Progress 10/01/13 to 06/30/14
Outputs
Target Audience: Target Audience Dairy producers and allied ag industry Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? Opportunities I attended the Western Dairy Management Conference and Multi-state meetings where I received new information that helped me in this area. I also attended webinars specific to raising calves and heifers. How have the results been disseminated to communities of interest? Dissemination I developed 3 factsheets, 2 Dairy Newsletter articles, 1 popular press article (Hoards' Dairyman), 1 presentation, 1 Master's thesis, and 2 publications in preparation. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Accomplishments Objective 1: We determined that practically all dairy farmers are not recording enough information to make informed, management decisions regarding calves and heifers. Most farms record date of birth, calf gender (not all of the time if males), and maybe a calving difficulty score. Almost no one records health, management or breeding events. There is ample room for improvement in recording events that may have productive and economic impact in later life. Nutrition is a major component of managing heifers; therefore, we conducted a study to determine growth performance and ruminal fermentation of dairy heifers when fed Teff (Erogrostis tef [Zucc.], Poaceae) hay-based diets. Rations were formulated to similar energy and protein requirements to meet the dietary needs of the heifers and individual intake collected. Intake of DM significantly increased by feeding the teff grass diet to dairy heifers (P= 0.01). Feeding the teff grass diet increased body weight gain and ADG (P= 0.02). Dietary treatments did not affect G:F. Using a cost for alfalfa of $220/ton; the teff hay ration cost becomes equal to the alfalfa hay ration at a teff cost of $185/ton (less than this would be an advantage for teff). We conclude that teff grass can be a viable, lower-cost, palatable alternative to feeding alfalfa in dairy heifer diets. Objective 2: The environment of calves can have an impact, but is not well characterized over long-periods of time. We collected body weight, hip height, starter intake and weather data (temperature, wind speed, relative humidity, precipitation and barometric pressure) for 100 dairy calves over a 1-year period. Data were averaged for seven-day intervals beginning at birth through thirteen weeks of age. A regression model was developed including starter intake, milk intake, hip and wither height, calf heath scores, and weather data with weight gain as the dependent variable for each of the 4 seasons of the year. The fall season (September, October, and November) had a negative impact on calf intake and weight gain (averaging 20 pounds (9.1 kilograms) less at two months) than other seasons. Calves raised in the winter months also ate significantly more starter, but had the same weight gain as other seasons. We conclude that environmental stress factors impact animal welfare and animal productivity, which in turn impacts the economics of the dairy operation and should also be used in determining husbandry practices. Objective 3: Based on our results, dairy farmers should monitor weather conditions on their farm and make management decisions to improve calf environment. In addition, heat abatement for calves in hutches (under western conditions; open to weather) should be considered (for example, shade covers). In addition, even though we did not directly measure this, an additional interpretation of our data would be that dry cows that suffer heat stress can cause problems in utero that will affected a calf during early life. This would suggest that heat abatement for dry cows is very important to calf well-being.
Publications
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Progress 07/01/09 to 06/30/14
Outputs
Target Audience: Target Audience Dairy producers and allied ag industry Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? Opportunities I attended the Western Dairy Management Conference and Multi-state meetings where I received new information that helped me in this area. I also attended webinars specific to raising calves and heifers. How have the results been disseminated to communities of interest? Dissemination I developed 3 factsheets, 2 Dairy Newsletter articles, 1 popular press article (Hoards' Dairyman), 1 presentation, 1 Master's thesis, and 2 publications in preparation. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Accomplishments Objective 1: We determined that practically all dairy farmers are not recording enough information to make informed, management decisions regarding calves and heifers. Most farms record date of birth, calf gender (not all of the time if males), and maybe a calving difficulty score. Almost no one records health, management or breeding events. There is ample room for improvement in recording events that may have productive and economic impact in later life. Nutrition is a major component of managing heifers; therefore, we conducted a study to determine growth performance and ruminal fermentation of dairy heifers when fed Teff (Erogrostis tef [Zucc.], Poaceae) hay-based diets. Rations were formulated to similar energy and protein requirements to meet the dietary needs of the heifers and individual intake collected. Intake of DM significantly increased by feeding the teff grass diet to dairy heifers (P= 0.01). Feeding the teff grass diet increased body weight gain and ADG (P= 0.02). Dietary treatments did not affect G:F. Using a cost for alfalfa of $220/ton; the teff hay ration cost becomes equal to the alfalfa hay ration at a teff cost of $185/ton (less than this would be an advantage for teff). We conclude that teff grass can be a viable, lower-cost, palatable alternative to feeding alfalfa in dairy heifer diets. Objective 2: The environment of calves can have an impact, but is not well characterized over long-periods of time. We collected body weight, hip height, starter intake and weather data (temperature, wind speed, relative humidity, precipitation and barometric pressure) for 100 dairy calves over a 1-year period. Data were averaged for seven-day intervals beginning at birth through thirteen weeks of age. A regression model was developed including starter intake, milk intake, hip and wither height, calf heath scores, and weather data with weight gain as the dependent variable for each of the 4 seasons of the year. The fall season (September, October, and November) had a negative impact on calf intake and weight gain (averaging 20 pounds (9.1 kilograms) less at two months) than other seasons. Calves raised in the winter months also ate significantly more starter, but had the same weight gain as other seasons. We conclude that environmental stress factors impact animal welfare and animal productivity, which in turn impacts the economics of the dairy operation and should also be used in determining husbandry practices. Objective 3: Based on our results, dairy farmers should monitor weather conditions on their farm and make management decisions to improve calf environment. In addition, heat abatement for calves in hutches (under western conditions; open to weather) should be considered (for example, shade covers). In addition, even though we did not directly measure this, an additional interpretation of our data would be that dry cows that suffer heat stress can cause problems in utero that will affected a calf during early life. This would suggest that heat abatement for dry cows is very important to calf well-being.
