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Characterization of Pasture-Based Dairy Farms in Florida and Georgia

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Title:
Characterization of Pasture-Based Dairy Farms in Florida and Georgia
Physical Description:
1 online resource (111 p.)
Language:
english
Creator:
Du, Fei
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Animal Sciences
Committee Chair:
De Vries, Albert
Committee Members:
Staples, Charles R
Newman, Yoana Cecilia
Young, Linda

Subjects

Subjects / Keywords:
based -- pasture -- survey
Animal Sciences -- Dissertations, Academic -- UF
Genre:
Animal Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
The management practices and results of pasture-based dairy farms in the Southeast appear to vary widely, and have not been described fully. The objective of this study was to characterize pasture-based dairy farms in Florida and Georgia.  An 18-page survey was designed and consisted of 62 questions covering 7 areas. Data were collected by personal interviews from September 2012 to March 2013. Respondents were asked to answer the questions in reference to summer 2011 through spring 2012. Twenty-three farms participated with 18 in Florida and 5 in Georgia. The dominant breed was pure bred Holstein on 17 farms, representing 71% of all cows. Milk production in the winter was 26.5 ± 6.6 kg/cow/day and in the summer 19.0 ± 6.8 kg/cow/day. Somatic cell counts were 249,826 ± 68,894 and 368,130 ± 79,085 cells/ml in the winter and summer, respectively.  Three farms employed 100% seasonal breeding. The greatest number of calvings were reported for October (11 farms) while 16 farms reported their fewest calvings during the summer. Non-breeding periods were reported by 17 farms. Summer breeding was avoided due to increased risk of calving problems (10 farms).  A total of 26 different grasses and forages were used. During the summer, all 23 farms grew warm-season perennial grasses. During the winter, 18 farms grew cool-season annual grasses. Oat and annual ryegrass were the most popular winter annual forages. The most popular summer perennial grasses were bermudagrass and bahiagrass.  For lactating cows the average total DMI was 17.7 ± 4.9 and 20.0 ± 4.0 kg/cow/day in the summer and winter, respectively.  In conclusion, management practices and production results varied greatly among farms but were similar to results for grazing dairy farms. Future studies may focus on describing the financial performance of pasture-based dairy farms and the association with management practices.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Fei Du.
Thesis:
Thesis (M.S.)--University of Florida, 2013.
Local:
Adviser: De Vries, Albert.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0046020:00001

MISSING IMAGE

Material Information

Title:
Characterization of Pasture-Based Dairy Farms in Florida and Georgia
Physical Description:
1 online resource (111 p.)
Language:
english
Creator:
Du, Fei
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Animal Sciences
Committee Chair:
De Vries, Albert
Committee Members:
Staples, Charles R
Newman, Yoana Cecilia
Young, Linda

Subjects

Subjects / Keywords:
based -- pasture -- survey
Animal Sciences -- Dissertations, Academic -- UF
Genre:
Animal Sciences thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
The management practices and results of pasture-based dairy farms in the Southeast appear to vary widely, and have not been described fully. The objective of this study was to characterize pasture-based dairy farms in Florida and Georgia.  An 18-page survey was designed and consisted of 62 questions covering 7 areas. Data were collected by personal interviews from September 2012 to March 2013. Respondents were asked to answer the questions in reference to summer 2011 through spring 2012. Twenty-three farms participated with 18 in Florida and 5 in Georgia. The dominant breed was pure bred Holstein on 17 farms, representing 71% of all cows. Milk production in the winter was 26.5 ± 6.6 kg/cow/day and in the summer 19.0 ± 6.8 kg/cow/day. Somatic cell counts were 249,826 ± 68,894 and 368,130 ± 79,085 cells/ml in the winter and summer, respectively.  Three farms employed 100% seasonal breeding. The greatest number of calvings were reported for October (11 farms) while 16 farms reported their fewest calvings during the summer. Non-breeding periods were reported by 17 farms. Summer breeding was avoided due to increased risk of calving problems (10 farms).  A total of 26 different grasses and forages were used. During the summer, all 23 farms grew warm-season perennial grasses. During the winter, 18 farms grew cool-season annual grasses. Oat and annual ryegrass were the most popular winter annual forages. The most popular summer perennial grasses were bermudagrass and bahiagrass.  For lactating cows the average total DMI was 17.7 ± 4.9 and 20.0 ± 4.0 kg/cow/day in the summer and winter, respectively.  In conclusion, management practices and production results varied greatly among farms but were similar to results for grazing dairy farms. Future studies may focus on describing the financial performance of pasture-based dairy farms and the association with management practices.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Fei Du.
Thesis:
Thesis (M.S.)--University of Florida, 2013.
Local:
Adviser: De Vries, Albert.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0046020:00001


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1 CHARACTERIZATION OF PASTURE BASED DAIRY FARMS IN FLORIDA AND GEORGIA By FEI DU A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2013

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2 2013 Fei Du

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3 To my parents

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4 ACKNOWLEDGMENTS The road traveled to complete my master s is neither easy nor short. It is only through the assistance, encouragement, a nd guidance of numerous people that it has been completed. All these individuals have provided me with the drive and the ability to complete my work. I would like to express my gratitude to all those who have contributed to both my study and experience her e at Florida. First of all, I would like to express my deep appreciation to my major advisor, Dr. Albert De Vries for his guidance, and trust. It has been a tremendous honor for me to work with him over the last 2 years. In addition to the excellent mentor ing, his confidence, enthusiasm and critical thinking for scientific questions had a great influence on me. His wisdom and dedication have led me in the right way, bolstered me through difficult times and served as a model that I hope to be one day. Thank you for being patient with me. I have not only learned knowledge from him, but also independence, confidence, preciseness and how to be a scientist. Next, I would like to thank Dr. Yoana Newman for serving on my committee. Her contribution to my research and advice on many issues has been invaluable. I really enjoyed every talk we had. I learned a lot of crop plant knowledge that I had not learn ed from class. I really appreciate all the effort she put on my survey and really thank for her help on introduci ng farms to me. My appreciation also goes to Dr. Charles Staples and Dr. Linda Young for serving on my committee. I am impressed with their friendship, willingness to help, and all the great suggestions during my committee meetings and every communicatio n. Without Dr. Linda Young, I could not get my minor in statistic. Without Dr. Staples, I

