Effect of average diurnal barn airspace temperatures on prediction of their development during the day

Abstract

CHLÁDEK, G., HANUŠ, O., FALTA, D.: Eff ect of average diurnal barn airspace temperatures on prediction of their development during the day. Acta univ. agric. et silvic. Mendel. Brun., 2011, LIX, No. 6, pp. 161-166 A year-round (i.e. 365 days) experiment was performed at the Mendel University Training Farm in Žabčice, Czech Republic (GPS 49°0'51.967"N and 16°36'14.614"E, the altitude 179 m) with the aim to quantify the eff ect of the variation of average diurnal barn airspace temperatures on prediction of their changes during the day. Barn airspace temperatures were monitored daily in one-hour intervals and the recorded values were used for calculations of average diurnal temperatures. These were classifi ed into 7 categories (i.e. below 0 °C; 0.1 to 5 °C; 5.1 to 10 °C; 10.1 to 15 °C; 15.1 to 20 °C; 20.1 to 25 °C and above 25 °C). Regarding this classifi cation system, all diff erences between temperatures measured at identical hours but within various limits were statistically highly signifi cant. The statistical analysis involved also the calculation of the third degree polynomial regression equations, which enabled to characterise the relationship between the temperature and the hour of measurement within the aforementioned categories of diurnal temperatures. Individual equations were markedly diff erent and ranged from y = − 0.0019x 3 + 0.0596x 2 − 0.3797x − 1.2169 (for temperatures below 0 °C) to y = − 0.0108x 3 + 0.3297x 2 − 1.9367x + 24.3931 (for temperatures above 25 °C). Correlation coeffi cients (r) and coeffi cients of determination (R 2 ) of these regression equations were generally very high and ranged from 0.872 to 0.976 and from 0.760 to 0.953, respectively. Regarding high values of both coeffi cients it can be concluded that the calculated equations enable a good and reliable prediction of the diurnal development of barn airspace temperatures. barn airspace temperature, prediction, heat stress, regression equations