Publications
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Progress 01/01/13 to 09/30/13
Outputs
Target Audience: Target audiences are researchers and the dairy industry. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? I attended the Western Dairy Management Conference and received new information that helps me in this area. How have the results been disseminated to communities of interest? Currently we are in the process of writing up these research results for dissemination to the dairy industry. What do you plan to do during the next reporting period to accomplish the goals? 1. To determine the current conditions of recording and raising calves and heifers in Utah. Prepare an article for Journal of Dairy Science on the calf research already completed.
Impacts
What was accomplished under these goals?
1. To determine the current conditions of recording and raising calves and heifers in Utah. Teff (Erogrostis tef [Zucc.], Poaceae) is attracting much interest among hay growers in the United States and Utah. This annual, warm-season grass grows best during the hottest months of the year when cool-season grasses suffer their “summer slump”. Due to its rapid growth, palatability, and high quality and yield, teff grass will likely become more common in Utah. An added benefit, teff is not known to have concern with prussic acid accumulation. Therefore, we conducted a study to determine growth performance and ruminal fermentation of growing beef steers and dairy heifers when fed teff hay-based diets (only dairy will be reported here). Twelve dairy heifers (~400 lb; 181 kg) were used in a 12-week study conducted comparing rations containing alfalfa hay with teff grass. Because of the differences in teff grass and alfalfa hay composition, rations were formulated to similar energy and protein requirements to meet the dietary needs of the heifers. The forage portion of the alfalfa-based diet contained 54.1% alfalfa hay and 24.8% corn silage (% of diet on a dry matter basis), while the forage portion of the teff grass diet had 8.5% alfalfa hay, 42.0% teff hay, and 11.3% corn silage. All animals were fed and housed in individual pens. Intake of DM significantly increased by feeding the teff grass diet to dairy heifers (P= 0.01). Feeding the teff grass diet to dairy heifers increased body weight gain and ADG (P= 0.02). Dietary treatments did not affect G:F. Using a cost for alfalfa of $220/ton; the teff hay ration cost becomes equal to the alfalfa hay ration at a teff cost of $185/ton (less than this would be an advantage for teff). We conclude that teff grass can be a viable, lower-cost, palatable alternative to feeding alfalfa in dairy heifer diets.
Publications
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Progress 01/01/12 to 12/31/12
Outputs
OUTPUTS: Female Holstein calves (n=100) were selected for this project beginning in March 2011 to March 2012 from the USU Caine Dairy and were followed from birth to weaning. Weekly body weights and wither and hip heights were collected. Daily whole milk and calf starter intake were also collected. Daily health assessments were made and recorded. An additional variable was added to differentiate between day and night with AM being defined as from 10:00 a.m. until 9:59 p.m. and PM defined as 10:00 p.m. until 9:59 a.m. In addition, each calf had an accelerometer monitor attached to one hind leg and a temperature/light sensor attached to the opposite hind leg. Environmental sensors were located adjacent to the hutches as well as hourly weather data collected from a weather station controlled by the Utah Climate Center located 1.3 km (0.8 mi) north from the dairy. A multiple regression analysis was used to determine the effects of nutrition and environment (temperature, relative humidity (RH), barometer, wind speed and precipitation) on weight gain (dependent variable). Least squares means for body weights, grain intakes, hip height, wither height, and weather-related variables were computed and summarized by 7-day increments, beginning at birth, and by season of year. All calves were retained in the dataset, including calves that got sick. A multiple regression model was developed to determine the variables associated with body weight. The final model of significant terms is as follows. Interactions were run, but had such a small effect they were not included in the final prediction model. The final R-squared was 0.94. The regression model used was body weights = -β0 + β1 intake am + β2 intake pm + β3 hip height + β4 precipitation am - β5 precipitation pm - β6 RH am + β7 RH pm - β8 score + β9 Temp am - β10 Temp pm - β11 wind speed am + β12 days since birth + β13 milk intake. An example of the time of day is: Intake am was the calf starter consumed from the morning feeding until the evening feeding. Intake pm was the calf starter consumed from the evening feeding to the morning feeding. Score is also a dummy variable where the totaled daily heath score was summed and if the number came to 3 or more, it was assigned a 1, all else were assigned a 0. PARTICIPANTS: ADVS faculty and graduate students TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Calves raised in the fall were at least 12 lb (5.5 kg) lighter at 8 weeks of life than calves raised at other times of the year, even though they ate as much as other times of the year. This may reflect excessive temperature swings or carry-over problems associated with heat stress while in utero during the summer period. Models of factors affecting weight gain found that increasing calf starter intake and environmental factors were important in predicting weight gain of unweaned calves. Based on $18/cwt milk and the cost of calf starter, delays in weaning past optimal levels adds $3/calf/day in additional feed costs (not including labor, etc.).
