Review Reports

Round 1

Reviewer 1 Report

The report can be found in the attached file.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 1 Comments

128: The authors mention that the number of cows in dry period. Why did they this? Were these cows involved into the analysis, as well?

The Reviewer is right; such information about cows in the dry period was eliminated. Thank you.

157: The authors mention referring to Figure 1 that there were days of heat stress with THI ≥ 80 (black colour) even in winter, although these are not visible in Figure 1. How to explain this?

The Reviewer is right again; corrected as suggested. Thank.

274: In Table 5, the numbers of THI level (72-76 and 77-80) are not correct, at least not the original grouping, and the percentage data of THI ≥ 80 is missing from the Table 5.

Our mistake; the Reviewer is right; corrected as suggested.

285: What did the authors mean of “2.4 l y 4.2”? What is the „y” between data?

Sorry; “y” was written in spanish; corrected to “and”…, thank you.

 

310: The authors mention the relationship at the THI ≥ 80 level, but this regression figure/trend with data is missing in Figure 9. Please, represent this figure, as well!

Again, the Reviewer is right. We miss the THI ≥ 80. Included in this new R1 version.

Reviewer 2 Report

In attachment

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 2 Comments

As suggested by Reviewer 2, in order to better support our main results and discussion, in this R1 version, we cited the following authors.  We acknwoledge this very important input. Thank you.

1. Cincović, M.; Majkić, M.; Belić, B.; Plavša, N.; Lakić, I.; Radinović, M. Thermal Comfort of Cows and Temperature Humidity Index in Period of 2005-2016 in Vojvodina Region (Serbia). Acta Agric. Serbica 2017, 22, 133–145, doi:10.5937/aaser1744133c.
2. Cincovic, M.; Belic, B.; Toholj, B.; Radovic, I.; Vidovic, B. The Influence of THI Values at Different Periods of Lactation on Milk Quality and Characteristics of Lactation Curve. Agric. Sci. Belgrade 2010, 55, 235–241, doi:10.2298/jas1003235c.
3. Hempel, S.; Menz, C.; Pinto, S.; Galán, E.; Janke, D.; Estellés, F.; Müschner-Siemens, T.; Wang, X.; Heinicke, J.; Zhang, G.; et al. Heat Stress Risk in European Dairy Cattle Husbandry under Different Climate Change Scenarios-Uncertainties and Potential Impacts. Earth Syst. Dyn. 2019, 10, 859–884, doi:10.5194/esd-10-859-2019.
4. Nogoy, K.M.C.; Park, J.; Chon, S. Il; Sivamani, S.; Park, M.J.; Cho, J.P.; Hong, H.K.; Lee, D.H.; Choi, S.H. Precision Detection of Real-Time Conditions of Dairy Cows Using an Advanced Artificial Intelligence Hub. Sci. 2021, 11, doi:10.3390/app112412043.
5. Gernand, E.; König, S.; Kipp, C. Influence of On-Farm Measurements for Heat Stress Indicators on Dairy Cow Productivity, Female Fertility, and Health. Dairy Sci. 2019, 102, 6660–6671, doi:10.3168/jds.2018-16011.
6. Herbut, P.; Angrecka, S.; Walczak, J. Environmental Parameters to Assessing of Heat Stress in Dairy Cattle—a Review. J. Biometeorol. 2018, 62,

On the other hand, the THI was calculated from climatic data retrieved via satellite by using the application described in the Materials and Methods section.  This application is a novel tool based on artificial intelligence and big data approaches.  According to the outlined information by the manufacturing company, this digital approach is highly correlated with terrestrial climatic and meteorological stations. The information available in other dairy farms taken directly in climatic stations should validate the information generated by digital and satellite approaches; such item must be evaluated in a subsequent investigation. Thank you for this important input.

Round 2

Reviewer 2 Report

No