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5 would not have the confidence to deal with the feeding management analysis. He also gave me lot of suggestions on the survey. I also would like to thank Dr. Mary Sowe rby for assistance on contacting the farms. Without her help, my survey would have never been completed. She provided me with all the contact information and dedicated assistance on my survey project. I also wish to express my appreciation to Dr. Mike Hut jens from University of Illinois, Dr. John Bernard and Dr. Curt Lacy from University of Georgia. They gave me a lot of valuable suggestions on the survey conducting process. I wish to thank all the farmers who volunteered involved in the survey. Without their cooperation, I could not finish my survey. I also would like to thank Sustainable Agriculture Research & Education (SARE). They provided the funds to help me finish my study. I am very grateful to have stayed in th e friendly laboratory in the last 2 year and glad to have met Karun Kaniyamattam and Keegan Gay. Specially Keegan Gay, he gave me a lot of help when we conducted the survey. In addition, he also went to the farms to help me to collect data for the survey. During the last 2 years, Keegan and Karun gave me so much help to improve my dairy knowledge. They are not only colleagues, but also my best friends and good brothers. In addition, I wish to thank Dr. Tao Sha. He gave me lots of advice not only on my study, but also on my regular life as a big brother. I would also like to thank all the friends and colleagues I met while at Florida. They will remain with me in fond memory and I do hope that one day I will have the opportunity to collaborate with them again. Their support and friendship is in valuable.

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6 Finally, I would like to thank my wise and lovely mother, Li He, and my virtuous father, Yuqing Du for their support s throughout my entire life Whenever where I am they always stand behind me, look a fter me, love me and trust me. Without their encouragement, I could not be here and pursue my dream. Moreover, I would also like to express my appreciation to all my friends who are either in Tennessee or in China. Without their company, I could not bolster through all my hard times. All of them nev er let me feel lonely on the way to my dream. I love you all.

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7 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 2 LITERATURE REVIEW ................................ ................................ .......................... 16 2.1 Definition of Pasture based Dairy Farms ................................ .......................... 16 2.2 Motivation for Pasture Base d Dairy Farming ................................ .................... 17 2.3 Milk Production and Dry Matter Intake ................................ .............................. 20 2.4 Milk Quality ................................ ................................ ................................ ....... 22 2.5 Reproduction ................................ ................................ ................................ .... 23 2.6 Cattle Breeds on Pasture Based Dairy Farms ................................ .................. 25 2.7 Forage and Feed ................................ ................................ .............................. 27 2.8 Cost of Production and Profitability of Pasture Based Dairy Farms .................. 31 3 MATERIALS AND METHODS ................................ ................................ ................ 37 3.1 Survey Design and Data Collection ................................ ................................ .. 37 3.2 Statistical Analysis ................................ ................................ ............................ 38 4 CHARACTERIZATION OF FARM STRUCTURE, MILK PRODUCTION, REPRODUCTION AND FACILITIES ................................ ................................ ...... 40 4.1 Dairy Farms Business Structure ................................ ................................ ....... 40 4.2 Description of Dairy Breed, Replacemen t and Genetic Selection Goals ........... 41 4.3 Milk Production and Procedures ................................ ................................ ....... 42 4.4 Reproduction Programs ................................ ................................ .................... 44 4.5 Facilities and Time Budgets ................................ ................................ .............. 48 5 CHARACTERIZATION OF PASTURE AND FEEDING MANAGEMENT ................ 61 5 .1 Land Use ................................ ................................ ................................ .......... 61 5.2 Pasture Utilization ................................ ................................ ............................. 62 5.3 Grass and Forage Species Grown on Farms ................................ .................... 64

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8 5.4 Insect and Weed Control, and Fertilization ................................ ....................... 67 5.5 Dairy Cows Nutrition and Feed Intake ................................ .............................. 69 5.6 Elect ricity Use and Future Outlook ................................ ................................ .... 71 6 DISCUSSION ................................ ................................ ................................ ......... 79 6.1 Survey Participation ................................ ................................ .......................... 79 6.2 Cattle Breeds, Genetic Selection, and Culling ................................ .................. 80 6.3 Milk Production and Milk Quality ................................ ................................ ....... 81 6.4 Reproduction ................................ ................................ ................................ .... 82 6.5 Feed Intake ................................ ................................ ................................ ....... 83 6.6 Electricity Consumption and Future Prospects ................................ ................. 84 7 CONCLUSIONS ................................ ................................ ................................ ..... 85 APPENDIX: SURVEY ................................ ................................ ................................ ... 86 LIST OF REFERENCES ................................ ................................ ............................. 104 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 111

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9 LIST OF TABLES Table page 2 1 Monthly milk production per cow in Florida (USDA NASS, 2011) ....................... 33 2 2 Production costs for rotational grazing system and non rotational grazing system dairy farms reported in the 2007 Census of Agriculture (Paine, 2013) ... 33 4 1 Business structure of the 23 surveyed dairy farms in three regions ................... 52 4 2 Description of adult dairy cattle breeds and cull rates ................................ ........ 52 4 3 Genetic selection goals ................................ ................................ ...................... 53 4 4 Primary culling reasons ................................ ................................ ...................... 53 4 5 Milking procedures ................................ ................................ ............................. 54 4 6 Reasons for a reduced or limited insemination period ................................ ........ 54 4 7 Insemination methods ................................ ................................ ........................ 55 4 8. Time budget for lactating cows in each location ................................ ................. 55 4 9 Cooling systems used on pasture and while cows are grouped for milking and supplementation ................................ ................................ .......................... 56 4 10 Cooling system at holding area and milking parlor ................................ ............. 56 5 1 Ways the paddocks were laid out ................................ ................................ ....... 73 5 2 Types of grasses and forages used on farms ................................ ..................... 7 4 5 3 Crop sequence applied on farms ................................ ................................ ........ 75 5 4 Irrigation methods ................................ ................................ ............................... 75 5 5 Considerations for balancing feed rations ................................ .......................... 75 5 6 Major limitations for growth of the farm ................................ ............................... 76

