Modeling Safflower Seed Productivity in Dependence on Cultivation Technology by the Means of Multiple Linear Regression Model
 
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1
Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
2
Mykolaiv National Agrarian University, Heorhiia Honhadze 9 Street, 54000, Mykolaiv, Ukraine
3
Kherson State Agrarian University, Stritenska 23 Street, 73006, Kherson, Ukraine
CORRESPONDING AUTHOR
Pavlo Volodymyrovych Lykhovyd   

Institute of Irrigated Agriculture
Publish date: 2019-04-01
 
J. Ecol. Eng. 2019; 20(4):8–13
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ABSTRACT
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.