Publications
- No publications reported this period
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Progress 01/01/11 to 12/31/11
Outputs
OUTPUTS: Sixty female calves were selected for this project beginning in March 2011 to November 2011 from the USU Caine Dairy and were followed from birth to weaning. Weekly weighs, wither heights and hip heights were collected. Daily whole milk and calf starter intake were also collected. Daily health assessments were made and recorded. In addition, each calf had an accelerometer monitor attached to one hind leg and a temperature/light sensor attached to the opposite hind leg. Environmental sensors were located adjacent to the hutches as well as hourly weather data collected from a weather station controlled by the Utah Climate Center located 1.3 km (0.8 mi) north from the dairy. A subset of the total calves was used to help develop and refine a model for analyzing the data. Data from calves born in April 2011 and July 2011 were used to develop multiple regression analysis to determine the effects of nutrition and environment (temperature) on weight gain (dependent variable). A pivot table was constructed of the hourly weather station data beginning from July 2009 to December 2011 to compute the percentage of hours where the temperature fell below the lower critical thermal neutral temperature for calves up to 1 month old and also where the temperature was higher than the upper critical thermal neutral temperature for the same calves. An additional variable was applied to differentiate between day and night temperatures as described below. The regression model used was body weights = β0 + β1 intakeam + β2 intakepm + β3 spring + β4 tempam + β5 temppm + β6 score. Intakeam was the calf starter consumed from the morning feeding until the evening feeding. Intakepm was the calf starter consumed from the evening feeding to the morning feeding. Spring is a dummy variable indicating whether the data collected was in April (Spring) or July (Summer). Tempam was the average temperature from 10:00 a.m. until 9:59 p.m. and the temppm was average temperature from 10:00 p.m. until 9:59 a.m.. Score is also a dummy variable where the totaled daily heath score was summed and if 3 or more was assigned a 1, all else were assigned a 0. The variables intakeam, intakepm, spring, and tempam were all statistically significant at on a 1% level. Temppm and score were not statistically significant and dropped from the model. PARTICIPANTS: ADVS faculty and graduate students TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
The results of the model found that for every ounce of calf starter the calves consumed in the a.m. period increased body weight by 0.012 lb per day and were statistically significant (P < 0.00001). For every ounce of starter consumed in the p.m. period increased body weight by 0.0134 lb per day (P < 0.0001). The Tempam variable had a coefficient of 0.0721 lb/calf per day. It was the most statistically significant of any or the factors (P < 0.00001). These results confirm that higher intakes lead to higher weight gain. The variable Spring was also significant (P < 0.00001) had a coefficient of 0.8, which means that calves born in April were predicted to have 0.8 lb increased weight gain (24 lb/month) during the spring compared with those born in July. These calves should be able to be weaned sooner than those in the summer. At this rate it would take the calves raised in the summer between 2 and 3 weeks longer to reach their weaning weight. At a cost of $1.87 per day to feed a calf, calves born in July will cost the dairy between $26 and $39 ($785 - $1178 per month) more per calf due to slower growth. From January to May and October to December, over 80% of the hourly temperatures were below the thermal neutral zone. The percentages for June, July, August and September were 48, 11, 21, and 44%, respectively. The four months just mentioned were the only months were the temperature was above the upper thermal neutral level and were 12, 35, 30, and 16%, respectively.
Publications
- No publications reported this period
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Progress 01/01/10 to 12/31/10
Outputs
OUTPUTS: Objective 1: A random selection of dairies in Utah was selected from herds that belong to DHIA. Herd DHIA production information was downloaded and a report listing all heifers was run. Individual heifer records were evaluated for any recorded health events. Only 1 herd had any health events recorded; all other records were blank. Only scours and/or pneumonia were recorded for the one herd that recorded data. Objective 2: In order to validate the instrumentation we plan to use to determine the impact of environment on calves, 9 unweaned heifer calves (45 to 100 kg) were selected over 3 time periods (n=1, 4, and 5, respectively; March, August and September). HOBO data sensors that measured light and temperature (Pendant Temp/Light) and/or an accelerometer (Pendant G Logger) were attached either to the lower rear leg or neck of the calf. The accelerometer on the leg measured standing or lying behaviors in real time as well as any movement and motion of the leg. The accelerometer on the neck measured up and down movement of the head and neck as well as forward motion. The temperature and light senor was placed on the neck of the calf on the side opposite the accelerometer and all sensors were attached and oriented using VetWrap. Readings were taken every 6 seconds and recorded for approximately 24-26 hrs. All data was downloaded and graphed using HOBOware software (vs. 2.7.3). In addition, ambient temperature, RH% and light were recorded adjacent to the calf hutches and inside calf hutches not being used to give a relative baseline for inside the hutches. PARTICIPANTS: Dairies in Utah TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Objective 1: It is obvious that dairy farmers are not recording health events for heifers. It is also obvious that a farmer's ability to determine heifer disease and health problems on a herd basis is not currently possible based on their recorded data. Considering that approximately one third of their total expenses are on replacement heifers, it seems this is a major weakness in economic viability. The ability of a dairy to implement the Dairy Calf and Heifer Association "Gold Standards" cannot be done until this data is recorded. Objective 2: We have been able to validate the accelerometer readings such that we can tell if a calf is standing or lying down and for how long. We have also been able to determine whether the calf is inside the hutch or outside of the hutch during daylight hours. Because the same sensor also records temperature, we can correlate surface body temperature of the calf with whether it is inside or outside the hutch (shade or no shade). We have not yet been able to determine when the calf is inside or outside the hutch during the night; however, the temperature sensor on the neck has shown that the surface temperature of the calf will cool during the night, but at intervals the surface temperature will increase to approximately daytime temperatures. Not all calves do this and the younger calves seem to do it more. Our hypothesis is that the calf is expending energy in order to warm itself during the night. Our system seems to be a viable method for assessing environmental effects on calf behavior without having to constantly monitor by workers.
Publications
- No publications reported this period
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Progress 01/01/09 to 12/31/09
Outputs
OUTPUTS: I have been working to collect the needed equipment before beginning the project and at this time do not have any data to report. PARTICIPANTS: Dairies in Cache Valley, Utah TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers PROJECT MODIFICATIONS: I have changed direction in my Ag. Exp. Station project and will begin working with calves and heifers. This means I am having to retool and develop new protcols. I will be ready by January 2010.
Impacts
It is still too early for any work to have been accomplished, so there are no outcomes to report.
