Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
(N/A)
Non Technical Summary
No dairy cow in the United States can avoid heat stress. To develop targeted heat abatementstrategies that judiciously use resources and promote dairy cow welfare, we must first understandthe behavioral variability of the heat stress response and phenotypically characterize a suite of noninvasivemetrics that are representative of an individual cow that can remain consistentlyproductive during thermally challenging environments. We seek to non-invasively characterize 1)the behavioral and physiological variability of the heat stress response, 2) the interaction betweentemperament and thermotolerance, and 3) the genotypes of cows that differ in their ability to copewith heat stress. A suite of individual cow responses will be collected during multipleenvironmental conditions using a variety of disparate data collection strategies. During lactation,cows will be monitored using body-mounted sensors (e.g., standing, lying, rumination), automaticrobotic milking systems (e.g., robot behavior, milk let-down reflex), video analytics (e.g., wateruse, brush use), productivity (e.g., milk fat, milk yield, SSC), reproductive success (e.g., DIM atfirst service), and health events (e.g., acidosis, lameness, mastitis). During the dry period, cowtemperament will be evaluated using a startle test. These results will be combined with genotypeand used to characterize known and novel phenotypes, and evaluate the associations and variabilityobserved therein. The knowledge gained from this proposal is expected to identify a noveltemperament selection strategy for dairy cows regarding thermotolerance and identify a suite ofnon-invasive animal-based metrics indicative of thermotolerance that can be integrated into geneticselection decisions.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Overall aimTo understand the variability of the heat stress response and to evaluate a suite of non-invasive metrics, using a combination of body-mounted, video analytic, and productivity tracking technologies, that are representative of an individual that can remain consistently productive during thermally challenging environments.Objective 1: The ethology of heat stressWe will characterize the variability and consistency of early lactation dairy cow behavior and productivity during a variety of heat stress conditions (measured within the barn) using body-mounted technologies (e.g., rumination collar, pedometer), the Lely A4 Robotic Milking System (e.g., milking behavior, milk yield, dead milk time, milk flow speed, fat, protein, lactose, solids non-fat, milk), video analytics (e.g., water use, brush use), and reproductive success (e.g., DIM at first service, first service conception rate).Objective 2: The temperament of heat stressThe autonomic nervous system (ANS) controls anxiety, thermoregulation, and the startle response. We will characterize the behavioral response of cows to a startle test during their dry period. The goal is to assess whether metrics reflective of ANS reactivity (e.g., behavior during the startle test, dead milk time, max milk speed) or activation (e.g., drinking behavior, brush use) would be viable phenotypes for characterizing dairy cow temperament and heat stress responsivity (Figure 2).Objective 3: The genotypes for the phenotype of heat stressObjective 3: The genotypes for the phenotype of heat stressOur inter-disciplinary team will synthesize behavioral, physiological, productivity, genetic, and environmental data to identify novel animal-based indicators of heat stress, evaluate the heritability of these phenotypes, and to characterize automated, non-invasive phenotypes for identifying specific cows that can maintain productivity during thermally challenging conditions.
Project Methods
Multiparous Holstein cows of known sires that are 45-90 DIM. Evaluating cows during this time frame will allow us to monitor the impact of heat stress conditions on cows close to their peak of milk production when they are most at risk of experiencing heat stress.Cows will be housed at T&K Dairy in a single free stall barn that is divided into six pens (n = 180 cows/pen). Each pen provides cattle with access to four water troughs evenly placed throughout the barn. Near three of the water troughs within the pen, is an automatic rotating cattle brush. Cows will be milked using a Lely Robotic Milking System (n = 18 robots, 3 robots per pen) a minimum of two times per day.A multitude of variables are collected using the robotic system and recorded in the Lely management software Time for Cows (T4C). Of specific interest to this project is milk production, milk yield, maximum milk speed, dead milking time, and robot behavior (visit, rejection, and fetch frequency). Within each pen, a subset of focal cows (n = 96; 16 cows/pen) that are 45-90 DIM will be monitored for a 45-d period. Each of the focal cows will be fitted with a leg-mounted pedometer (IceQube, Peacock Technologies, Inc.) designed to monitor cow standing, lying, and walking behavior.We will characterize the variability and consistency of early lactation dairy cow behavior and productivity during a variety of heat stress conditions using body-mounted technology, video analytics, productivity software, reproductive history, and environmental monitors.Individual cows will be fitted with SCR Dairy rumination collars (Merck Animal Health, Madison, NJ) and IceQubes (Peacock Technologies, Edinburgh, Scotland) during the 45-d observation period. SCR collars will measure the total time spent ruminating and active during each 2-h interval