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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.
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Tom
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8--13
Opis fizyczny
Bibliogr. 13 poz., tab.
Twórcy
autor
- Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
autor
- Mykolaiv National Agrarian University, Heorhiia Honhadze 9 Street, 54000, Mykolaiv, Ukraine
autor
- Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
autor
- Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
autor
- Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
autor
- Kherson State Agrarian University, Stritenska 23 Street, 73006, Kherson, Ukraine
autor
- Kherson State Agrarian University, Stritenska 23 Street, 73006, Kherson, Ukraine
Bibliografia
- 1. Cheng CB, Lee ES. 2001. Fuzzy regression with radial basis function network. Fuzzy Sets and Systems 119(2), 291–301.
- 2. Cross SS, Harrison RF, Kennedy RL. 1995. Introduction to neural networks. The Lancet 346(8982), 1075–1079.
- 3. Draper NR, Smith H. 2014. Applied regression analysis. John Wiley & Sons, New York City,
- 4. Gelfand AE, Diggle P, Guttorp P, Fuentes M. 2010. Handbook of spatial statistics. CRC Press,
- 5. Kim HY. 2014. Analysis of variance (ANOVA) comparing means of more than two groups. Restorative Dentistry & Endodontics 39(1), 74–77.
- 6. Kutner MH, Nachtsheim C, Neter J. 2004. Applied linear regression models. McGraw-Hill, Irwin,
- 7. Lykhovyd PV. 2018. Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods. Biosystems Diversity 26(1), 11–15.
- 8. Mead R. 2017. Statistical methods in agriculture and experimental biology. Chapman and Hall, CRC,
- 9. Montgomery DC, Peck EA, 2012. Vining GG. Introduction to linear regression analysis (Vol. 821). John Wiley & Sons, New York City,
- 10. Neter J, Kutner MH, Nachtsheim CJ, Wasserman W. 1996. Applied linear statistical models (Vol. 4). Chicago: Irwin, p. 318.
- 11. Rosner B. 2006. Fundamentals of biostatistics. Duxbury Press, Belmont CA.
- 12. Seber GA, Lee AJ. 2012. Linear regression analysis (Vol. 936). John Wiley & Sons, New York City.
- 13. Ushkarenko VO, Naidionova VO, Lazer PN. 2016, Scientific investigations in agronomy: the textbook. Kherson, Grіn DS, pp. 314.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-890d5981-3fae-418b-ac8d-f27185dfc371