Publications
- No publications reported this period
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Progress 01/01/08 to 12/31/08
Outputs
OUTPUTS: Correlations of the complete database for the Jersey dairy (B) had positive correlations (>+0.4) between milk/cow and DMI for the fall season and most measures of temperature and humidity were negative (<-0.4) in spring. Also, there was a positive correlation between milk per cow and light over the complete dataset for Herd B. Herds A, B and C, over all data points, were negatively correlated for all temperature measures with milk protein percent and positive with relative humidity percent. Herds A (Holstein)and B showed strong negative correlations between milk protein percent and all temperature measures in the spring and fall season, while Herd C (Holstein) showed no significant correlation at the same times. Herds A, B and C all showed negative correlations over the complete data set between milk fat percent and all measures of temperature with fall season having the greatest correlation for Herds A and B, but not for C. In general, Herd C did not show as strong of a correlation between temperature and milk fat percent as the other dairies. Dry matter intakes for Herd B and C were negatively correlated with both average light and the light index during the winter season (December to February). Herds A and B were positively correlated with dry matter intake during the spring season. An interesting observation for Herd A was that the values from the sensors outside the freestall areas showed stronger correlations with production values than the sensors inside the freestall areas during the December thru February period. It is not clear why this is occurring because the difference in temperature between the outside monitors and inside monitors is close to zero during the winter and should have the same effect. Milk urea nitrogen (MUN) was also monitored during DHI test day. Each dairy showed highs and lows that were related to both management and environment; however, analysis of this function has not been completed. The standard deviation of each test day MUN was calculated as well as a mean for the herd and each string within the herd. In general, Herds B and C showed no relationship between the average MUN and the standard deviation; however, Herd A showed a positive (>0.8) correlation between the standard deviation and average test day MUN. This means that as the average increased, so did the variability among animals (a negative effect). Analysis of the difference between average MUN for the Jersey cows and Holstein cows on this dairy showed a positive polynomial relationship; the Jersey herd was higher than the Holstein by 2 MUN units. Herd C also had made management decisions to mitigate the effects of hot weather which could explain the lack of high correlations during the summer months. PARTICIPANTS: Dairies in Cache Valley, Utah TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
The evidence from this study suggests that Jersey cows handle summer heat better than Holstein cows unless some type of mitigation occurs, but are more negatively influenced by spring and fall conditions than Holsteins. Winter conditions had few correlations between production and environmental values for Jersey cows compared with Holstein cows which are surprising because the hypothesis was that they would do worse. Also, a similar correlation between herds showed as many differences as similarities suggesting that other factors such as nutrition and management may overcome some of the environmental challenges and is herd specific. A general observation suggests that herds seemed to be doing best when the coefficient of variation for MUN was 16 percent or less or had a standard deviation of less than 2.2. When either was higher, then it appears that intervention should occur. A spreadsheet to evaluate the whole farm effects of high nitrogen in the ration (as measured by MUN) was developed to evaluate the economic impact on dairies. Further work needs to be done to validate it on Utah dairies.
Publications
- Kincaid, S.D. (Major professor: Allen Young) 2006. Impact of Environmental Conditions and Nutritional Management on Nitrogen Utilization on Dairies. M.S. Thesis, Utah State University, Logan.
- Young, A.J. 2006. Can weather affect cows in Utah . USU Dairy Extension Newsletter, May Vol 29, No. 3:(May 2006).
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Progress 01/01/07 to 12/31/07
Outputs
OUTPUTS: This study is to determine the whole-farm effects of environment and nutrition on changes in production. Four dairies have had HOBO temperature sensors placed at strategic locations around the dairy. Sensors take a reading every 30 minutes and store temperature, relative humidity and light. All dairies are located in Cache Valley (Utah and Idaho). Dairy A began on March 21, 2007, Dairies B and C began on June 12, 2007 and Dairy D began on July 20, 2007. Herds C and D are Holstein herds. Herd B is a Jersey herd and A is a mix of both breeds of cows. Data collected includes feed quantity and composition, bulk tank milk, fat and protein percent, somatic cell count. All herds are on DHIA and all herds have a MUN test done at most monthly tests. Daily minimum, average and maximum temperature is recorded and the daily average temperature humidity index (THI) calculated. All values are compiled into a database from which SAS analyses could be performed. At present, only
correlations from June 12, 2007 to August 30, 2007 have been performed for Herds B and C. Dairy C is almost exactly north by 4.4 miles of Dairy B and is in a river bottom. For the time period, Dairy B averaged +6.54 degrees minimum temperature, +2.77 degrees average temperature, -3.37 degrees maximum temperature and +2.16 degrees THI compared with C. The average relative humidity of Dairy B was -6.56 percent compared with C. Dairy B averaged about 30 minutes longer per day than C where the THI was greater than 72 (historically the point where cows have problems). Correlation analysis was performed for both dairies and the 7 highest correlations for each variable were determined.
PARTICIPANTS: Dairies in Cache Valley, Utah
TARGET AUDIENCES: Dairy industry, ag industry representatives and researchers
Impacts
The Jersey dairy (B), had a positive correlation between minimum temperature and both milk/DMI and milk/cow. There was also a positive correlation with THI and milk/cow. The Holstein herd (C) had a positive correlation between moving average total milk and maximum temperature, THI and relative humidity percent. For milkfat percent, the Jersey herd had a negative correlation with the average amount of light during the day and the Holstein herd had a negative correlation with the number of hours the THI was over 72 (heat). Protein percent had a positive correlation with average light amount for the Jersey herd and had a negative correlation with minimum and average temperature, THI, relative humidity and heat for the Holstein herd. The SCC was positively correlated with all measures of temperature for the Jersey herd, but only positive for maximum temperature for the Holstein herd. Total DMI was positively correlated with average light and negatively correlated with
minimum temperature and THI for the Jersey herd and positively correlated with maximum temperature and heat for the Holstein herd. The preliminary evidence suggests that Jersey cows handle heat better than Holstein cows and light and minimum temperatures are very important parameters in the summer for this breed. Holstein cows tend to not do as well under high temperature conditions and maximum temperature was the most important weather variable. These conclusions are consistent with what we know about each breed, but this is the first time this has been documented in an arid area of low relative humidity that conventional wisdom suggests is immune from these environmental effects. These data will allow us to better troubleshoot weather problem and give ideas for better handling environmental problems found in this area.
