PSXI-10 Movement patterns and water source seeking in grazing lactating first-calf beef cows with different residual feed intake.
Bibliographic record
Abstract
Abstract Feed-efficient animals positively influence both environmental and economic sustainability. Selecting cows for a lower residual feed intake (RFI) has guided feed efficiency and been thoroughly studied; however, its association with thermoregulatory behavior on grazing systems is not widely explored. This study evaluated the impact of weather conditions on the distance walked and daily water proximity of grazing first-calf beef cows with different RFI. Thirty-five crossbred heifers with 11 mo of age were classified as more efficient (LOW-RFI: n = 17, -0.8 ± 0.214 kg dry matter/d) or less efficient (HIGH-RFI: n = 18, 1.5 ± 0.220 kg dry matter/d). After calving, cows (432 ± 8.40 kg, 26 ± 1 mo) were grazed within a single pasture from June to August. Geolocation data were recorded every 7.5 min and used to compute walking distances and water proximity using Nofence© collars for 34 d. Locations were calculated with the “geosphere” Spherical Trigonometry R package v. 1.5-20 to calculate kilometers and meters walked by hour. Animal distribution was mapped in Tableau 2024.3 displaying the location relative to water sources. Environmental parameters were recorded from a meteorological station within one kilometer and included temperature, solar radiation (SR), humidity, and wind speed (WS), which were used to compute the Comprehensive Climate Index (CCI) and adjusted temperature-humidity index (THIadj). Simple linear regression models were used to assess the ability of environmental variables to predict walking distance and proximity to water sources (SAS 9.4). The daily CCI ranged from 10 to 40 (absent to extreme environmental stress; respectively) while THIadj ranged from 7 to 63 (absent stress). The LOW-RFI cows had lower walked activity at 3 AM compared with HIGH-RFI (10 vs. 14 m; P ≤ 0.005). Regardless of RFI, cows walked more when THIadj was higher (1.97 km with THIadj of 67, vs. 1.43 km with THIadj of 46; P < 0.001). Moreover, when THIadj was ≤ 63 and CCI ≤ 40 (extreme), significant correlations with water proximity were found (P = 0.0026 and P = 0.03, respectively). SR and WS were the most influential variables when modeling predictors that explained cows’ proximity to a water source, with SR increasing nearness to water source and WS reducing it (P < 0.01). However, no significant correlations were detected between RFI classification and water proximity (P > 0.085). In conclusion, these findings highlight the influence of environmental conditions on movement patterns and water-seeking behavior of grazing lactating beef cows. Solar radiation and wind speed were the strongest predictors of cow proximity to water sources. However, residual feed intake classification did not significantly impact daily water procurement but impacted walked activity at early morning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".