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10 LIST OF FIGURES Figu re page 2 1 Monthly milk production in Florida (figure from Tao and Dahl, 2013) ................. 34 2 2 Daily milk production (lbs) pe r lactation cow in Florida and Georgia (figure from DRMS 2013) ................................ ................................ ............................... 35 2 3 Average test day milk yield and SCC from Dairy Herd Improvement herds during 2012 by month (Norman et al., 2013) ................................ ...................... 36 4 1 Average daily milk production in the winter and summer seasons ..................... 57 4 2 Somatic cell counts in the winter and summer seasons ................................ ..... 57 4 3 Heifers calving pattern ................................ ................................ ........................ 58 4 4 Cows calving pattern ................................ ................................ .......................... 59 4 5 Cows insemination pattern ................................ ................................ ................. 60 5 1 Land usage in each farm ................................ ................................ .................... 77 5 2 Stocking density and pasture rest period (days) ................................ ................. 77 5 3 Total dry matter intake for lactating cows in winter ................................ ............. 78 5 4 Total dry matter in take for lactating cows in the summer ................................ ... 78

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11 LIST OF ABBREVIATIONS AI Artificial insemination DMI Dry matter intake EAI Estrus based artificial insemination NS Natural s ervice SCC Somatic cell count SD Standard deviation TAI Timed artificial insemination

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12 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science CHARACTERIZATION OF PASTURE BASED DAIRY FARMS IN FLORIDA AND GEORGIA By Fei Du August 2013 Chair: Alb ert De Vries Major: Animal Sciences The management practices and results of pasture based dairy farms in the Southeast appear to vary widely, and have not been described fully The objective of this study was to characterize pasture based dairy farms in F lorida and Georgia. An 18 page survey was designed and consisted of 62 questions covering 7 areas. Data were collected by personal interviews from September 2012 to March 2013. Respondents were asked to answer the questions in reference to summer 2011 thr ough spring 2012. Twenty three farms participated with 18 in Florida and 5 in Georgia The dominant breed was pure bred Holstein on 17 farms, representing 71% of all cows. Milk production in the winter was 26.5 6.6 kg/cow/day and in the summer 19.0 6.8 kg/cow/day. Somatic cell counts were 249,826 68,894 and 368,130 79,085 cells/m l in the winter and summer, respectively. Three farms employed 100% seasonal breeding. The greatest number of calvings were reported for October (11 farms) while 16 farms re ported the ir fewest calvings during the summer. Non breeding periods were reported by 17 farms. Summer breeding was avoided due to increased risk of calving problems (10 farms).

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13 A total of 26 different grasses and forages were used. During the summer all 23 farms grew warm season perennial grasses. During the winter 18 farms grew cool season annual grasses. Oat and annual ryegrass were the most popular winter annual forages The most popular summer perennial grasses were b ermuda grass and b ahia grass For lactating cows the average total DMI was 17.7 4.9 and 20.0 4.0 kg/cow/day in the summer and winter respectively. In conclusion, m anagement practices and production results varied greatly among farms but were similar to results for grazing dairy farms Future stud ies may focus on describing the financial performance of pasture based dairy farms and the association with management practices.

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14 CHAPTER 1 INTRODUCTIO N Grazing or pasture based dairy farms are used successfully in many countries such as Ne w Zealand Australia and western Europe In the United States, grazing systems are gaining popularity in the e ast, m idwest, and n orth w est regions of the U.S. where cool and moist climates are favorable to this type of dairy production. These regions allow for abundant grass growth during at least part of the year and heat stress is limited. Traditionally, most dairy farms in the southeastern United States confine cows to barns or pasture lots year round and feed stored forages and concentrated feeds (Font aneli et al. 2005). Although cattle might be housed outside, often much of the feed is purchased. However, the cost of purchased feed and fuel has risen rapidly in the last 5 years ( USDA NASS, 2011). In addition, a significant amount of capital is tied up in buildings, machinery and manure management systems on the farms. Moreover, a boost in demand for green natural and organic milk has led to an increased interest in grazing systems (Gillespie et al., 2009). For these reasons, many dairy farmers have shown an interest in or started transitioning to pasture based dairy systems (Ricks and Hardee, 2012). The management practices and production results of pasture based dairy farms in the S outheast appear to vary widely however (Mac o on et al. 2011) a nd have not been described. Characterization of pasture based dairy farms in the Southeast may be useful to aid farm advisors and to illicit profitable and sustainable practices.

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15 The objective of this study was to characterize pasture based dairy farms in Florida and Georgia with regards to production indices such as milk production reproduction, facilities, nutrition and pasture management. The layout of this thesis is as follows. Chapter 2 is a review of the literature regarding the characterization of pasture based dairy farms in Florida and Georgia. Chapter 3 describes the materials and methods of a survey that was conducted among pasture based dairy farms in Florida and Georgia. In Chapter 4, the results of the survey regarding milk production, repr oduction and facilities are described. Chapter 5 continues the description of the results with a focus on pasture management and nutrition. Chapter s 6 and 7 present the discussion and conclusion s respectively The study is part of the Southeast Susta inable Dairy Farms Project, which is funded by Sustainable Agriculture Research and Education (SARE) as LS11 243 Improving the Welfare of Southeastern Dairy Families Through the Adoption of Sustainable Production Systems (principal investigator R. C. Lacy, University of Georgia).

