Cereal Research Communications

, Volume 36, Issue 1, pp 167–176 | Cite as

Effect of Various Crop Production Factors on the Yield and Yield Stability of Maize in a Long-Term Experiment

  • Z. BerzsenyiEmail author
  • Q. L. Dang
Open Access


In a long-term experiment set up in Martonvásár (N 47°21′, E 18°49′), Hungary in 1960 on a humous loam soil of the chernozem type, the effect of five crop production factors in increasing maize yields was studied in seven treatments. The factors studied were soil cultivation, fertilisation, plant density, variety and weed control. All the factors had a favourable and an unfavourable level. Yield data recorded over 42 years were evaluated using analysis of variance and stability analysis. The highest yield (8.59 t ha −1) was obtained when all the production factors were favourable and lowest (2.09 t ha −1) when these factors were unfavourable. When only one factor was unfavourable and all the other factors were favourable the following yields were obtained (t ha −1): soil tillage: 8.32, fertilisation: 5.21, genotype: 4.98, plant density: 6.31 weed control: 7.01. The crop production factors contributed to the increase in maize yield in the following ratios (%): fertilisation 30.6, variety 32.6, plant density 20.2, weed control 14.2, soil cultivation 2.4. The highest value of the coefficient of variation (CV%) was obtained when all the production factors were at the unfavourable level (45.7%) and when weed control or fertilisation were unfavourable (36.6% and 34.8%, respectively), while the lowest value was recorded when all the factors were favourable (19.5%). The significant treatment × year interaction could be attributed principally to treatments in which weed control, fertilisation, genotype or all the factors were unfavourable. The regression coefficient of linear regression analysis provided a satisfactory characterisation of the stability of the treatments in different environments, while the distance between the straight lines expressed the yield differences between the treatment pairs. The AMMI (Additive Main Effect and Multiplicative Interaction) model proved to be a valuable approach for understanding agronomic treatment × environment interactions and assessing the mean performance and yield stability of treatments.


long-term experiment maize production factors stability analysis AMMI model 


  1. Berzsenyi, Z., Győrffy, B. 1995. Különböző növénytermesztési tényezők hatása a kukorica Termésére és termésstabilitására (Effect of various crop production factors on the yield and yield stability of maize). Növénytermelés 44:507–517.Google Scholar
  2. Crossa, J. 1990. Statistical analyses of multilocation trials. Advances in Agronomy 44:55–85.CrossRefGoogle Scholar
  3. Duvick, D.N., Smith, J.S.C., Cooper, M. 2004. Changes in Performance, Parentage, and Genetic Diversity of Successful Corn Hybrids, 1930–2000. In: Smith, C.W. (ed.), Corn: Origin, History, Technology, and Production. John Wiley & Sons Inc., pp. 65–97.Google Scholar
  4. Finlay, K.W., Wilkinson, G.N. 1963. The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res. 14:742–754.CrossRefGoogle Scholar
  5. Győrffy, B. 1969. Különböző növénytermesztési tényezők hatása a kukorica termésére, Komplex I (Effect of crop production factors on maize yields, Experiment code: Komplex I). In: I’só, I. (ed.), Kukoricatermesztési kísérletek 1965–1968 (Maize Production Experiments 1965–1968). Akadémiai Kiadó, Budapest, pp. 54–60.Google Scholar
  6. Kang, M.S. 1995. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agronomy Journal 85:754–757.CrossRefGoogle Scholar
  7. Piepho, H.P. 1998. Methods for comparing the yield stability of cropping systems — A review. J. Agronomy and Crop Science 180:193–213.CrossRefGoogle Scholar
  8. Sváb, J. 1981. Biometriai módszerek a kutatásban (Biometrical Methods in Research). Mezőgazdasági Kiadó, Budapest, pp. 230–240.Google Scholar
  9. Tollenaar, M., Lee, E.A. 2002. Yield potential, yield stability and stress tolerance in maize. Field Crops Research 75:161–169.CrossRefGoogle Scholar
  10. Yan, W., Kang, M.S., Ma, B., Woods, S., Cornelius, P.L. 2007. GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data. Crop Science 47:643–655.CrossRefGoogle Scholar

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© Akadémiai Kiadó, Budapest 2008

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Agricultural Research Institute of the Hungarian Academy of SciencesMartonvásárHungary

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