Skip to main content
Log in

Stability Analysis of Tuber Yield and Starch Yield in Mid-Late and Late Maturing Starch Cultivars of Potato (Solanum tuberosum)

  • Published:
Potato Research Aims and scope Submit manuscript

Abstract

Potato is one the of world’s most important crops. It is grown primarily for human consumption but is also used to feed cattle and to produce alcohol and starch. In modern industry, potatoes are a major source of starch. Unpredictable growing conditions strongly influence the cultivation of starch potatoes. One of the best methods for dealing with unpredictable stresses, including those caused by climate change, is cultivation of stable and high yielding cultivars. In the present work, tuber yield and starch yield from a Polish post-registration series of field trials conducted in the years 2013–2016 were analyzed using three different linear mixed models, which can be associated with three different stability measures. It is shown that cultivar Pokusa was the highest yielding and the most stable cultivar in terms of tuber yield, while cultivar Kuras was the most stable and the highest starch yielding cultivar. Moreover, using mixed model factorial regression, it is shown that temperatures in June and August and total precipitation in August had significant influence on tuber yield in the analyzed series of field trials.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23

    Article  Google Scholar 

  • Caliński T, Czajka S, Kaczmarek Z, Krajewski P, Siatkowski I (1998) Podręcznik użytkownika programu Sergen 3. Instytut Genetyki Roślin PAN w Poznaniu. (in Polish)

  • Caliński T, Czajka S, Kaczmarek Z, Krajewski P, Pilarczyk W (2005) Analyzing multi-environment variety trials using randomization derived mixed models. Biometrics 61:448–455

    Article  PubMed  Google Scholar 

  • Caliński T, Czajka S, Kaczmarek Z, Krajewski P, Pilarczyk W (2009a) Analyzing the genotype by-environment interactions under a randomization derived mixed models. J Agr Biol Environ Stat 14:224–241

    Article  Google Scholar 

  • Caliński T, Czajka S, Kaczmarek Z, Krajewski P, Pilarczyk W (2009b) A mixed model analysis of variance for multi environment variety trials. Stat Pap 50:735–759

    Article  Google Scholar 

  • Caliński T, Czajka S, Kaczmarek Z, Krajewski P, Pilarczyk W, Siatkowski M (2017) On mixed model analysis of multi-environment variety trials: a reconsideration of the one-stage and the two-stage models and analyses. Stat Pap 58:433–465

    Article  Google Scholar 

  • Campbell BT, Baezinger PS, Eskridge KM, Budak H, Streck NA, Weiss A, Gill KS, Erayman M (2004) Using environmental covariates to explain genotype x environment and QTL x environment interactions for agronomic traits on chromosome 3A of wheat. Crop Sci 44:620–627

    Article  Google Scholar 

  • Central Statistical Office (2016) Statistical yearbook of agriculture. Warszawa

  • Cukier, Skrobia, Biopaliwa (2017) Statistical yearbook. Bartens, Słubice

  • Damesa TM, Möhring J, Worku M, Piepho HP (2017) One step at a time: stage-wise analysis of a series of experiments. Agron J 109:845–857

    Article  Google Scholar 

  • Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40

    Article  Google Scholar 

  • Eskridge KM, Mumm RF (1992) Choosing plant cultivars based on the probability of outperforming a check. Theor Appl Genet 84:894–900

    Google Scholar 

  • Eskrigde KM (1990) Selection of stable cultivars using a safety-first rule. Crop Sci 30:369–374

    Article  Google Scholar 

  • Flis B, Domański L, Zimoch-Guzowska E, Polgar Z, Pousa SÁ, Pawlak A (2014) Stability analysis of agronomic traits in potato cultivars of different origin. Am J Potato Res 91:404–413

    Article  CAS  Google Scholar 

  • Forkman J, Piepho HP (2014) Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models. Biometrics 70:639–647

    Article  PubMed  Google Scholar 

  • Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44:705–715

    Article  Google Scholar 

  • Gauch HG (1992) Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier, New York

    Google Scholar 

  • Gauch HG, Zobel RW (1990) Imputing missing yield trial data. Theor Appl Genet 79:753–761

    Article  PubMed  Google Scholar 

  • Grabowska K, Dymerska A, Pożarska K, Grabowski J (2016) Predicting of blue lupine yields based on selected climate chance scenarios. Acta Agrophysica 23:363–380

    Google Scholar 

  • Hu X, Yan S, Shen K (2013) Heterogeneity of error variance and its influence on genotype comparison in multi-location trails. Field Crops Res 149:322–328

    Article  Google Scholar 

  • Hu X, Yan S, Li S (2014) The influence of error variance variation on analysis of genotype stability in multi-environment trails. Field Crops Res 156:84–90