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16 CHAPTER 2 LITERATURE REVIEW Currently, there are approximately 119 ,000 dairy cows and 70,000 dairy cows, respectively in Florida and Georgia ( USDA NASS 2012) These cows are kept on 130 dairy farms in Florida and twice that many in Georgia. The National Animal Health Monitoring System ( USDA NAHMS 2007) reports grazing as the primary method of operation in over 3% of U S dairy operations. It is not clear how many of the dairy farms in Florida and Georgia house and feed cows on pa sture and to what extent. Production practices regarding milk production, reproduction, nutrition, facilities and pasture management appear to vary widely. The objective of this review is to describe important characteristics of pasture based dairy farms a s found in the literature and describe voids in the description of the characteristics of pasture based dairy farms in Florida and Georgia. 2. 1 Definition of Pasture b ased Dairy Farms A pasture is a forage crop delimited by fencing ( Y. C. Newman personal communication). Alternatively, p asture is defined as an area covered with grass or other plants used or suitable t o grazing by livestock (Dictionary.com). It is difficult to find a clear consensus on a specific definition of pasture based dairying in the l iterature however Taylor and Foltz (2006) defined pasture based dair y farms as either farms using a or Rotational grazing operation s use pasture as the pr imary forage source during the grazing period wh ereas mixed feed operation s obtain part of their dietary forage from pasture but rely primarily on stored forages

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17 In selecting a sample of Pennsylvania dairy farms for a survey of grazers Hanson et al. (19 98) required that the animals obtain 40% of their forage needs during the summer months from pasture. Dartt et al. (1999) defined rotational grazing system as one where 1) at least 25% of the annual forage requirement c ome s from the pasture and 2) the ani mals graze for at least 4 months every year Smith and Ely (1997) defined pasture housing as one in which cow s are housed outside and obtain a portion of their feed by grazing. Collectively, one can conclude that grazing refers to cows actively harvesting and feed ing on grasses and herbage. In addition, d airy cattle can be housed on pasture but most of their f orage intake can be from stored feed Thus in this thesis pasture based refers to farms that at least partially, or temporarily, house (some of) the ir dairy cattle on pastures. The opposite of pasture based dairy farms is confinement based dairy farming, which is characterized by feeding stored feeds to dairy cattle and housing cattle inside in barns or dry lots. 2. 2 Motivation for Pasture Based Da iry Farming Keeping lactating dairy cows on pasture is not a new method. Prior to the 1940s and before rural electrification, the vast majority of dairy farms in the United States u sed grazing management systems (Gay, 2012). Grazing has been advocated, aba ndoned, and now is being advanced again as an alternative feeding system, particularly in the northeastern United States (Hanson et al., 1998). During the 1980s, the rising expense s of machinery and housing, and reduced profitability (Parker et al., 1992) began to make pasture systems appear to be more attractive.

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18 Purchased feed costs have increased significantly in the last 7 years. USDA ERS (2013) reported feed cost for dairy farms in Florida of $ 19.78 /100 kg of milk in 2006 and $ 30.13 /100 kg of milk in 2012. B ecause of th is increase in feed cost, more farms consider growing more of their own feed, including grass for cows to graze. In addition, as the consumer desires more healthy food, the demand for green natural and organic milk has increased. M any farmers have shown an interest in or started to return to pasture based dairy farm ing as a means to gain economic efficiencies (Gillespie et al., 2009). More farmers in many temperate and subtropical regions of the world have shown an interest in o r started to return to pasture based dairy management system s as an alternative to confined systems (Macdonald et al., 2008) because of 1) reductions in milk price, 2) increments in production costs (Dillon et al., 2005) and 3) perceived environmental and animal welfare concerns associated with intensive (confined) dairying (Dillon 2006). Fontaneli et al. ( 2005 ) listed several reasons for an increasing interest in grazing 20 years ago in Florida including 1) lower cost of feed, equipment and labor potenti ally resulting in greater profitability per cow, 2) easier manage ment of manure nutrients and fertilizer and less potential environment hazard s 3) reported improvements in animal health and reproduction (less culling), and 4) improved quality of life for owners and managers (less stress, more leisure time, etc.) P asture based dairy systems often are promoted as a socially and environmentally sustainable production model for North American dairy farms (USDA NRCS, 2007). Moreover, this system has t h e potent ial to improve profitability of dairy operations of all sizes by maximizing the utilization of fresh pasture and focusing on reducing production costs (Paine, 201 3 ).

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19 Grazing or pasture based systems have been utilized very successfully in many countries su ch as New Zealand, Australia, Ireland, the Netherlands and the United Kingdom. In the United States, grazing systems have become m ore common again in the e astern, m idwestern, and n orth w estern Pacific Coast regions of the country where climates are favorab le to this type of production system due to abundant grass growth at least part of the season, and moderate climates, although the favorable grazing seasons are not the same in the North as in the South (Gay, 2012). In the 2000 and 2005 dairy versions of t he Agricultural Resource Management Survey farmers were In 2000 and 2005, 68.5 and 64.5 % of farmers respectively, indicated such use (Gillespie et al., 2009) I n fact, grazing made up about 3 % of all U.S. dairy operations C ombination grazing and confinement operations represented an additional 31.1% of U.S. dairies ( USDA APHIS 2007 and NAHMS 2007 ) Grazing is not a feasible practice year round in many place s due to the harsh winters, summers, or the limitations to the grass growing season. Therefore, many farmers u se a combination system and apply grazing while the grass is available and feed ing total mixed rations or silage and balage during the non grazing season. The climate of the southeast ern United States is typical of a humid subtropical climate. Especially in areas along the Gulf of Mexico and south ern Atlantic coast, the summers are hot of long duration and are normally the period of greatest rain fall. Summer heat stress has been recognized as reducing both the productivity and reproductive efficiency of dairy cattle in the southeastern and southwestern portions of the United States (Jordan, 2003). In the other hand t he shorter cool season also al lows

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20 for extended time of growth of forages and grasses. In short, the seasonal effects of heat stress have a large impact on pasture based dairying in Florida and Georgia. 2. 3 Milk Production and Dry Matter Intake According to the Florida livestock dairy and poultry summary (USDA NASS 2011), the national average milk production in 2011 was 9,702 kg / cow / year with 3.71% milk fat. In Florida, the average milk production was 8,667 kg/ cow with 3.67% milk fat; in Georgia, the average milk production was 8,343 kg / cow / year with 3.71% milk fat (USDA NASS 2012 ). These averages contain both pasture based and conventional dairy farms. In Florida and Georgia, milk production show s a significant seasonal pattern. From May to September, milk production decline s as air temperatures r ise (Figure 2 1) ( Tao and Dahl, 2013 ).These results are in agreement with the data from USDA NASS (2011) USDA NASS (2011) reported that the l east productive month in Flo rida was September (Table 2 1) In 2011 the lowest production was 591 kg / cow in September and the highest production was 846 kg/ cow in March These results agree with F igure 2 2 which shows 12 months of milk production of all milk cows in the Dairy Records Management Systems (DRMS 2013 ) dataset. M ilk yield per cow decreased from May to September T he lowest milk production per cow was in September o nly 24 .7 kg / day There was large difference between milk production in the cool er winter compared to the hot and humid summer Hahn and Osburn (1969) calculated that cows produci ng 32 kg of milk per day and living below a line drawn approximately through mid Missouri, diagonally through Tennessee, and northern Georgia would lose approximately 180 kg of production from

