Variability of the constituent elements of the productivity of maize hybrids of different ripeness groups under irrigation conditions
DOI:
https://doi.org/10.21498/2518-1017.15.3.2019.181093Keywords:
grain yield, number of grain rows, number of grains in a row, grain weight per ear, weight of 1000 grainsAbstract
Purpose. To determine the correlation dependences of the constituent elements of the productivity of maize hybrids of different ripeness groups with grain productivity under irrigation conditions in the Southern Steppe of Ukraine.
Methods. Field, laboratory, mathematical and statistical.
Results. The article presents the results of studies to determine the correlation dependencies between the biometric features of the corn cob, in order to assess plant productivity. According to the number of rows per ear, hybrids of late ripening groups stood out – 17.7 pcs., medium late forms were statistically close to this – 16.8 pcs. The largest number of grains in a row was formed by hybrids of the middle-late group (FAO 400–490) 48.8 pcs. The highest grain weight from the cob wasfound in hybrids of the middle-late group – 312.5 g. It was shown that the number of rows has significant correlation with the diameter of the core and cob. The number of rows had a stable low directed effect on productivity. A significant correlation is fixed between the number of grains in a row and such signs as the length of the core and the length of the grained cob. Connection was high in all FAO groups. The mass of grains per cob is the main component of the structure of the corn crop. A close correlation of the mass of grains per cob was observed with the following signs: grain productivity, length of the core, length of the cob with grains, diameter of the cob, weight of 1000 seeds, grain yield.
Conclusions. Under irrigation conditions the genotypic variability of the constituent elements of the maize hybrids productivity was revealed, which allows predicting the conduct of effective screening on specific characteristics according to ripeness groups.The revealed correlation dependences between quantitative signs of the cob structure and grain yield will allow to make a preliminary assessment of potential yield by factorial characteristics adapted to the conditions of irrigation of corn hybrids with FAO 180–600.
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References
Dziubetskyi, B. V., & Abelmasov, O. V. (2018). Characterization of testocrosses of early maturing corn lines of plasma of Aioident in the conditions of the northern zone of the Steppe of Ukraine. Zernovì kulʹturi [Grain Crops], 2(1), 5–13. doi: 10.31867/2523-4544/0001. [in Ukrainian]
Beckett, T. J., Rocheford, T. R., & Mohammadi, M. (2019). Reimagining Maize Inbred Potential: Identifying Breeding Crosses Using Genetic Variance of Simulated Progeny. Crop Sci., 59(4), 1457–1468. doi: 10.2135/cropsci2018.08.0508
Lavrynenko, Yu. O., Marchenko, T. Yu., Nuzhna, M. V., & Bodenko, N. A. (2018). Models of corn hybrids of different maturity groups FAO 150–490 for irrigated conditions. Plant Var. Stud. Prot., 14(1), 58–64. doi: 10.21498/2518-1017.14.1.2018.126508. [in Ukrainian]
Prysiazhniuk, L. M., Shovhun, O. O., Korol, L. V., & Korovko, I. I. (2016). Assessment of stability and plasticity of new hybrids of maize (Zea mays L.) under the conditions of Polissia and Steppe zones of Ukraine. Plant Var. Stud. Prot., 2, 16–21. doi: 10.21498/2518-1017.2(31).2016.70050. [in Ukrainian]
Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of Corn and Soybean Grain Yield with Topography and Soil Properties. Agron. J., 92(1), 75–83. doi: 10.2134/agronj2000.92175x
Li, B., Hoi, S. C. H., & Gopalkrishna, V. (2011). CORN: Correlation-driven nonparametric learning approach for portfolio selection. ACM Trans. Intell. Syst. Technol., 2(3), 21–24. doi: 10.1145/1961189.1961193
Johnston, R. Z., Sandefur, H. N., Bandekar, P., Matlock, M. D., Haggard, B. E., & Thoma, G. (2015). Predicting changes in yield and water use in the production of corn in the United States under climate change scenarios. Ecol. Engin., 82, 555–565. doi: 10.1016/j.ecoleng.2015.05.021
Assefa, Y., Vara Prasad, P. V., Carter, P., Hinds, M., Bhalla, G., Schon, R., … Ciampitti, I. A. (2017). A New Insight into Corn Yield: Trends from 1987 through 2015. Crop Sci., 57(5), 2799–2811. doi: 10.2135/cropsci2017.01.0066
