The purpose of this research was to predict soil temperature pro-file using ‘panel-data’ models. Panel-data analysis endows regres-sion analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation.The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in Khorasan-Razavi Province, Iran. Daily mean soil temperatures for 9 years (2001– 2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training parameter training) over the period of 2001– 2008, and validation over the period of the year 2009. The panel-data models were developed using the average air tempera-ture and rainfall of the day before ( T d −1 and R t −1, respectively) and the average air temperature of the past 7 days ( Tw) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (R2= 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE =−0.83 and 0.74) revealed the effectiveness of this model. Inaddition, these results were compared with the results of classic lin-ear regression models using t -test, which showed the superiority of the panel-data models.

Keywords