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    Wyświetlanie 1-2 z 2
    Tytuł:
    Modeling Safflower Seed Productivity in Dependence on Cultivation Technology by the Means of Multiple Linear Regression Model
    Autorzy:
    Vozhehova, Raisa
    Fedorchuk, Mykhailo
    Kokovikhin, Serhii
    Lykhovyd, Pavlo
    Nesterchuk, Vasyl
    Mrynskii, Ivan
    Markovska, Olena
    Powiązania:
    https://bibliotekanauki.pl/articles/124374.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    cultivation technology
    prediction
    statistical analysis
    yields
    Opis:
    The results of the study devoted to the evaluation of reliability of the multiple linear regression model for safflower seed yields prediction were presented. Regression model reliability was assessed by the direct comparison of the modeled yields values with the true ones, which were obtained in the field trials with safflower during 2010-2012. The trials were dedicated to study of the effect of various cultivation technology treatments on the safflower seed productivity at the irrigated lands of the South of Ukraine. The agrotechnological factors, which were investigated in the experiments, include: A – soil tillage: A1 – disking at the depth of 14–16 cm; A2 – plowing at the depth of 20–22 cm; B – time of sowing: B1 – 3rd decade of March; B2 – 2nd decade of April; B3 – 3rd decade of April; C – inter-row spacing: C1 – 30 cm; C2- 45 cm; C3 – 60 cm; D – mineral fertilizers dose: D1 – N0P0; D2 – N30P30; D3 – N60P60; D4 – N90P90. Regression analysis allowed us to create a model of the crop productivity, which looks as follows: Y = –1.3639 + 0.0213Х1 + 0.0017Х2 – 0.0121Х3 + 0.0045Х4, where: Y is safflower seed yields, t ha-1; Х1 – soil tillage depth, cm; Х2 – sum of the positive temperatures above 10°С; Х3 – inter-row spacing, cm; Х4 – mineral fertilizers dose, kg ha-1. A direct comparison of the modeled safflower seed yield values with the true ones showed a very slight inaccuracy of the developed model. The maximum amplitude of the residuals averaged to 0.27 t ha-1. Therefore, we conclude that multiple linear regression analysis can be successfully used in purposes of agricultural modeling.
    Źródło:
    Journal of Ecological Engineering; 2019, 20, 4; 8-13
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply
    Autorzy:
    Vozhehova, Raisa
    Kokovikhin, Sergii
    Lykhovyd, Pavlo V.
    Balashova, Halyna
    Lavrynenko, Yuriy
    Biliaieva, Iryna
    Markovska, Olena
    Powiązania:
    https://bibliotekanauki.pl/articles/292829.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    linear model
    multiple linear regression analysis
    onion
    potato
    tomato
    yield modelling
    Opis:
    Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.
    Źródło:
    Journal of Water and Land Development; 2020, 45; 190-197
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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