Publications
- No publications reported this period
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Progress 01/01/06 to 12/31/06
Outputs
A limited data set was analyzed using values from the U.S.U. Caine Dairy to determine relationship between body weight (BW) and milk urea nitrogen (MUN). Weights were taken from cows that were all group-fed the same ration, so relationships between BW and MUN were assumed to reflect variations in milk production (MILK) and dry matter intake. Stepwise models were developed that used the different variables as linear elements. Future models will look at the quadratic and cubic values. Three models were developed: a) daily milk as the dependent variable and stepwise regression (forward, backward and maximum r) was performed using the individual cow variables of days since fresh (DSF), fat and protein percent, BW, lactation number, MUN and somatic cell count (SCC) as the independent variables; b) BW as the dependent variable, same variables minus BW as the independent variables; and c) MUN as the dependent variable, same variables minus MUN as the independent variables.
Significant variables of all stepwise models for model a) were: protein percent, MUN, BW, and DSF (the coefficient signs were: -, +, +, and -, respectively; model r-squared = 0.524). Significant variables of all stepwise models for model b) were: DSF, lactation number, milk, MUN, SCC and protein percent (the coefficient signs were: +, +, +, -, -, +, respectively; model r-squared = 0.503). Significant variables of all stepwise models for model c) were: milk, SCC, BW, and DSF (the coefficient signs were: +, -, -, +, respectively; model r-squared = 0.21). In general, BW and MUN appeared to be associated in all three models. In model b) MUN and BW are inversely associated; that is for every increase of 1 unit of MUN, BW decreased from 13.9 to 16.5 lbs. In model c) MUN and BW were again inversely associated; for every increase of 100 lbs BW, MUN decreased by 0.367 units (i.e. 1 unit decrease in MUN for every 272.5 lb BW). The lower r-squared for model c) suggests that our assumption of
equivalent diet composition of intakes and the association with BW may not be true. However, using the string averages from the original dataset and model variables for a), b) and c), model c) predicted group MUN within +/- 0.7 % while, model b) predicted average BW within +/- 1.9% and model a) predicted milk average within +/- 7.2%. Model a) might have been a better predictor if the quadratic and cubic terms of DSF were used. Our data, using just one herd shows that MUN and BW are associated with one another and alone or in combination are better predictors of milk, BW and MUN than without the other. The inverse relationship is interesting. The most logical reason would be increased MUN caused excess energy to be diverted from BW to detoxifying MUN. If one is using MUN as a predictor of excess dietary protein and subsequent excess loss of N to the environment, BW should be part of the model.
Impacts
Nutrition can impact dairy income by altering milk yield and composition or increasing feed costs. Feed costs are typically half of all expenses on a dairy. Development of better models to predict MUN will help the dairy farmer more correctly balance the ration so that protein is used more efficiently and less leaves the cow via urine and feces, thereby decreasing the potential to cause environmental problems.
Publications
- No publications reported this period
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Progress 01/01/05 to 12/31/05
Outputs
This past year, data collection was completed from the Caine Dairy Center and analyzed to determine relationships between nutrition and environmental factors on production parameters. Bulk tank milk samples were collected every other day from March 1, 2004 to April 30, 2005 and were analyzed for milk urea nitrogen (MUN) concentration, milk protein percent, milk fat percent, and somatic cell count (SCC). Total milk yield was determined from bulk tank pickups and AFIFARM records. Environmental variables including temperature, light radiance, and relative humidity were measured. The daily minimum, maximum, and mean values of these variables were determined. Temperature and humidity values were used to calculate temperature-humidity index and temperature and light radiance were used to calculate potential evapotranspiration rate. Dry matter intake, percent crude protein and acid detergent fiber of the ration were determined. A multivariate regression in SAS was used to
determine the relationship of the environmental and ration variables to MUN. The GLM procedure in SAS was used to determine the relationships of the environmental and ration variables to milk production parameters. Univariate regression models of SCC against all independent environmental and dietary variables showed no statistically significant results (results not shown). The best model for predicting MUN included percent crude protein in the ration and maximum temperature-humidity index (P < .0001; R2 = 0.618). The best environmental predictor of milk yield was maximum temperature-humidity index (P < .0001; R2 = 0.390). Dry matter intake was the best nutritional predictor of milk yield (P < .0001; R2 = 0.281). The best environmental predictor of milk protein percent was potential evapotranspiration rate (P < .0001; R2 = 0.716). Percent crude protein in the ration was the best nutritional predictor of milk protein percent (P < .0001; R2 = 0.501). Potential evapotranspiration rate was
also the best environmental predictor of milk fat percent (P < .0001; R2 = 0.409). Dry matter intake was the best nutritional predictor of milk fat percent (P = 0.0024; R2 = 0.245). Our data show that MUN and the other milk production parameters were significantly related to both environmental and nutritional variables and these variables can be used to predict milk yield and composition. It is important to consider both environmental conditions and ration composition when trying to diagnose optimal MUN concentrations and greater milk yield and composition.
Impacts
Environment and nutrition can impact dairy income by decreasing milk yield and composition or increasing feed costs. Feed costs are typically half of all expenses on a dairy. It is currently difficult to separate the two effects. Collection of information containing environmental and nutritional values and then analyzing the data statistically and by control charts can aid the dairy farmer and consultants in making correct management decisions by separating the day-to-day variability from real changes that can have production, health and economic consequences.
Publications
- Kincaid, S.D. 2005. Impact of Environmental Conditions and Nutritional Managementon Nitrogen Utilization on Dairies, M.S. Thesis, Utah State University, Logan.
- Young, A.J. 2005. In control or out - How is your dairy? In Hoard's Dairyman, Western Dairy News, Vol 5, No. 12 (Dec. 2005), pg W-207-208.
