Connection between the work of vertical drawing machines and meteorological factors

1986 ◽  
Vol 43 (11) ◽  
pp. 477-479
Author(s):  
V. I. Borisov ◽  
G. N. San'ko
Keyword(s):  
Meteorological Factors ◽  
Vertical Drawing

Influence of meteorological factors on potato yield in the conditions of the Right-Bank Forest-steppe

2018 ◽  
Vol 1 (94) ◽  
pp. 55-61
Author(s):  
R.O. Myalkovsky
Keyword(s):  
Meteorological Factors ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Potato Yield ◽  
High Yield ◽  
Forest Steppe ◽  
Potato Tubers ◽  
Middle Aged ◽  
Lower Yield ◽  
The Right

Goal. The purpose of the research was to determine the influence of meteorological factors on potato yield in the conditions of the Right Bank Forest-steppe of Ukraine. Methods.Field, analytical and statistical. Results.It was established that among the mid-range varieties Divo stands out with a yield of 42.3 t/ha, Malin white – 39.8 t/ha, and Legend – 37.1 t/ ha. The most favourable weather and climatic conditions for the production of potato tubers were for the Divo 2011 variety with a yield of 45.9 t/ha and 2013 – 45.1 t/ha. For the Legenda variety 2016, the yield of potato tubers is 40.6 t/ha and 2017 – 43.2 t/ha. Malin White 2013 is 41.4 t/ha and 2017 42.1 t/ha. The average varieties of potatoes showed a slightly lower yield on average over the years of research. However, among the varieties is allocated Nadiyna – 40.3 t/ha, Slovyanka – 37.2 t/ ha and Vera 33.8 t/ha. Among the years, the most high-yielding for the Vera variety was 2016 with a yield of 36.6 t/ha and 2017 year – 37.8 t/ha. Varieties Slovyanka and Nadiyna 2011 and 2012 with yields of 42.6 and 44.3 t/ha and 46.5 and 45.3 t/ha, respectively. Characterizing the yield of potato tubers of medium-late varieties over the years of research, there was a decrease in this indicator compared with medium-early and middle-aged varieties. However, the high yield of the varieties of Dar is allocated – 40.0 t/ha, Alladin – 33.6 t/ha and Oxamit 31.3 t/ha. Among the years, the most favourable ones were: for Oxamit and Alladin – 2011 – 33.5 and 36.5 t/ha, and 2017 – 34.1 and 36.4 t/ha, respectively. Favourable years for harvesting varieties were 2011 and 2012 with yields of 45.7 and 45.8 t/ha. Thus, the highest yield of potato tubers on average over the years of studies of medium-early varieties of 41.2-43.3 t / ha were provided by weather conditions of 2011 and 2017 years, medium-ripe varieties 41.0-41.1 - 2012 and 2011, medium- late 37,6-38,5 t / ha - 2012 and 2011, respectively.

Effects of Meteorological Factors at Different Growth Stages on Yield Traits of Maize (Zea maysL.) in Heilonggang Basin

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Growth Stages ◽  
Yield Traits ◽  
Different Growth Stages

Micro-variation in peach stem diameter and its relationship withsoil water potential and meteorological factors

2009 ◽  
Vol 17 (3) ◽  
pp. 489-494
Author(s):  
Jing-Wu ZHAN ◽  
You-Ke WANG ◽  
Lu-Jun ZHANG ◽  
Ping ZHANG ◽  
Jun-Qi HE
Keyword(s):  
Water Potential ◽  
Meteorological Factors ◽  
Stem Diameter

scholarly journals Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 187
Author(s):  
Olympia E. Anastasiou ◽  
Anika Hüsing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Cloud Cover ◽  
Detection Rate ◽  
Logistic Regression Model ◽  
Meteorological Factors ◽  
Seasonal Pattern ◽  
Weather Factors ◽  
Immunosuppressed Patients ◽  
Immunocompetent Patients

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.

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