Lorenzana, R. E., & Bernardo, R. (2008). Genetic Correlation between Corn Performance in Organic and Conventional Production Systems. Crop Sci., 48(3), 903–910. doi: 10.2135/cropsci2007.08.0465
Leng, G. (2017). Recent changes in county-level corn yield variability in the United States from observations and crop models. Sci. Total. Environ., 607–608, 683–690. doi: 10.1016/j.scitotenv.2017.07.017
Yi, Q., Liu, Y., Hou, X., Zhang, X., Zhang, X., Li, H., Zhang, J., … Huang, Y. (2019). Genetic dissection of yield-related traits and mid-parent heterosis for those traits in maize (Zea mays L.). BMC Plant Biol., 19(1), 392–399. doi: 10.1186/s12870-019-2009-2
Bielikov, Ye. I., & Kuprichenkova, T. H. (2018). New flinty maize. Zernovì kulʹturi [Grain Crops], 2(1), 22–28. doi: 10.31867/2523-4544/0003. [in Ukrainian]
Zarei, B., Kahrizi, D., Aboughadareh, A. P., & Sadeghi, F. (2012). Correlation and path coefficient analysis for determining interrelationships among grain yield and related characters in corn hybrids (Zea mays L.). Іnt. J. Agric. Crop Sci., 4(20), 1519–1522. doi: IJACS/2012/4-20/1519-1522
Abelmasov, O. V., & Bebeh, A. V. (2018). Specifics of the key yield components manifestation in self-pollinated corn lines under different growing conditions. Plant Var. Stud. Prot., 14(2), 209–214. doi: 10.21498/2518-1017.14.2.2018.134771. [in Ukrainian]
Mazur, M., Brkić, A., Šimić, D., Brkić, J., Jambrović, A., Zdunić, Z., & Galic, V. (2019) Genomewide analysis of biomass responses to water withholding in young plants of maize inbred lines with expired plant variety protection certificate. BioRxiv. doi: 10.1101/704668 [preprint]
Venancio, L. P., Mantovani, E. C., Amaral, C. H., Usher, C. M., Neale, C. M. U., Gonçalves, I. Z., Filgueiras, R., & Campos, I. (2019). Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agric. Water Manag., 225, 105–107. doi: 10.1016/j.agwat.2019.105779
Carpici, E. B., & Celik, N. (2012). Correlation and path coefficient analyses of grain yield and yield components in two-rowed of barley (Hordeum vulgare convar. distichon) varieties. Not. Sci. Biol., 4(2), 128–131. doi: 10.15835/nsb427388
Shukla, M. K., Lal, R., & Ebinger, M. (2014). Principal component analysis for predicting corn biomass and grain yields. Soil Sci., 169(3), 215–224. doi: 10.1097/01.ss.0000122521.03492.eb
Haidash, O. L. (2016). Assessment of combining ability for grain yield of self-pollinated S5 maize (Zea mays L.) families of mixed germplasm. Plant Var. Stud. Prot., 1, 62–66. doi: 10.21498/2518-1017.1(30).2016.61781
Lavrynenko, Yu. O., Kokovikhin, S. V., Naidonov, V. H., & Mykhalenko, I. V. (2008). Metodychni vkazivky z nasinnytstva kukurudzy v umovakh zroshennia [Methodological instructions for seeding of corn under irrigation conditions]. Kherson: Ailant. [in Ukrainian]
Dospekhov, B. A. (1985). Metodika polevogo opyta (s osnovami statisticheskoy obrabotki rezul’tatov issledovaniy) [Methods of field experiment (with the basics of statistical processing of research results)]. (5nd ed., rev.). Moscow: Agropromizdat. [in Russian]
Plyuta, V. (1980). Sravnitel’nyy mnogomernyy analiz v ekonomicheskikh issledovaniyakh: Metody taksonomii i faktornogo analiza [Comparative Multivariate Analysis in Economic Research: Taxonomy and Factor Analysis Methods]. Moscow: Statistika. [in Russian]
Vozhehova, R. A., Lavrynenko, Yu. O., & Hozh, O. A. (2015). Naukovo-praktychni rekomendatsii z tekhnolohii vyroshchuvannia kukurudzy v umovakh zroshennia Pivdennoho Stepu Ukrainy [Scientific and practical recommendations on the technology of corn cultivation under conditions of irrigation of the Southern Steppe of Ukraine]. Kherson: Hrin D. S. [in Ukrainian]
Lebid, Ye. M., Tsykov, V. S., Pashchenko, Yu. M., Dziubetskyi, B. V., & Cherchel, V. Yu. (2008). Metodyka provedennia polovykh doslidiv z kukurudzoiu [Method of conducting field experiments with corn]. Dnipropetrovsk: N.p. [in Ukrainian]
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Ukrainian Institute for Plant Varieties Examination 
Селекційно-генетичний інститут
Institute of Plant Physiology and Genetics of the National Academy of Sciences of Ukraine
The National Academy of Agrarian Sciences of Ukraine