Short communication: Summer on-farm environmental condition assessments in Québec tiestall farms and adaptation of temperature-humidity index calculated with local meteorological data
Bibliographic record
Abstract
Temperature-humidity index (THI) calculation following the equation developed by the National Research Council (A Guide to Environmental Research on Animals, 1971) requires ambient temperature (AT) and relative humidity (RH). Those data are widely and readily available at local meteorological stations. However, studies showed that using average AT and RH retrieved from the closest stations is not appropriate for estimating on-farm conditions. The present objectives were (1) to study summer on-farm environmental conditions, (2) to explore the relationship between summer THI calculated with on-farm data and summer THI calculated with local weather station data, and (3) to verify whether THI calculated with summer meteorological station data could be adapted to better represent summer on-farm conditions. Six tiestall dairy farms located in 2 regions of the province of Québec [Eastern Québec (EQ) and Southwestern Québec (SWQ)] were enrolled in this study. Within-barn conditions were monitored using 3 remote data loggers from August 2016 through August 2017. Two loggers were installed inside at varying distances relative to the ventilation inlet (L1: closest to inlet; L2: farthest from inlet) and a third was installed just outside of the barn (L3). Values retrieved from each logger and the closest local meteorological station were used to calculate daily THI according to the National Research Council formula and were ultimately compared. Our results showed that THI varied within the barn depending on the proximity relative to the inlet because THI measured by L1 was lower than THI measured by L2 in both regions. Moreover, our results showed that in both regions AT measured on-farm was consistently higher than AT measured at the weather station. The opposite was observed with RH, as it was significantly lower on-farm in EQ and numerically lower in SWQ compared with RH extracted from weather stations. Overall, this led to THI being lower by 4.6 and 3.7 units at the weather stations compared with within-barn conditions for EQ and SWQ farms, respectively. Hence, using local meteorological station data to estimate on-farm conditions would lead to an underestimation of heat stress level in dairy cows. Adapting THI calculations by including daily maximum AT and minimum RH retrieved from the local weather station instead of their average counterparts led to a better estimation of within-barn conditions. However, the difference between THI measured on-farm and the adapted THI calculated with weather station data remained significant. Although the adaption made to THI allowed for a closer relation to on-farm conditions, THI calculated with weather station data should only be used to assess heat stress level in dairy cows when heat stress thresholds are adapted for such data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".