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Progress 01/01/04 to 12/31/04
Outputs
This past year, data were collected from the USU Caine Dairy for the purpose of developing acceptable protocols and needed data in order to learn how to use this information on commercial dairies. Bulk tank data were collected and put into control charts. This included milk volume, fat and protein percent, somatic cell count and PI count. Samples from each bulk tank pickup were also collected and analyzed for fat and protein percent, somatic cell count and milk urea nitrogen concentration (MUN). Body weights of cows are also collected monthly. Daily milk weights were collected electronically from the Afikim System at the dairy. In addition, daily dry matter feed intakes were collected from the EZ-Feed program and periodic feed samples collected and analyzed at the USU Forage Laboratory. Cameras were also strategically placed within the freestalls of 2 pens of lactating cows at the university dairy and periodically run to capture cow feeding and lying behavior.
Approximately 8 HOBO data loggers were placed throughout the dairy. The data loggers collected temperature, humidity and light measurements at 15-minute intervals. We are currently assembling the information so that we can begin to look for relationships between environment and nutrition on production as well as confirm if MUN relationships with cow body weight in Utah are similar to relationships found in Maryland.
Impacts
Environment and nutrition can impact dairy income by decreasing milk production or increasing feed costs. Feed costs are typically half of all expenses on a dairy. It is currently difficult (impossible) to separate the two effects. Collection of information containing environmental and nutritional values and then analyzing the data statistically and by control charts can aid the dairy farmer and consultants in making correct management decisions by separating the day-to-day variability from real changes that can have production, health and economic consequences.
Publications
- Young, A.J. 2004. The Use of Milk Urea Nitrogen (MUN) as a Management Tool. Western Dairy News in Hoard's Dairyman West, October. Vol 4, No. 8:W-123.
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Progress 01/01/03 to 12/31/03
Outputs
This past year the CRIS project was rewritten with one objective continued, one dropped and two others added. The objective that was retained and the only one that I worked on was the use of control charts for summarizing information on a dairy to enhance the ability of a dairy farmer to make appropriate decisions. The demand for these charts has increased and I usually create from 6 to 72+ control charts for individual dairy farms in Utah, Idaho, Wyoming, Montana and Nevada. I have three herds that I work with closely to monitor daily bulk tank production values and compare with ration changes or management decisions to determine the effects on production and income. These charts have been used as part of management team meetings with the dairy farmer, veterinarian, feed consultant and management consultant. As was mentioned last year, most ration changes did not result in changes in bulk tank milk levels or milk components. Also, many production changes in either
milk yield and/or components did not necessarily translate into increased income because of the volatility of the market. We now are using control charts that combine milk yield and component percentages into pounds of fat or protein as a monitor to determine if production has really changed. In addition to production and income, we monitor dry matter feed intake and combine with income to develop benchmarks for the whole herd as well as individual strings. As we continue to add/develop new charts, the value to the management meetings has grown. A discussion at a management meeting lead to the monitoring of the differences between the pounds of milk as reported by the processor and those reported by the milk meters in the parlor. The two values should be approximately the same, yet the differences (this is a new parlor) were significant and required the service company to recalibrate the meters sooner than expected. From these control charts the company was able to determine that
meters should be recalibrated more often than manufacturer recommendations. In addition, this same dairy farm had a serious health problem outbreak that required the company that made their vaccine to do a site visit to determine if the problem was product related. We used control charts to summarize the health data and the Technical Service Veterinarian for the company said that as soon as he saw the process control graph of death loss and the vaccinations his job was done. He said this by far was the best and most correct way of looking at the data. He also planned on sending copies to the rest of the Tech service in this company. Control charts are becoming valuable on these farms when used at team meetings with the consultants, nutritionists, and dairy farmer in making management decisions.
Impacts
Control charts can aide the dairy farmer and consultants in making management decisions by separating the day-to-day variability from real changes that can have production, health and economic consequences.
Publications
- Spears, R.A., A. J. Young, R.A. Kohn. 2003. Whole-farm phosphorus balance on western dairy farms. J. Dairy Sci. 86(2): 688-695.
- Johnson, R.G. and A.J. Young. 2003. Relationship of milk urea nitrogen and DHI production variables in western commercial dairy herds. J. Dairy Sci. 86(9):3008-3015.
- Spears, R.A., R.A. Kohn and A.J. Young. 2003. Whole-farm nitrogen balance on western dairy farms. J. Dairy Sci. 86(12): 4178-4186.
- Young, A.J. and S.P. Tripp. 2003. Culling rate and death loss associations with DHI production values. J. Dairy Sci. 86(Suppl. 1): 54 (abst.).
- Young, A.J. and S.P. Tripp. 2003. Association Between Production, Feed and Weather on a Commercial Dairy, a case study. J. Dairy Sci. 86(Suppl. 1): 55 (abst.).
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Progress 01/01/02 to 12/31/02
Outputs
The correlation of MBTI preferences with production values to see what definable skills determine productivity on a dairy was completed this past year and resulted in a publication in the Journal of Dairy Science. In general, few preferences were correlated with production values; however, the "E-I" dimension seemed to be the most relevant and further work into the financial correlations would be interesting. Further work at this time probably will be postponed. The use of control charts for summarizing information on a dairy to enhance the ability of a dairy farmer to make appropriate decisions has increased during the past year. I currently have created various control charts for 39 dairy farms in Utah, Idaho, Wyoming, Montana and Nevada. At present I have two herds that I have sufficient information to compare daily bulk tank milk, milk fat percent, milk protein percent and somatic cell count with changes in rations or management decisions to determine the effects
on income as measured by the Federal Milk Marketing Order (FMMO) and Chicago Mercantile Exchange (CME) cheese price. It was interesting that most ration changes did not result in changes in bulk tank milk levels or milk components. It was also interesting than many production changes in either milk yield and/or components did not necessarily translate into increased income. Further, there was a difference in income depending on whether one used the FMMO or the CME cheese price. I was able to document a two-week increase in somatic cell count that decreased bulk tank milk yield. This resulted in lost income in just milk sales of over $30,000 and the dairy farmer was not aware that it had happened. Control charts are becoming valuable on these farms when used at team meetings with the consultants, nutritionists, and dairy farmer in making management decisions. The bankers of one dairy have required the use of control charts to document day-to-day changes in income for this dairy. The
association of the Milk Urea Nitrogen test (MUN) with production variables was written and submitted to the Journal of Dairy Science. This has delayed the next objective of this project in determining the profitability associated with changes in MUN as measured using control charts and DHIA records.
