Simple SummaryTemperature and thermal conditions of the interior of a swine trailer during transport were monitored over a broad range of outdoor conditions (34 trips total) managed according to industry best practice (Transport Quality Assurance (TQA) guidelines (NPB, 2008)). For the outdoor temperature range of 5 °C (40 °F) to 27 °C (80 °F), generally acceptable trailer thermal conditions were observed according to the TQA. Beyond this outdoor temperature range, undesirable conditions within the trailer were prevalent. Areas for potential improvement in transport management were identified. Stops resulted in rapid increases in temperature, which could be beneficial during cooler outdoor temperatures, but detrimental for warmer outdoor temperatures.AbstractTransport is a critical factor in modern pork production and can seriously affect swine welfare. While previous research has explored thermal conditions during transport, the impact of extreme weather conditions on the trailer thermal environment under industry practices has not been well documented; and the critical factors impacting microclimate are not well understood. To assess the trailer microclimate during transport events, an instrumentation system was designed and installed at the central ceiling level, pig level and floor-level in each of six zones inside a commercial swine trailer. Transport environmental data from 34 monitoring trips (approximately 1–4 h in duration each) were collected from May, 2012, to February, 2013, with trailer management corresponding to the National Pork Board Transport Quality Assurance (TQA) guidelines in 31 of these trips. According to the TQA guidelines, for outdoor temperature ranging from 5 °C (40 °F) to 27 °C (80 °F), acceptable thermal conditions were observed based on the criteria that no more than 10% of the trip duration was above 35 °C (95 °F) or below 0 °C (32 °F). Recommended bedding, boarding and water application were sufficient in this range. Measurements support relaxing boarding guidelines for moderate outdoor conditions, as this did not result in less desirable conditions. Pigs experienced extended undesirable thermal conditions for outdoor temperatures above 27 °C (80 °F) or below 5 °C (40 °F), meriting a recommendation for further assessment of bedding, boarding and water application guidelines for extreme outdoor temperatures. An Emergency Livestock Weather Safety Index (LWSI) condition was observed inside the trailer when outdoor temperature exceeded 10 °C (50 °F); although the validity of LWSI to indicate heat stress for pigs during transport is not well established. Extreme pig surface temperatures in the rear and middle zones of the trailer were more frequently experienced than in the front zones, and the few observations of pigs dead or down upon arrival were noted in these zones. Observations indicate that arranging boarding placement may alter the ventilation patterns inside the trailer.

The objective of this study was to evaluate the effects of piglet birth weight and drying piglets at birth on post-natal rectal temperatures using a CRD with 2 treatments: 1) Drying (not dried vs. dried at birth with a desiccant); 2) Birth weight [4 within-litter birth weight quartiles (Q1: 1.13 ± 0.33 kg, Q2: 1.43 ± 0.28 kg, Q3: 1.62 ± 0.28 kg, Q4: 1.81 ± 0.28 kg)]. Sows (26) and litters (281 piglets) were randomly allotted to drying treatment and were housed in farrowing crates with a heat lamp; room temperature was set at 22.8°C. Piglets were weighed at birth and rectal temperature measured at 0, 15, 30, 45, 60, 90, 120, 180, 240, and 1440 min after birth. Data were analyzed using PROC MIXED of SAS (SAS Inst. Inc., Cary, NC); the model included fixed effects of litter birth weight quartile and drying treatment and interaction, and time (repeated measure), and random effect of sow. Mean piglet birth weight and rectal temperature at birth were 1.49 ± 0.39 kg and 39.2 ± 0.43°C, respectively. There were no drying by birth weight treatment interactions. Temperatures were similar (P > 0.05) for the drying and birth weight treatments at birth and 240 and 1440 min (Table 1). Drying increased (P < 0.05) rectal temperature from 15 to 180 min; the greatest difference was at 45 min (2.4°C). Temperatures were similar (P > 0.05) for Q2, 3, and 4 from 15 to 180 min. Quartile 1 had a lower (P < 0.05) temperature than the 3 heavier quartiles from 15 to 180 min, except at 120 min when temperatures were similar for Q1 and 2. The lightest piglets exhibited the greatest post-natal temperature decline and drying of piglets at birth reduced the post-natal temperature decline in piglets of all weights.

No abstract

Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (>12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry.

No abstract