Biotic and abiotic factors limiting the productivity of <i>Prunus cerasifera</i> subsp. <i>macrocarpa</i> (Ehrh.) Erem. et Garcov.

Grigoriev, Alexander A.; Lukicheva, L. A.; Korzin, V.; Горіна, В. М.

doi:10.17660/actahortic.2021.1322.28

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…The aim of the present research is to focus on those environmental factors that significantly affect the flowering and productivity of cherry-plum trees (Prunus cerasifera Ehrh.). Some research [54] also addresses the minimum air temperature during flowering as the factor that affects the productivity of cherry plums, which is in accordance with our findings that highlight the importance of the temperature, as the DOY was more affected than the GDD during the 17-year period.…”

Section: Discussionsupporting

confidence: 91%

“…The species is highly resistant, especially to frost, and has shown a great power of adaptability, which is why it is used as a rootstock for grafting, breeding, and genetic resources [26,[78][79][80][81]. Some authors [54] also indicate the high resistance of different cultivars of the cherry plum to frost, based on research in Crimea. Others [82] point out that the cherry plum is a very resistant species, which does not have many requirements and whose production and cultivation do not have a bad impact on the environment.…”

Section: Discussionmentioning

confidence: 99%

“…A study [54] determined that the main factors limiting productivity in cherry-plum cultivars were the flowering intensity and minimum air temperature during flowering as well as spring frosts. Our results suggest that Prunus cerasifera Ehrh.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of climate factors, it was found that Prunus cerasifera Ehrh. is mostly affected by the flowering intensity and minimum air temperature during flowering, and spring frosts are especially important [54]. Another research [55] proved that the capacity of the cherry plum to withstand cold in the winter is highly dependent on temperature variations, highlighting autumn and the beginning of winter as the crucial period for the species to prepare for the winter season.…”

Section: Introductionmentioning

confidence: 99%

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Adaptability of Prunus cerasifera Ehrh. to Climate Changes in Multifunctional Landscape

Petrov,

Ocokoljić,

Galečić

et al. 2024

Atmosphere

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Urban trees play a vital role in mitigating climate changes, maintaining the sustainability of ecosystems. This study focuses on the assessment of the resilience of cherry plums to climate changes, a fruit-bearing species that offers diverse ecosystem services within multifunctional urban and suburban landscapes. This study examines flowering and fruiting in the context of climate characteristics, expressed through the Day of the Year (DOY), Growing Degree Days (GDDs), and a yield over 17 consecutive years. The results indicate significant shifts in the DOY but not in the GDD, apart from the end of flowering. The onset of flowering was earlier and the end postponed, extending the phenophase by an average of 4 days. The cherry plum’s yield was unaffected by climate changes, including extreme events like a late-spring frost. The stability of the cherry plum was confirmed by the phenological patterns of the bullace (cherry plum and blackthorn hybrid) exhibiting repeated flowering in the warmest year of 2023. The cherry plum is an adaptive species, with a high adaptability to a changing climate and a high resistance to late-spring frosts; thus, it is a favorable choice in urban design and planning, demonstrating resilience to climate shifts and thriving in polluted urban environments. It is especially appreciated for multiple ecosystem services: biodiversity conservation in natural and semi-natural areas, yielding good provisions in challenging environments, and the preservation of ornamental values through an extended flowering phenophase.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptability of Prunus cerasifera Ehrh. to Climate Changes in Multifunctional Landscape

Petrov,

Ocokoljić,

Galečić

et al. 2024

Atmosphere

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“…The arithmetic means of the values obtained and the relative standard deviation are calculated. The arithmetic mean value is taken as the result of the measurements of the total content of water-soluble antioxidants (in terms of quercetin) in the prepared parallel sample of the relative standard deviation [29,30].…”

mentioning

confidence: 99%

The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

Kazak

Plugatar

Johnson

et al. 2022

ASI

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This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R2 of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.

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Biotic and abiotic factors limiting the productivity of Prunus cerasifera subsp. macrocarpa (Ehrh.) Erem. et Garcov.

Cited by 2 publications

References 9 publications

Adaptability of Prunus cerasifera Ehrh. to Climate Changes in Multifunctional Landscape

Adaptability of Prunus cerasifera Ehrh. to Climate Changes in Multifunctional Landscape

The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines

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