Impacts
Control charts can aide the dairy farmer in making management decisions by separating the day-to-day variability from real changes that can have production, health and economic consequences. The MUN test can help improve income by making sure that protein in the ration is being correctly used. This will reduce environmental impact from nitrogen.
Publications
- Young, A.J. and J.W. Walters. 2002. Relationship between DHI production values and Myers-Briggs Type Indicator as a measure of management ability. J. Dairy Sci. 85(8):2046-2052.
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Progress 01/01/01 to 12/31/01
Outputs
The milk urea nitrogen (MUN) test can provide a quick, noninvasive indicator of herd nitrogen (N) metabolism. Excess N in the ration can be expensive and may contribute to excess N excretion into the environment. Dairy producers in Utah, Idaho and Montana have had the option for the past 3 years to analyze MUN level for their herds. Many are unsure of how to interpret and apply the results of this test. Information is needed on how MUN relates to production variables before farmers will fully understand and adopt the use of MUN to assess protein efficiency of their rations. Association between MUN and milk production variables for western dairies needs to be determined. A retrospective observational study using DHIA records was conducted with the objective to show the association of MUN concentration to milk yield, milk protein, milk fat, somatic cell count, parity, season, and reproduction for commercial dairy herds in Utah, Idaho, and Montana. Monthly individual cow
DHIA test records (approximately 90,000) from 96 Holstein and Jersey herds in Utah, Idaho, and Montana were analyzed to determine the relationships of milk production parameters to MUN. Records spanned two consecutive years ending December 2000. Means for Holsteins were 34.1 kg/d milk yield, 15.2 mg/dl MUN, 3.18 % milk protein, 3.5 % milk fat, and 280,000 somatic cell count. Mean MUN for Holsteins was lowest in fall (Oct.-Dec) and highest in winter and spring. Cows with highest milk protein percent (> 3.2%) had lowest MUN over all levels of milk yield (this has major implications in evaluating MUN). Means for Jerseys were 21.9 kg/d milk yield, 13.7 mg/dl MUN, 3.73 % milk protein, 4.6% milk fat, and 329,000 SCC. Mean MUN for Jerseys was lowest in summer and fall (June-Dec), while cows with the highest protein % (> 3.8%) did not have significantly different MUN concentrations from groups with lower milk protein %. Mean MUN, by 30 DIM increments, followed a curvilinear shape, similar to
a lactation curve. This suggests that ration protein changes in relation to milk production and/or stage of lactation change MUN. Models suggest that as MUN increased, pregnancy status decreased, especially in Jerseys. This is one of the first times that impaired reproduction has been demonstrated in commercial dairies as opposed to controlled studies. Mixed multivariate models showed that milk yield, protein %, month of test, and lactation number were significantly related to MUN concentration, with milk yield having the greatest effect on MUN variation for Holstein cows (79% of sum of squares) and lactation category contributing the most variation in Jerseys (34.1% of sum of squares). The study showed that MUN may be associated with DHIA production variables. It is important to consider production variables, especially milk protein percent, when developing optimal MUN concentrations relative to nutrient requirements under western conditions.
Impacts
The MUN test can provide an indicator of cow nitrogen (N) metabolism. Excess N in the ration can be expensive and may have an environmental impact. Production variables, such as milk yield and protein percent, were significantly related to MUN concentration and are important, especially milk protein percent, when evaluating MUN concentrations relative to nutrient requirements and cost of rations.
Publications
- Johnson, R.G., J.W. Walters and A.J. Young. 2001. Relationship of milk urea nitrogen and DHIA production variables in western commercial dairy herds. J. Dairy Sci. 84 (Suppl. 1): 267 (Abst.).
- Johnson, R.G. 2001, The relationship of milk urea nitrogen and DHIA production variables in western commercial dairy herds, M.S. Thesis, Utah State University, Logan.
- Young, A.J. 2001. Milk Urea Nitrogen Test (MUN). USU Extension Electronic Publication (referred).
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Progress 01/01/00 to 12/31/00
Outputs
Use of the milk urea nitrogen (MUN) test is becoming more important to the dairy industry because high concentrations are correlated with high urinary nitrogen output and increased feed expense due to excess protein in the ration. Information on average MUN concentrations under Intermountain U.S. conditions as well as average daily fluctuations is not known. This information would be useful in making productive and profitable management decisions. The objectives of this study are to determine daily and monthly average MUN concentrations on commercial dairies in the Intermountain region. DHIA data from commercial dairies that have had a whole-herd MUN analysis by Rocky Mountain DHIA are currently being coded for statistical analysis to determine the correlation between MUN and milk, fat and protein %, and somatic cell count (SCC). A preliminary analysis of over 7000 cow-months (out of a possible 50,000+) suggests a small, but significant (r = 0.31; P < 0.0001) positive
correlation of MUN with milk level and a small, but significant negative correlation with milk fat % (r = -0.12; P < 0.0001) and protein %(r = -0.25; P < 0.001). The second part of the objective involves collecting bulk tank milk samples from commercial dairies, analyze for MUN concentrations, and display the data using control charts. So far, four dairies (3 commercial; 1 university) have had bulk tank milk samples collected. Dairy A was sampled from 7/9/99 - 11/8/00 (n = 103), Dairy B was sampled from 11/19/99 - 8/1/00 (n = 116), Dairy C from 11/20/99 - 9/17/00 (n = 112), and Dairy D from 11/28/99 - 10/18/00 (n = 108). The mean (mg/dl), standard deviation and range, respectively, for the four dairies are as follows: Dairy A: 12.4, 0.75, 10.3 - 14.0; Dairy B: 12.2, 1.35, 9.2 - 15.5; Dairy C: 12.8, 1.49, 10.0 - 17.0; and Dairy D: 13.9, 1.85, 9.4 - 19.8. The overall mean of all four dairies was 12.8 mg/dl. Two standard deviations were used to determine an acceptable MUN variation. The
range was from 10.1 to 15.6 mg/dl. This is similar to the current recommendation for "normal" concentrations being between 12.0 - 16.0 mg/dl. Large daily variability, as determined by control charts, could be traced to less than ideal management practices on these dairies. Additional dairies will be sampled in the future.
