index (THI) was compared with maximum temperature in terms of its efficiency at predicting conception rate. The following data were recorded for each animal: herd, lactation number, insemination number, insemination date, inseminating bull, and AI technician along with climate variables such as mean and maximum temperatures, rainfall, mean and maximum THI for individual time points Days 7 to 1 before insemination, the day of insemination and 1, 2 and 3 days after insemination. Averages were also established for the following periods: from 7 days before insemination to the insemination day, from 3 days before insemination to the insemination day and from the insemination day to 3 days postinsemination. Based on the odds ratios, the likelihood of conception rate increased significantly by factors of 1.48, 1.47, 1.5, and 1.1 for the respective maximum THI classes <70, 71–75, 76–80, and 81–85 only on Day 3 before AI, while on the insemination day, it increased by factors of 1.73, 1.53, 1.11, and 1.3 for the respective maximum THI classes <70, 71–75, 76–80, and 81–85. In a subsequent logistic regression excluding mean and maximum THI, the effectiveness of temperature at predicting conception rate was evaluated. Although high, the fit of the second logistic regression model was slightly lower than that of the full model (P = 0.88 versus P = 0.98, respectively) and the information provided by the THI model. The likelihood of conception rate increased significantly by factors of 1.5, 1.2, 1.0, 1.0 for the respective maximum temperature classes <20, 21–25, 26–30, 31–35 8C on Day 1 after AI. The choice of the THI or temperature to monitor the farm environment would have to depend on the particular farm and situation. In our study conditions, the use of maximum temperature alone gives a new point of view regarding the information provided by the THI variables.