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21 June 1 to September 30, increasing gradually to 270 kg moving s outh to Florida and southern Alabama. Extended periods of high ambient temperature and relative humidity decrease the appetite of cows and then reduce the foraging and dry matter intake (DMI), (Hansen and Archiga, 1999). Various s tudies have shown that t here is a significant negative correlation between temperature humidity index (THI), a measure of heat stress, and DMI for cows in the southeastern United States (Holter et al., 1996; Holter et al., 1997). Heat stress occurs when THI is greater than approx imately 72 (West, 2003) West (2003 Ingraham et al. (1979) estimated that milk declined by 0.2 kg per unit increase in THI when THI exceeded 72. Secondly, different breeds show different effect s of heat stress on milk yield (Bianca, 1965) In short, heat stress has been observed to cause reductions in milk production up to 25% (Thatcher et al., 1974; Roman Ponce et al. 1977). Milk production in pasture based dairy farms is on average lower than in conventional dairy farms. The overall rolling herd average among all breeds on grazing dairy farms in Wisconsin was 7,005 kg / cow / year (Paine, 2013). A survey of 90 grazing dairy farms located primarily in the eastern U.S. indicated that the mean milk yield was 21. 3 5.1 kg / cow / day (Gay, 2012). Both are significantly below the national average. Cows in free stall housing produced 19% more milk (29.8 v s. 25.1 kg/ d ay ) over the duration of a 259 day study than cows managed on pasture (Fontaneli et al 2005) in Florida. During a 4 w ee k experimental period in Pennsylvania, Kolver and Muller

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22 (1998) reported that cows consuming an all pasture diet produced 3 3% less milk than cows feed a total mixed rations (29.6 vs. 44.1 kg/ d ay ), and had a milk protein concentration that was 0.19 percentage units lower. Fike et al. (1997) reported a large decrease (10 to 15 kg/ d ay ) in milk yield for cows moved from a confined housing system to Bermuda grass or rhizome peanut ( Arachis glabrata Benth. ) pasture in Florida in midsummer. White et al. (2001) reported that Holstein s produced more milk in confinement management (36.7 vs. 27.5 kg), but Jersey s produced more milk on pas ture (24.8 vs. 23.6 kg) in North Carolina between confinement and pasture based dairy cows. Because pasture based dairy farms are less able to cool cows while they graze, the seasonality of milk production is expected to be greater than on conventional far ms. However other than the experimental data shown, no data or studies were found that describe the milk production and the seasonality of milk production on pasture based dairy farms in the Southeast especially in Florida and Georgia. 2. 4 Milk Q uality Mi lk Somatic cell c ount (SCC) is a long established measure of milk quality (Eberhart et at. 1982). Norman et al. (2001) reported that SCC in Dairy Herd Improvement herds during 1996 and 1997 was lower during October through January than during July and Aug ust There was a negative relationship between milk yield and SCC throughout the year ( Figure 2 3 ). T he average SCC in the United States in 2012 was 200,000 cells/m l whereas i n Florida and Georgia the mean SCC was 267,000 cells/m l and 280,000 cells/m l res pectively (Norman et al., 2013). y farms in the U.S., the SCC was on average 237,320 141,050 cells/m l in the summer, and 233,420 108,190 cells/m l in the winter. The mean SCC was 236,850 125,030 cells/m l

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23 In the White et al. (2001) study in North Carolina, the SCC was always higher on pasture than in confinement both for Holsteins and Jerseys (Holstein s 453,100 vs. 71,000 cells/ml; Jersey s 276,000 vs. 132,800 cells/ml). I n Florida, the mean SCC was greater in co w s kept on pasture 24 hours per day compared to those moved on 2 different pasture s only at night (654,000 vs. 223,000 and 364,000) (Fontaneli et al. 2005). Fike et al (2002) reported that the SCC is higher in continuous pasture based systems than i n pasture based systems where cows are housed at night. It is not clear what the current milk quality is on pasture based farm s in Florida and Georgia. 2. 5 Reproduction Several studies have shown that reproductive performance in dairy herds has declined s ince the 1950s in the United States. Washburn et al. (2002b) found an increase in days to first service from 84 to 100 between 1985 and 1999 in Holstein herds in 10 Southeastern states that were on continuous DHIA test during that period. De Vries and Risc o (2005) also found that days to first service increased from a low of 84 d ays in 1983 to 104 d ays in 2001 in Florida and Georgia. In Kentucky, conception rates in herds enrolled in DHIA decreased from 62% in 1972 to 34% in 1996 (Silvia, 1998). Annual pre gnancy rates for 71 to 364 d since last calving decreased from 21.6% in 1977 to 1979, to 12% in 2000 to 2002 in Florida and Georgia (De Vries and Risco, 2005). P regnancy rates were lower during summer (9.0%) than that during winter (17.9%) (De Vries and Ri sco 2005). Al Katanani et al. (1999 ) also reported significant seasonality in reproductive performance in the southeastern United States Oseni et al. (2003) showed that days open increased during summer in most U.S. states, include

