Evaluation of fruit storability of different apple varieties using multivariate statistics
https://doi.org/10.31676/0235-2591-2025-5-39-53
Abstract
When developing new apple varieties (Malus domestica Borkh.), selection breeders should take into account not only the specifi c features of the cultivation area but also the desired set of economically valuable characteristics, including fruit storability with minimal quality loss. Apple fruits should meet a number of essential criteria, important to both producers and consumers. New genotypes should maximally conform to the characteristics of a commercial “model” variety developed and adapted to particular weather conditions. The parameters of storability under low storage temperatures should be accompanied by minimal quality loss and a high resistance to physiological disorders and microbiological diseases during storage. This study was aimed at identifying the most promising apple varieties in terms of an integrated set of storability criteria using “simulation” and “optimal” variety models. A simulation model of an “ideal” apple variety was developed, taking into account the criteria of fruit storability and durability during storage. On this basis, an “optimal” apple variety model for the central part of the Russian Federation (Central Region) was developed. The “ideal” and “optimal” models were described using 16 post-harvest criteria for 35 apple varieties studied over a 20-year period. The data were processed using a hierarchical clustering algorithm (HCA) with the Ward method and factor analysis with a multivariate principal component analysis to identify the most promising apple genotypes based on their maximum similarity to the model in terms of a set of storability traits and fruit resistance to physiological disorders and microbiological diseases during refrigeration at various temperatures. The degree of similarity between the developed models and the studied genotypes was determined based on the Euclidean distance. The Svezhest and Orlovsky Partizan varieties were closest to the “ideal” model. The criteria for the Svezhest variety were used as the basis for developing an “optimal” model. The Yubiley Moskvy, Orlovsky Partizan, Start, and Turgenevskoye varieties were closest to the “optimal” model.
About the Authors
A. L. NikitinRussian Federation
Andrey L. Nikitin, PhD (Agric.), Leading Researcher of the Laboratory of Biochemical and Technological evaluation of new varieties and storage
Orel Oblast, Orel district, Zhilina, 302530
M. A. Makarkina
Russian Federation
Honored Worker of RF Agriculture, Dr. Sci. (Agric.), Chief Researcher, Head of the Laboratory of Biochemical and Technological evaluation of new varieties and storage
Orel Oblast
References
1. Prichko T. G., Drofi cheva N. V., Smelik T. L., Germanova M. G. Criteria indicators characterizing picking maturity of apple fruits tree of the NCFCSHVW selection, New technologies. 2019;4(50):183-191. DOI: 10.24411/2072-0920-2019-10418. (in Russ.).
2. Aff holder F., Tittonell P., Corbeels M., Roux S., Motisi N., Tixier P., Wery J. Ad hoc modeling in agronomy: what have we learned in the last 15 years, Agronomy Journal. 2012;104:735-748. DOI:10.2134/agronj2011.0376.
3. Pytel I. F., Volynkin V. A., Oleinikov N. P. Implementation of models selected varieties of grapes in the technical direction of the institute for vine and wine «Magarach», Magarach. Viticulture at winemaking. 2015;3:74-75. (in Russ.).
4. Badenko V. L., Topaj A. G., Yakushev V. V., Mirschel W., Nendel C. Crop models as research and interpretative tools, Agricultural Biology. 2017:52(3):437-445. DOI: 10.15389/agrobiolo- gy.2017.3.437rus. (in Russ.).
5. Ogoltsova T. P. Selection of black currant. Proshloe, nastoyashchee, budushchee. Tula.: Priokskoe knizhnoe izdatel’stvo. 1991, 384. (in Russ.).
6. Sazonov F. F. M odel of an industrial black currant variety for Central Russia, Sadovodstvo i vinogradarstvo.2024;(4):13-20. DOI: 10.31676/0235-2591-2024-4-13-20. (in Russ.).
7. Soto-Bravo F., González-Lutz M. I. Analysis of statistical methods to evaluate the performance of simulation models in horticult ural crops, Agronomía Mesoamericana. 2019;30(2):517-534. DOI: 10.15517/am.v30i2.33839.
8. Huang K., Fu D., Jiang Y., Liu H., Shi F., Wen Y., Cai C., Chen J., Ou L., Yan Q. Storability and Linear Regression Models of Pericarp Browning and Decay in Fifty Litchi (Litchi chinensis Sonn.) Cultivars at Room Temperature Sto rage, Foods. 2023;12(8):1725. DOI: 10.3390/foods12081725.
9. Zhang B. B., Ma R. J., Cai Z. X., Yan Z. M., Yu M. L. A linear regression freezing point prediction model for peach (Prunus persica) fruits, Acta Horticulturae. 2021;1304:299-308. DOI: 10.17660/ActaHortic.2021.1304.41.
10. Zhou C. Y., Cheng Q. W., Ch en T., Meng L. L., Sun T. G., Hu B., Yang J., Zhang D. Y. Prediction of banana quality during storage by brown area, Acta Aliment. 2022;51(4):542-551. DOI: 10.1556/066.2022.00154.
11. Duktova N. A., Grib S. I., Ivanova E. V., Andronik E. L., Vinogradov D. V. Application of multivariate analysis to assess the breeding value of spring hard wheat source material, AgroEcoInfo: Electronic scientifi c and producti on journal. 2023;1. DOI: 10.51419/202131125. EDN: https://elibrary.ru/WCWOTI. (in Russ.).