Impacts
This research is important because low milk prices and greater emphasis on reducing environmental pollution have put increased financial stress on dairy farmers. The Milk Urea Nitrogen test is a management tool that will allow the farmer to make the most efficient economical use of the protein fraction of his ration and, at the same time, reduce environmental pollution through decreased urinary excretion of nitrogen.
Publications
- No publications reported this period
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Progress 01/01/99 to 12/31/99
Outputs
I have been developing a profile of what constitutes a 'good manager' as defined by correlations between Myers-Briggs Type Indicator preferences and DHIA production values. The instrument was completed by 75 dairy farmers at annual DHIA meetings. Dairies were divided by number of milkings per day and breed of cow. Preference type was analyzed against 144 DHIA production parameters. Most type-production combinations showed no significant differences. Differences due to number of times milking per day are important because the management skills required to manage a dairy milking 3 times per day (3X) are different than 2X. Fifty-one percent (51%) of the dairy farmers (all males) were classified as Introversion (I) in the E-I dimension. Ninety-five percent (95%) were Sensing (S), 71% were Thinking (T), and 64% were Judging (J). The composite profile was either ISTJ or ESTJ and accounted for 50% of all respondents. Statistically, the only difference in production
parameters between the two types was that ESTJ-3X milkers had a significantly lower Age at First Calving than ISTJ-3X milkers. However, in terms of practical differences, ISTJ-2X milkers had almost $220,000 more total gross income than ESTJ - 2X milkers (computed using $12/cwt milk, herd size and mature equivalent milk per cow). However, ESTJ-3X milkers had over $295,000 more total gross income than ISTJ-3X milkers. Using standard industry benchmarks, the difference in potential income between ESTJ-2X and ESTJ-3X based upon Age at First Calving is approximately $19,200. ISTJ-3X would lose about $2,650 compared with ISTJ-2X. This suggests that the personality of successful dairy farmers may be different between those that milk 2X compared with 3X. Comparing each dimension by itself suggests that J-3X milkers had significantly higher days open compared with all other combinations of J-P with milking frequency. Lifetime milk production was significantly higher in T-2X compared with F-2X,
but was not different between T and F for 3X milkers. Dairy farmers that were E-3X used significantly higher PDM bulls as sires. Based upon the strength of the preference score (i.e. how strongly a person tended to be toward the extreme of a dimension), E-I was the most important dimension with I's being more associated with higher production than E's, but E's more correlated with reproductive parameters. Personality style can be useful in defining management qualities that may make for a more successful dairy manager.
Impacts
This study is important because the dairy industry is entering a time when managing people and resources is as important as managing cows. It is important that the dairy industry characterize what constitutes a good manager because survival in the dairy industry is extremely difficult. I'm trying to determine what traits/characteristics define a good dairy manager so that those traits can be strengthened in the future and hence improve the economic viability of those surviving dairies.
Publications
- No publications reported this period
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Progress 01/01/98 to 12/31/98
Outputs
I have been developing a profile of what constitutes a 'good manager' as defined by looking at the correlation between Myers-Briggs Type Indicator preferences and farm financial income. The instrument has been given to 11 dairy producers, plus 3 wives whose complete financial records are collected through the Bridgerland Applied Technology Center. Preliminary analysis shows that the composite profile for the 3 women (mean age = 39) is ESFJ (100% are E, S, and J while 75% are F). The composite for the dairy farmers (all males, mean age = 48) was I(E)STJ (45% were E, 92% S, 75% T, and 83% J). Correlation of the MBTI Preference Score (value which denotes the strength of a person's preference) with net income on the whole farm showed that the correlation for the four dimensions was: E-I, r = -0.33; S-N, r = -0.59; T-F, r = -0.44; and J-P, r = -0.33. On a per cwt milk basis, the highest correlation was the J-P dimension (r = 0.27). The correlation between the net return
using all expenses and the T-F dimension was essentially r = 0, yet increased to r = 0.27 when net return was computed using only the operating expenses. A similar correlation for this dimension was seen on a per cow basis. On a per cow basis, the highest correlation with total net income was the E-I dimension (r = -0.36). The correlation between the net return using all expenses and the J-P dimension was r = -0.15, yet increased (r = -0.41) when net return was computed using only the operating expenses. Although not significantly different, average net income on a whole herd basis, cwt milk basis, or per cow basis were higher in dairy producers who had the E preference than I preference. Preliminary conclusions suggest that farmers with a stronger preference (more extreme) of the dimensions have lower net income than those that tend toward an equal balance between the two extremes. This was most pronounced in the S-N group. One explanation is that those farmers who have the ability
to focus on the future possibilities as well as the present facts have a tendency toward more income compared to those who focus on just the present facts. This can have implications for developing programs to strengthen those areas.
Impacts
(N/A)
Publications
- No publications reported this period
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