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24 Florida and Georgia. I n heat stressed cows the duration and intensity of estrus is reduced ( Gwazdauskas et al., 1981; Younas et al., 1993 ). In summer, the conception rate s can decrease by 15 % to 2 0% compared to the winter (Hansen, 2008) Cartmill et al. (2001) reported that wh e and conception rate was lower. Similarly, Ingraham et al (1976) reported that conception rate declined from 66% to 35% when THI increased from 68 to 72 before breeding. All these studies showed tha t heat stress during the summer affect s conception rate and cause s poor fertility. However, reproductive performance in pasture based dairy farms in Florida and Georgia is unclear Artificial insemination (AI) and n atural s ervice bulls (NS) are both widely used in the U .S. (NAHMS, 2007) To overcome problems associated with estrus detection, many dair y farms use bulls, either immediately after the voluntary waiting period, or after an unsuccessful AI period. According to De Vries et al. (2005), in Florida a nd Georgia, 47.3% of the 488 herds reported the consistent use of one type of breeding system between 1994 and 2002 : 11.1% reported AI, 20.1% reported NS, and 16.2% reported a combination of both (mixed). The remaining 52.7% of herds reported changes in br eeding system s between 1994 and 2002. The reproductive performances were different among the three types of breeding systems in summer and winter During the summer, pregnancy rate for AI herds was slightly less than that for mixed and NS herds. During the winter, pregnancy rates for AI herds did not differ from that for mixed and NS herds Bela et al. (1995) reported that cows on pastures have fewer service s per conception and shorter calving intervals. B reeding systems used on pasture based dairy farms in Florida and Georgia are not characterized however.

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25 Summer heat stress likely leads to seasonal calving pattern in the Southeast. It is not clear to which extend dairy farmers in Florida and Georgia attempt to inseminate cattle year round, or are aiming to have a seasonal calving pattern deliberately, for example by not inseminating cattle during part of the year. 2. 6 Cattle Br eeds on Pasture Based Dairy Farms Holsteins are the predominant dairy breed in the U .S. (NAHMS, 2007) Approximately 95% percent of operations with dairy cows had at least one Holstein cow, and Holsteins represented around 90% of all cows. Although 18% of operations reported having Jerseys on hand, only 5.3% of all cows were Jerseys. Other breeds, which also included cross breed cat tle, were present on 21% of operations (NAHMS, 2007). based dairy farms, he reported that Holstein some Holstein genetics. The second mo st dominant breed was Jersey, which was present on 69% of the surveyed farms (Gay, 2012). The Cornell Dairy Farm Business Summary (DFBS, 2008) for intensive grazing farms indicated that Holstein was the most common breed with 11 out of 26 farms in New York having 95% Holstein s The second most common was crossbreeds (DFBS, 2008). In a dairy grazing practice s survey in Wisconsin, 62% were Holstein based herd s with Jerseys based herds comprising 12 % of all grazing herds C ross breeds were 17% of all herds ( Paine, 2013). Regarding the reasons for predominance of Holsteins, Washburn et al. (2002b) reported that the Holstein had a much better milk yield compared with Jersey. In the White et al. (2001) study of confinement and pasture based dairy cows in North

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26 Carolina Holstein s had a much greater milk yield both i n confinement and pasture system s than Jerseys. Another factor is the tolerance to relative humidity the milk yield of Holstein, Jersey and Brown Swiss cows was 97, 93, and 98% of normal; but when the relative humidity was increased to 90%, the milk yields were 69, 75, and 83% of normal (B ianca, 1965). Thus, Holsteins appeared to be the least heat tolerant although they also produced the most milk. Another factor is differences in reproductive performance. Washburn et al (2002a) reported that Jerseys had higher conception rates and higher p ercentages of cows pregnant in 75 days than Holstein. Vibart et al. (2012) reported tha t Holstein x Jersey crossbreed s has the greatest first service conception rate and percent pregnant by 90 d ays after calving among Holstein and Jersey s T he ideal cow wi ll not be the same for all pasture based dairy farms (Probert, 2013). The most important trait considered by pasture based dairy farmers is reproducti ve efficiency O ther traits considered by many pasture based producers were body size, udder health, prod uctive life, feet and legs (Probert, 2013). Gay (2012) reported that the most desired traits for grazing dairy farmers from highest to low, were productive life, udder composite, feet and leg composite, and fat percentage. Also, he found that the actual s election habits, from highest to low, were udder composite, productive life, feet and leg composite, and calving ability. He reported that the actual importance of SCC drastically dropped when compared to the desired importance in a grazing environment bec ause grazing dairy farmers already have a low SCC.

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27 A pproximately 34% of all cows are culled each year (De Vries et al. 2009). They reported that In a nother study (Pinedo et al., 2010) c ulling reasons were died (20.6%), reproduction (17.7%), injury or other (14.3%), mastitis (12.1%) and grazing and non grazing dairy farm wer e fertility, high SCC low production, feet and legs problem s and old age. 2. 7 Forage and Feed The subtropical climate is a double edged sword for forages in Florida and Georgia. It provides a longer growth period for grasses. On the other hand, it affec ts the quality of forages. In intensive pasture based dairy management system s cows are out on pasture almost year round and are given little grain concentrate supplement. The U.S. grazing season ranges from 4 to 5 months in Wisconsin to year round in the Southeast (Gillespie et al. 2009). In Florida and Georgia, forage growth is the highest in the summer and the lowest in spring and fall (Staples et al., 1994). Often cool season annual grasses (ryegrasses or small grains) are grown in the winter. Even aft er using different forages throughout the year, pasture based dairy farms in Florida and Georgia cannot keep up with the forage requirements for cows. There is still a 45 to 90 day gap in the late fall or early winter season when perennial grass yield s are low and the ryegrass or small grain production has not started yet (Stap l e s et al., 1994). Stored or purchased forage or feed is necessary at that time. For the Southeast, Fike (1999) reported that Bermuda grass is one of the most extensively grown impro ved, perennial, warm season forages. Bermuda grasses

