cassman, K. G. & Field, C. B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment & Resources 34, 179–204.Google Scholar
Mackay, I., Horwell, A., Garner, J., White, J., Mckee, J. & Philpott, H. (2011). Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time. Theoretical & Applied Genetics 122, 225–238.CrossRefGoogle ScholarPubMed
Meyer, U., Laidig, F. & Drobek, T. (2011). Optimization of number of trials in official VCU trial series of Germany. Biuletyn Oceny Odmian 33, 73–82.Google Scholar
Moro, J., Alonso, R. & Rodriguez, A. (1989). Variety trials in Spain. Biuletyn Oceny Odmian 21–22, 88–104.Google Scholar
Patterson, H. D. (1997). Analysis of series of variety trials. In Statistical Methods for Plant Variety Evaluation (Eds Kempton, R. A., Fox, P. N. & Cerezo, M.), pp. 139–161. London, UK: Chapman and Hall.Google Scholar
Peltonen-Sainio, P., Jauhiainen, L. & Laurila, I. P. (2009). Cereal yield trends in northern European conditions: changes in yield potential and its realisation. Field Crops Research 110, 85–90.Google Scholar
Piepho, H. P., Mohring, J., Melchinger, A. E. & Buchse, A. (2008). BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161, 209–228.Google Scholar
R Core Team (2015). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available from: https://www.R-project.org/ (verified 31 August 2016).Google Scholar
Smith, A., Cullis, B. & Gilmour, A. (2001). The analysis of crop variety evaluation data in Australia. Australian & New Zealand Journal of Statistics 43, 129–145.CrossRefGoogle Scholar
Smith, A. B., Cullis, B. R. & Thompson, R. (2005). Analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. Journal of Agricultural Science, Cambridge 143, 449–462.Google Scholar
Sudarić, A., Šimić, D. & Vratarić, M. (2006). Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe. Plant Breeding 125, 191–194.Google Scholar
Talbot, M. (1984). Yield variability of crop varieties in the UK. Journal of Agricultural Science, Cambridge 102, 315–321.Google Scholar
Talbot, M. (1993). Variety yield stability. Aspects of Applied Biology 34, 37–46.Google Scholar
Utz, H. F. (1995). PLABSTAT Version M. Ein Computer programm zur statistischen Analyse von pflanzenzüchterischen Experimenten. Stuttgart, Germany: Selbstverlag Universität Hohenheim.Google Scholar
Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance – a review. Field Crops Research 143, 4–17.Google Scholar
Yan, W. & Rajcan, I. (2003). Prediction of cultivar performance based on single-versus multiple-year tests in soybean. Crop Science 43, 549–555.Google Scholar