    Article  Google Scholar 

  • Jankowska J, Pietraszko M, Lutomirska B (2015) The analysis of yielding stability of some potato (Solanum tuberosum L.) cultivars on light soils. Fragm Agron 32:32–43 (in Polish)

    Google Scholar 

  • Kalbarczyk R (2004) The relation between agrometeorological factors and potato crop yields in different regions of Poland. Acta Agrophysica 4:339–350 (in Polish)

    Google Scholar 

  • Kang MS (1988) A rank sum method for selecting high-yielding, stable corn genotypes. Cereal Res Commun 16:113–115

    Google Scholar 

  • Kenward MG, Roger JH (1997) Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 58:545–554

    Google Scholar 

  • Macholdt J, Piepho HP, Honermeier B (2019) Long-term impact of sub-optimal and optimal nutrient supply on grain yield and yield stability of winter wheat. Eur J Agron 102:14–22

    Article  Google Scholar 

  • Mądry W, Iwańska M (2011) Usefulness of statistical methods and measures for evaluating cultivar stability and adaptation: an overview of research. Biuletyn IHAR 260(261):193–218 (in Polish)

    Google Scholar 

  • Mądry W, Kang MS (2005) Scheffe-Caliński and Shukla models: their interpretation and usefulness in stability and adaptation analyses. J Crop Improv 14(1/2):325–369

    Article  Google Scholar 

  • Malik W, Hadasch S, Forkman J, Piepho HP (2018) Nonparametric resampling methods for testing multiplicative terms in AMMI and GGE models for multienvironment trials. Crop Sci 58:752–761

    Article  Google Scholar 

  • Mohammadi R, Amri A (2013) Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192:227–249

    Article  CAS  Google Scholar 

  • Moore KJ, Dixon PM (2015) Analysis of combined experiments revisited. Agron J 107:763–771

    Article  Google Scholar 

  • Nowosad K, Liersch A, Popławska W, Bocianowski J (2016) Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208:187–194

    Article  Google Scholar 

  • Oyekunle M, Menkir A, Mani H, Olaoye G, Usman IS, Ado SG, Abdullahi US, Ahmed HO, Hassan LB, Abdulmalik RO, Abubakar H (2017) Stability analysis of maize cultivars adapted to tropical environments using AMMI analysis. Cereal Res Commun 45:336–345

    Article  Google Scholar 

  • Paderewski J, Rodrigues PC (2014) The usefulness of EM-AMMI to study the influence of missing data pattern and application to Polish post-registration winter wheat data. Austral J Crop Sci 8:640–645

    Google Scholar 

  • Paget MF, Alspach PA, Anderson JAD, Genet RA, Apiolaza LA (2015) Trial heterogeneity and variance models in the genetic evaluation of potato tuber yield. Plant Breed 134:203–211

    Article  Google Scholar 

  • Piepho HP (1998a) Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures. Theoretical and Applied Genetics 97 (1-2):195–201

    Article  Google Scholar 

  • Piepho HP (1998b) Methods for comparing the yield stability of cropping systems. J Agron Crop Sci 180:193–213

    Article  Google Scholar 

  • Piepho HP (1999) Stability analysis using the SAS system. Agron J 91:154–160

    Article  Google Scholar 

  • Piepho HP (2000) Exact confidence limits for covariate-dependent risk in cultivar trials. J Agric Biol Environ Stat 5:202–213

    Article  Google Scholar 

  • Piepho HP, van Eeuwijk FA (2002) Stability analysis in crop performance evaluation. In: Kang M (ed) Crop improvements: challenges in the twenty-first century. pp 315–351

  • Piepho HP, Denis JB, van Eeuwijk FA (1998) Predicting cultivar differences using covariates. J Agric Biol Environ Stat 3:151–162

    Article  Google Scholar 

  • Piepho HP, Möhring J, Schulz-Streeck T, Ogutu JO (2012) A stage-wise approach for analysis of multi-environment trials. Biom J 54:844–860

    Article  PubMed  Google Scholar 

  • Przystalski M, Osman A, Thiemt EM, Rolland B, Ericson L, Østergård H, Levy L, Wolfe M, Büchse A, Piepho HP, Krajewski P (2008) Comparing the performance of cereal varieties in organic and non-organic cropping systems in different European countries. Euphytica 163:417–433

    Article  Google Scholar 

  • Purchase JL, Hatting H, van Deventer CS (2000) Genotype x environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. S Afr J Plant Soil 17:101–107

    Article  Google Scholar 

  • Reynolds MP, Trethowan R, Crossa J, Vargas M, Sayre KD (2002) Physiological factors associated with genotype x environment interaction in wheat. Field Crops Res 75:139–160