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28 occupy more than half of the pasture acreage in the southern U .S. (Adams, 1992). There are many other grasses and legume s which can be grow n on pasture s in the S outheast also, such as warm season perenni al Bahia grass ( Paspalum notatum ), warm season annual pearl millet ( Pennisetum glaucum ), and cool season annual o at ( Avena sativa ). In a survey of Florida dairy farmers, Staples et al. ( 1997 ) reported that 8 out of 48 dairy farmers stated that they were in terested in trying new forage s in the near future, such as Tifton 78 b ermuda grass was mentioned the most often in their survey Staples et al. (1997) a lso indicated that the most common forages were small grains, Callie Bermuda, and Pearl Millet at that mo ment. Fontaneli et al. (2001) reported that rye ( Secale cereal L .), annual ryegrass ( Lolium multiflorum Lam. ), and clovers ( Trifolium sp. ) are valuable forages for winter pasture programs in northern Florida. However, the forage species currently use d by d airy farms in Florida and Georgia are not characterized The perennial, warm season forages adapted to the Southeast are typically of lower nutritive value than either cool season perennials or warm season annuals ( Hoveland, 1996 ). Minson and McLeod (1970) reported that tropical grasses were an average 13% less digestible than temperate grasses. But the main factor which determines the level of animal production from pasture is the amount of digestible nutrient s consumed per day. Thus, the high amounts of indigestible fiber on tropical grasses leads to a low daily intake of feed and lower animal production (Stobbs, 1975). In addition, the availability and accessibility of leaf is one of the major factors influencing the quality and quantity of feed consumed by grazing cattle (Stobbs, 1975). Y oung forage usually contains sufficient protein and minerals to meet the requirements

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29 of the animals. Production is limited by the digestible nutrients, which is affected by the fiber content of the feed. As the pasture matures, the fiber content will increase, resulting in a decrease in digestibility and intake (Stobbs, 1975). Also, the protein and mineral content in mature pasture will decrease. When cows graz e on poor quality tropical pasture s they have to draw heavil y from the body reserves and los e weight which affects milk production. Every grazing dairy farmer recognize s the effect of pasture maturity on milk production Thus, as far as practi cal dairy farmers attempted to feed young forage (Stobbs, 1972). This p rinciple also influence s the grazing sequence : fresh cows and milk ing cows go before dry cows on the same pasture. Because so many factors influence the grass and nutrient quality, proper DMI can be a problem for grazing cows or pasture based dairy farm. C ows need approximately 6 to 9 hours a day to graz e The n umber of hours grazed and biting rate is affected by the condition of the pasture (Staples et al., 1994). The relationship was measured using cows grazing ryegrass pastures during the growing season in Scotland (Phillips and Leaver, 1987). The ir study results indicated that in the spring, when forage was lush, cows averaged 60 bites per minute. The grazing time was 8 hours per day and the total intake was about 14.4 kg (31.7 lbs) dry matter per day. I n the fall, when pasture growth was lower, cows took more bites per minute with on average 66 bites. The grazing time was about 9 hours a day and the total intake was about 19.6 kg (43.2 lbs) dry matter per day. That means either extra grain supplements or concentrates must be added to reach the required amount of dry matter intake. S tocking rate also influences forage intake. F orage intake decreased with increasing herd size in one unit area in Wisconsin (Paine, 2013) F orage intake

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30 decreased when supplem ents were fed with forages which have greater nutritive value (Holmes and Jones, 1964; Golding et al., 1976; Arriaga Jordan and Homes, 1986). But the quality of pasture and supplement, the amount s of pasture and supplement fed may affect the response of in take to supplement Waldo (1986) expressed that total dietary DMI is affected very little by forage quality when diets contain ed a 80% of dry matter ) level of concentrate. Feeding of supplements is a common practice, but not all farms apply this efficiently. Supplementation induced a significant increase in milk yield, fat, and protein content and reduced the butter fat level (Delaby and Peyraud, 1997 ). Many studies have clarified that typical responses were approximately 0.3 to 0.4 kg of mil k per kg of supplement fed to cows grazing an adequate tempe rate pasture (Leaver et al., 19 6 8 ; Journet and Demarquilly, 1979; Meijs and Hoekstra, 1984). When cows grazed tropical pasture, a similar response (0.34 kg of milk per kg of supplement) was repor ted (Combellas et at., 1979). Davison et al. (1991) stated similar results but indicated that cows were not adapted to high amounts of supplement (8 kg of dry matter / day ) and abundant herbage resulted in greater than normal substitution effects. These re sponses varied between different lactating periods. With complete lactation studies, Jennings and Holmes (1984) found the range of response to supplement was 0.1 to 1.80 kg of milk/kg of supplement, with an average response of 0.82 kg of milk/kg of supplem ent. Several long 250 days) have shown a linear milk yield increase in response to an increasing supplement rate (Cowan et al., 197 6; Davison et al., 1991 ). But some others reported a curvilinear response (Coulon and Remond, 1991; Delaby an d Peyraud, 1997 ).

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31 Current studies that characterized feeding management on pasture based dairy farms in Florida and Georgia were not found. 2. 8 Cost of Production and Profitability of Pasture Based Dairy Farm s Little information is available about the co st of production and profitability of pasture based dairy farms. Table 2 2 compares the financial performance of non rotational grazing system s and rotational grazing system s from the 2007 Ag Census (USDA Ag census, 2007) Cost of milk production on the rotational grazing system farms was lower at $ 2 730 per cow per year than on the non rotational grazing system farms, which was $ 3 116 per cow per year. Feed cost, labor cost and equipment cost were all lower on rotational grazing system farms than non rotational grazing system farms. However, revenues and profitability were not given. However, the pasture based dairy farm may not be preferred in some cases. Elbehri and Ford (1995) simulated production systems for a 60 cow Pennsylvania dairy farm and fo und that intensive grazing pasture based system s were more profitable than conventional systems if milk production was equal The study also showed that if milk yields for the pasture based system decreased by only 4% to 6%, the intensive grazing system wo uld no longer be preferred. Tucker et al. (2001) evaluated dairy cow performance and profitability on a total mixed rations diet versus rotationa l grazing of annual ryegrass during March to May in M ississippi. Daily milk production declined on the ryegras s diet, but milk income over feed costs was higher. White et al. (2002) conducted a four year experiment with conventional and pasture based systems in North Carolina. The authors concluded that a pasture based

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32 system had the potential to be economically competitive since significant differences in return over feed costs between the systems were not found. The literature review suggests that pasture based dairy farming can be profitable and be competitive with conventional dairy farms. The financial perfor mance of pasture based dairy farms in Florida and Georgia is not documented, however.