throughout the day. IceQubes will be attached to the right rear leg for the 45-d observation period. These accelerometers will quantify individual cow lying time, lying bouts, number of steps, lying bout time and motion index.Video cameras will capture individual cow behavior at the waterers and the brushes throughout the 45-d observation period. While wearing pedometers, their drinking and brush use behavior (frequency, duration, circadian pattern, displacements) will be decoded from video recordings. When individual identification is required on the video recordings, each dairy cow has a unique coat color spotting pattern that can be used for individual identification.Individual daily milk yield will be collected from each cow using the Time for Cows software program that is a component of the Lely A4 Robotic Milking system. This system has the capacity to monitor individual cow milking behavior (e.g., visits per day, refusals, inter-milking interval) and productivity (milk yield), is integrated with the SCR Rumination and Activity collars, and can monitor the milk let-down reflect (e.g., milk flow speed, dead milk time) and udder health at the individual quarter level (e.g., fat, protein, lactose, solids non-fat).A backup file of the dairy's DairyCOMP 305 (Valley Agricultural Software, Tulare, CA) productivity tracking software will be collected 60-d after the termination of the experiment (150 DIM). The data collected will be used to evaluate the reproductive success of the cows. Cows are bred based on estrus expression determined by the activity level measured by the SCR collars. If estrus does not occur by 72-78 DIM, cows are subjected to a timed AI (TAI) protocol and inseminated between 82-88 DIM. The number of cows that express estrus versus the number of cows that are bred by a TAI protocol will be evaluated to assess the effects of heat stress on estrus expression. Subsequently, the number of DIM at first service and first service conception rate will be determined for cows that are bred based on estrus or TAI. This will allow for the evaluation of reproductive success of cows bred during times of heat stress.Temperature-humidity data from HOBO U23 relative humidity and temperature loggers placed strategically throughout the barn.We will characterize the behavioral profiles of cows experiencing varying degrees of heat stress. Individual behavioral data will be used to characterize the diversity of heat stress behaviors observed and the seasonal impacts on behaviors both within and between groups. Cows that do not deviate significantly in milk production during times in which heat stress would be expected will be identified. Practical significance for identifying cows that are resistant to heat stress will be determined as part of the analysis after data is collected. A focal evaluation of their behavior (e.g., pedometer output, rumination data, robot behavior, drinking, brush use) and milk let-down reflex (e.g., max milk speed) will be compared among cows that experienced varying degrees of change in milk production.Using milk yield variability as the internal marker for heat stress, the variation in behavioral and productivity responses to increasing environmental temperatures will identify novel indicators of heat stress and demonstrate how individual cows vary in their behavioral and physiological responses to thermally challenging conditions. Reproductive success, milk production, and milk quality characteristics will be recorded from DairyComp 305. Production records will assess the dynamics of milk production and milk components. This production performance will be compared with the estimated production for the animal in her 305-day milk period to evaluate the amount of milk lost in the lactation due to heat stress.??Once focal cows enter the dry phase of production, they will be subjected to a startle test. The test will be conducted by the TAMU team at the T&K Dairy. Cows will be placed into a solid sided pen (20'x20') in groups of 4 and allowed to acclimate for 60 sec. After 60 sec in the pen, four umbrellas will be opened simultaneously to startle the cows The cow's behavioral reaction to this startling stimulus will be video recorded for a subsequent 5 minutes. Video recordings will be decoded for cow behavior in response to the umbrella opening, including, but not limited to number of steps, latency to first step after stimulus, etc. Cow behavior during the startle test will be compared to previous lactation productivity, dead milk time, and max milk speed.Regression modeling will be used to assess the effect of the startle response to the lactation performance. Each effect mentioned previously will be incorporated into the model to control for lurking effects. Should the full model be unable to show a continuous effect on milk production the model will be adjusted. A two-way ANOVA model, or a Logistic model are alterations based on how sensitive the response variable is shown to be. We anticipate that cows with stronger startle responses will have greater fluctuations in milk production in response to environmental temperatures, slower max milk speeds, longer dead milk times, use the brush more frequently, and have more visits per day to the water.Our inter-disciplinary team will synthesize behavioral, physiological, productivity, genetic, and environmental data to identify novel animal-based indicators of heat stress, evaluate the heritability of these phenotypes, and to develop automated, non-invasive phenotypes for identifying specific cows that can maintain productivity during thermally challenging conditions.