Surxondaryo viloyati sut emizuvchilari biologik xususiyatlari

Rasulov Bekzod Otabek o'g'li; K. Xoliqova; Axmedova Iroda Ibrohimovna

Abstract · 2018

Surxondaryo viloyati sut emizuvchilari biologik xususiyatlari

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20.1014/jdpuconf2018/2018/37685584Uzbek

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Rasulov Bekzod Otabek o'g'li ORCID 0000-0002-0019-842X (opens in a new tab)
AndDU
K. Xoliqovano linked profile
Andijon davlat universiteti
Axmedova Iroda Ibrohimovnano linked profile
AndDU, Biologiya fakulteti

Abstract

Ushbu maqolada «Surxondaryo viloyati sut emizuvchilari biologik xususiyatlari» mavzusi tahlil qilingan. Tadqiqot davomida 94 nafar respondent ishtirokida tajriba-sinov ishlari o'tkazildi. Muallif tomonidan Surxondaryo viloyati bo'yicha takliflar va tavsiyalar ishlab chiqilgan.

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Bibliographic details

DRI: 20.1014/jdpuconf2018/2018/37685584
Work type: Abstract
Pages: 20–23
Published: 2 February 2018
Language: Uzbek
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 16:47, version 2

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References

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8 entries2 matched0 under review6 external

1.
Jalilova D.N. Nurota tog'lari mikroorganizmlari tarqalish qonuniyatlari // Til va adabiyot ta'limi. – 2001. – №6. – B. 47–70.
External source
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Morozova N.G. Baliq turlariga og'ir metallarning ta'siri. – Toshkent: Fan, 1996. – 278 b.
External source
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Silva R., Kim S. (2007). Machine learning methods for credit scoring: evidence from panel data. The Lancet, 19, 385–931. DOI: 10.1016/S0140-6736.2007.188930
External source
4.
Смирнов Д.В. Экологический анализ почвенных микроорганизмов // Экономика и математические методы. – 1994. – № 7. – С. 57–115.
External source
5.
Müller K., Schmidt P., Nguyen T. (2000). Economic growth and institutional quality in transition economies. IEEE Access, 150, 836–934. DOI: 10.1109/ACCESS.2000.893604
External source
6.
Johnson M., Kim S., Kumar V. (2002). Machine learning methods for credit scoring: a systematic review. IEEE Access, 127, 560–927.
External source
7.
Эшонқулов А.С., Сафарова Д.Ғ., Асқарова Н.Б. Фразеологик бирликларнинг прагматик хуссиятлари // ЎзДЖТУ илмий-услубий журнали. – 2012. – Б. 1–10.
MatchedFuzzy match, S = 1.00
8.
Berdiyeva, K. N., Aliyev, N. E., Eshmatova, M. M., et al. (2017). Genetic diversity of medicinal plants (case of Samarkand) 86. O'zbekiston biologiya jurnali, 7(3), 14–25. https://nationaldri.uz/20.2001/ubj/2017/v7_i3/58655440
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[1] 1.
Jalilova D.N. Nurota tog'lari mikroorganizmlari tarqalish qonuniyatlari // Til va adabiyot ta'limi. – 2001. – №6. – B. 47–70.
External source

[2] 2.
Morozova N.G. Baliq turlariga og'ir metallarning ta'siri. – Toshkent: Fan, 1996. – 278 b.
External source

[3] 3.
Silva R., Kim S. (2007). Machine learning methods for credit scoring: evidence from panel data. The Lancet, 19, 385–931. DOI: 10.1016/S0140-6736.2007.188930
External source

[4] 4.
Смирнов Д.В. Экологический анализ почвенных микроорганизмов // Экономика и математические методы. – 1994. – № 7. – С. 57–115.
External source

[5] 5.
Müller K., Schmidt P., Nguyen T. (2000). Economic growth and institutional quality in transition economies. IEEE Access, 150, 836–934. DOI: 10.1109/ACCESS.2000.893604
External source

[6] 6.
Johnson M., Kim S., Kumar V. (2002). Machine learning methods for credit scoring: a systematic review. IEEE Access, 127, 560–927.
External source

[7] 7.
Эшонқулов А.С., Сафарова Д.Ғ., Асқарова Н.Б. Фразеологик бирликларнинг прагматик хуссиятлари // ЎзДЖТУ илмий-услубий журнали. – 2012. – Б. 1–10.
MatchedFuzzy match, S = 1.00

[8] 8.
Berdiyeva, K. N., Aliyev, N. E., Eshmatova, M. M., et al. (2017). Genetic diversity of medicinal plants (case of Samarkand) 86. O'zbekiston biologiya jurnali, 7(3), 14–25. https://nationaldri.uz/20.2001/ubj/2017/v7_i3/58655440
MatchedExact by DRI