    Article  Google Scholar 

  • Rykaczewska K (2013) The impact of high temperatures during growing season of potato cultivars with different responses to environmental stresses. Am J Plant Sci 4:2386–2393

    Article  Google Scholar 

  • Rykaczewska K (2015) The effect of high temperature occurring in subsequent stages of plant development on potato yield and tuber physiological defects. Am J Potato Res 92:339–349

    Article  CAS  Google Scholar 

  • Rymuza K, Radzka E, Lenartowicz T (2015) Effect of weather conditions on early potato yields in east-central Poland. Commun Biom Crop Sci 10:65–72

    Google Scholar 

  • Searle SR, Casella G, McCulloch CE (2006) Variance components, Second edn. Wiley, Hoboken

    Google Scholar 

  • Sekutowski T, Badowski T (2010) Effect of weed infestation, meteorological conditions and herbicidal protection on potato tuber yield and fractions. Prog Plant Prot 50:1390–1394 (in Polish)

    Google Scholar 

  • Smith AB, Cullis BR (2018) Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data. Euphytica 214:article 143

    Article  Google Scholar 

  • Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: and overview of current mixed model approaches. J Agric Sci 143:449–462

    Article  Google Scholar 

  • Strobel W, Pszczółkowski P (2007) Effect of pod moisture content and weather conditions on pod dehiscing and seed shedding in narrow-leafed lupin. Zesz Probl Postep Nauk Rol 522:317–323 (in Polish)

    Google Scholar 

  • Studnicki M, Paderewski J, Piepho HP, Wójcik-Gront E (2017) Prediction accuracy and consistency in cultivar ranking for factor-analytic linear mixed models for winter wheat multienvironmental trials. Crop Sci 57:2506–2516

    Article  Google Scholar 

  • Tatarowska B, Flis B, Zimnoch-Guzowska E (2012) Biological stability of resistance to Phytophthora infestans (Mont.) de Bary in 22 Polish potato cultivars evaluation in field experiments. Am J Potato Res 89:73–81

    Article  Google Scholar 

  • van Eeuwijk FA (1995) Linear and bilinear models for the analysis of multi-environmental trials: I. An inventory model. Euphytica 84:1–8

    Article  Google Scholar 

  • van Eeuwijk FA, Keizer LCP, Bakker JJ (1995) Linear and bilinear models for the analysis of multi-environmental trials: II. An application to data from the Dutch maize variety trials. Euphytica 84:9–22

    Article  Google Scholar 

  • Vargas M, Crossa J, Sayre K, Reynolds M, Ramírez ME, Talbot M (1998) Interpreting genotype x environment interaction in wheat by partial least squares regression. Crop Sci 38:679–689

    Article  Google Scholar 

  • Vargas M, Crossa J, van Eeuwijk F, Ramírez M, Sayre K (1999) Using partial least regression, factorial regression, and AMMI models for interpreting genotype x environment interaction. Crop Sci 39:955–967

    Article  Google Scholar 

  • Vargas M, Crossa J, van Eeuwijk F, Sayre KD, Reynolds MP (2001) Interpreting treatment x environment in agronomy trials. Agrom J 93:949–960

    Google Scholar 

  • Vargas M, van Eeuwijk FA, Crossa J, Ribaut JM (2006) Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet 112:1009–1023

    Article  CAS  PubMed  Google Scholar 

  • Voltas J, van Eeuwijk FA, Sombrero A, Lafarga A, Igartua E, Romagosa I (1999) Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley. I. Individual grain weight. Field Crops Res 62:63–74

    Article  Google Scholar 

  • Voltas J, López CH, Borrás G (2005) Using biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. Eur J Agron 22:309–324

    Article  Google Scholar 

  • Ziernicka A (2004) Global warming and effectiveness of precipitation. Acta Agrophysica 3:393–397

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the reviewers for their helpful comments and corrections, which led to the improvement of the paper.

Author information

Authors and Affiliations

Authors

Contributions

TL proposed the methodology and designed the experiments. HPP and MP proposed the statistical methodology. MP performed the statistical computations. All authors equally participated in writing the draft of the paper.

Corresponding author

Correspondence to Tomasz Lenartowicz.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

ESM 1

(DOCX 16 kb)

ESM 2

(DOCX 24 kb)

ESM 3

(DOCX 17 kb)

ESM 4

(DOCX 19 kb)

ESM 5

(DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lenartowicz, T., Piepho, HP. & Przystalski, M. Stability Analysis of Tuber Yield and Starch Yield in Mid-Late and Late Maturing Starch Cultivars of Potato (Solanum tuberosum). Potato Res. 63, 179–197 (2020). https://doi.org/10.1007/s11540-019-09434-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11540-019-09434-z

Keywords

Navigation