Influence of environmental factors on the intensity of tomato plant transpiration

Abstract

Goal. To study the influence of environmental factors on the transpiration intensity of tomato plants and to build a model of transpiration intensity based on the multiple linear regression (MLR). Methods. General scientific: vegetation, laboratory, measurement-calculation, and mathematical-statistical analysis. Results. It was found that there was a close correlation between the intensity of transpiration of tomato plants by solar radiation, relative humidity, and air temperature, the correlation coefficients are 0.99, 0.96, and 0.94, respectively. The intensity of transpiration increases linearly with increasing solar radiation and air temperature and decreases with increasing relative humidity. The maximum values of transpiration intensity were at 12 and 14 h, and reached 538.1 and 574.1 g/m2·h, respectively, and between 12 and 14 h there was a noon depression of transpiration. Based on multiple linear regression, they obtained a model of transpiration intensity of tomato plants during the day with a coefficient of determination R2=0.91, standard error STr=27.5. A comprehensive statistical analysis of the obtained model was performed, which confirmed that the obtained model was statistically significant with a reliability of 95% and confirmed the hypothesis of the influence of climatic factors on the intensity of transpiration of tomato plants. The average absolute percentage error (AAPE) was 7.4%. Conclusions. Based on the conducted researches the daily periodicity of transpiration intensity of tomato plants and linear dependence of transpiration intensity on the action of solar radiation, temperature, and relative humidity of air is confirmed. The influence of ecological factors on the intensity of transpiration is established based on a multiple linear regression model. The greatest influence on the intensity of tomato transpiration has solar radiation, and the smallest — air temperature, standardized regression coefficients are equal to 1.169 and 0.028, respectively. The conducted comprehensive statistical analysis confirmed the high accuracy of the obtained model: the AAPE error is less than 10%.