12. Starchak V. I., Kibal’nik O. P., Stepanchenko D. A., Efremo- va I. G. Semin D. S. The use of cluster analysis in the selection of crop sorghum in Russia, Journal of Agriculture and Environment. 2022;2(22). DOI: 10.23649/jae.2022.2.22.10. (in Russ.).
13. Yushkov A. N., Bo rzykh N. V. Integral evaluation of winter hardiness of apple varieties using multidimensional statistical analysis, Plodovodstvo i yagodovodstvo Rossii. 2019;56:9-14. DOI: 10.31676/2073-4948-2019-56-9-14. (in Russ.).
14. Kibalnik O. P., Larina T. V. Cluster analysis of crossing components for the development of new drought-tolerant hybrids of grain sorghum, Agricultural Journal. 2023;1(16):25-33. DOI: 10.48612/FARC/2687-1254/003.1.16.2023. (in Russ.).
15. Stepanova N. A., Sidorenko V. S., Yandubaykin E.E. Cluster analysis of varieties and breeding lines of spring soft wheat by indicators of structural analysis and grain quality. Zernovye i krupyanye kul’tury. 2023;2(46):107-116. DOI: 10.24412/2309-348X-2023-2-107-116. (in Russ.).
16. Kanshina M. V., Misnikova N. V., Astakhov A. A., Yagovenko G. L. Morphological and biological peculiarities of sweet cherry productivity development in the south of the Non-chernozem zone, Agricultural Biology. 2021;56(5):979-989. DOI: 10.15389/agrobiology.2021.5.979rus. (in Russ.).
17. Gudova L. A., Lekarev A. V. Determination of the breeding value of sunfl ower varieties andhybrids with cluster and factor analyzes, Agrarian Bulletin of the Urals. 2024;24(07):850-859. DOI: 10.32417/1997-4868-2024-24-06-850-859. (i n Russ.).
18. Yakovenko V. V., Lapshin V . I. Methods of multivariate analysis in estimation of the new prospect strawberry selections, Agrarian science. 2024;7:149-153. DOI: 10.32634/0869-8155-2024-384-7-149-153.(in Russ.).
19. Sidorenko V. S., Tugareva F. V., Starikova Zh. V. Experimental verifi cation of cluster analysis to ide ntify valuable breeding samples of spring wheat, IOP Conf. Series: Earth and Environmental Science. 2021;650:012105. DOI: 10.1088/1755-1315/650/1/012105.
20. Lapshin V. I., Yakovenko V. V., Shcheglov S. N. Evaluation of the breeding and genetic potential of strawberry varieties and hybrids based on economically valuable traits, E3S Web of Con- ferences.2021;254:01020. DOI: 10.1051/e3sconf/202125401020.
21. Borojevic S. Principles and Methods of Plant Breeding. Per. s serbohorv. V. V. Inozemceva. Pod red. i s predisl. A. K. Fedorova. M.: Kolos. 1984, 344. (in Russ.).
22. Mal’chikov P. N., V’yushkov A. A., Myasnikova M. G. Formation of models of durum wheat varieties for the Middle Volga region. Samara: Samar. nauch. Centr RAN. 2009, 112. (in Russ.).
23. White J. W., Hoogenboom G. Simulating eff ects of genes for physiological traits in a process-oriented crop model, Agron- omy J. 1996;88:416-422. DOI: 10.2134/agronj1996.00021962008800030009x.
24. Hoogenboom G., White J. W., Acosta-Gallegos J., Gaudiel R. G., Myers J. R., Silbernagel M. J. Evaluation of a crop simulation model that incorporates gene action, Agronomy J. 1997;89:613- 620. DOI: 10.2134/agronj1997.00021962008900040013x.
25. Jones J. W., Hoogenboom G., Porter C. H., Boote K. J., Batchelor W. D., Hunt L. A., Wilkens P. W., Singh U., Gijsman A. J., Ritchie J. T. The DSSAT cropping system model, European Journal of Agronomy. 2003;18(3-4):235-265. DOI: 10.1016/S1161-0301(02)00107-7.
26. Sedova Z. A., Gudkovsky V. A. Study of pome fruit storage duration. Programma i metodika sortoizucheniya plodovyh, yagodnyh i orekhoplodnyh kul’tur. Pod red. E. N. Sedova, T. P. Ogol’covoj. Orel: VNIISPK, 1999, 177-183. (in Russ.).
27. Dement’eva M. I., Vygonskii M. I. Diseases of fruits, vegetables and potatoes during storage. M.: Agropromizdat. 1988. (in Russ.).
28. Nikitin A. L., Makarkina M. A. Infl uence of the weather condi- tions of the growing season, degree of maturity and tempera ture regimes storage on the keeping quality and fruit quality of new apple cultivars bred by VNIISPK, Storage and processing of farm products. 2023;4:145-164. DOI: 10.36107/spfp.2023.4.449. (in Russ.).
29. Kulikov I. M., Burmenko J. V., Svistunova N. Y., Evdokimenko S. N., Sazonov F. F., Tumaeva T. A., Konovalov S. N. Regionally Adapted Model of an Ideal Malus x domestica Borkh. Apple Varietyfor Industrial-Scale Cultivationin European Russia, Agriculture. 2022;12,2124. DOI: 10.3390/agriculture12122124.
Review
For citations:
Nikitin A.L., Makarkina M.A. Evaluation of fruit storability of different apple varieties using multivariate statistics. Horticulture and viticulture. 2025;(5):39-53. (In Russ.) https://doi.org/10.31676/0235-2591-2025-5-39-53






