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33 Table 2 1. Monthly milk production per cow in Florida (USDA NASS, 2011) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Monthly production per cow (kg) ( kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) 2002 640 606 688 661 658 611 568 540 474 479 499 574 6998 2003 617 586 651 642 629 586 552 508 463 502 536 615 6887 2004 651 642 704 688 692 633 608 556 465 529 568 658 7396 2005 690 670 745 71 3 720 654 597 561 502 515 554 617 7536 2006 674 647 745 699 690 642 613 536 504 538 565 631 7484 2007 667 642 735 722 720 667 636 558 531 527 563 640 7609 2008 704 695 751 712 738 654 624 563 527 556 599 690 7812 2009 754 699 799 776 772 697 658 602 53 6 558 617 701 8170 2010 733 724 826 804 772 726 695 606 570 606 665 754 8481 2011 806 767 847 790 783 740 690 636 590 613 663 740 8665 2012 808 781 847 817 804 735 690 640 568 595 663 717 8665 Table 2 2. Production costs for rotational grazin g system and non rotational grazing system dairy farms reported in the 2007 Census of Agriculture ( Paine, 2013 ) Cost category MiG 1 dairy farms Non MiG 2 dairy farms Percent MiG: Non Hired labor $439 $635 0.69 Feed cost $626 $732 0.86 Equipment rent $114 $129 0.88 Custom work $128 $144 0.89 Chemical cost $89 $99 0.90 Land & facilities rent $178 $194 0.91 Depreciation $415 $444 0.93 Fuel cost $158 $163 0.97 Repairs cost $301 $304 0.99 Fertilizer cost $180 $172 1.04 Utilities cost $104 $98 1.06 Tot al $2730 $3116 0.88 1 MiG = rotational grazing systems as known as management intensive grazing 2 Non MiG = non rotational grazing systems

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34 Figure 2 1. Monthly milk production in Florida ( figure from Tao and Dahl, 2013 )

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35 Figure 2 2 Daily milk production (lbs) in Dairy Herd Improvement herds per lactating cow in Florida and Georgia (figure from DRMS 2013 )

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36 Figure 2 3 Average test day milk yield and SCC from Dairy Herd Improvement herds dur ing 2012 by month (Norman et al., 201 3 )

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37 CHAPTER 3 MATERIALS AND METHODS 3.1 Survey Design and D ata C ollection An 18 page survey was designed and consisted of 62 questions which included 26 short answer questions, 17 multiple answer questions, 17 fill in the blank questions and 2 questions for which respondents were asked to give their top 3 reasons (see Appendix). The survey covered 7 areas which included farm business structure, young stock management, milking herd management, pasture and crop management feeding management, manure and nutrient management and environment and sustainability. The survey focused on pasture based dairy farm s where pasture based refers to farms that at least partially, or temporarily, house some or all of their dairy cattle o n pastures The survey form was designed to be filled out by interviewers who were vising the participating dairy farms. Reasons for personal visits were that a higher survey completion rate was expected given the small number of pasture based dairy farms in Florida and Georgia, and the pasture based management practices could be best captured through a dialogue with the respondent. Pasture based d airy farmers in Florida and Georgia were invited in the summer and fall of 2012 by mail, telephone calls, emai ls, and general announcements in newsletters to participate in the survey An opportunity to complete the survey was offered at the 2013 Georgia Dairy Conference. Two Florida dairy Extension agents provide d a list of 42 dairy farms to be targeted for parti cipation. One hundred dollars was awarded to each farm for completing the survey. Based on these lists, a total of 40 survey invitations were mailed at the end of September, 2012 and these farms were called to set up appointments if they were

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38 interested to participate In addition, 8 farms initiated contact and said they were interested to participate. A total of 24 farms of those that were contacted did not participate because either wrong telephone numbers were given or the farms did not return the phone calls. One farm volunteered to fill out the survey by mail but did not return the form. Finally, the survey was conducted for 23 dairy farms by 20 different interviewees. Data were collect ed by personal interviews from September 2012 to March 2013 Respo ndents were asked to answer the questions in reference to summer 2011 through spring 2012. The target interviewees of the survey were those responsible for most of the business or who made most decision s on the farm. Of the 23 respondents, 5 were farm mana gers and 18 were owners. Of the 20 interviewees, 1 reported for 3 farms and 1 reported 2 farms. Three interviewees owned more than one farm, but because these farms were managed the same but at a different location, they reported as one whole farm. The sur vey form was slightly edited after the first 3 interviews to clarify some questions. 3.2 Statistical Analysis Data were entered into and summarized by Microsoft Excel 2010 using counts, averages and standard deviations (SD) Three regions were defined: far ms in Georgia, farms in Florida north of Gainesville, and farms in Florida south of Gainesville based on the natural clustering of participating farms The following associations were analyzed with procedure GLM in SAS (Cary, NC) to determine a region eff ect: 1) number of employees = region, 2) number of cows = region, and 3) number of heifers = region Two seasons were defined: summer and winter. The following associations were analyzed to determine a season effect using

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39 procedure Mixed in SAS : 1 ) daily m ilk yield = season, 2 ) somatic cell count = season. Farm was assumed random in the last two models. Least square means for region and season were compared with the Tukey Kramer Adjustment for multiple comparisons and the pdiff option Pearson correlation c oefficients were calculated with procedures Corr in SAS. Significance was declared if the P < 0.05 and a trend was declared if P < 0.10. To compare milk production from cows by providing cooling inside vs. no cooling or access to a cooling pond vs. other outside cooling methods, t he following associations were analyzed with the procedure Mixed in SAS: 1) milk = season + cooling + season*cooling, 2) milk = season + cooling pond + season*cooling pond. Farm was assumed random in the last two models the o ther explanatory variables were fixed Least square means for cooling methods were compared with the pdiff option.

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40 CHAPTER 4 CHARACTERIZATION OF FARM STRUCTURE, MILK PRODUCTION, REPRODUCTION AND FACILITIES 4.1 Dairy Fa rms B usiness S tructure The number of surveyed dairy farms and their business stru ctures are presented in Table 4 1 This survey was conducted on 23 dairy farms: 4 in Georgia, 13 in n orth Florida, and 6 in s outh Florida. Of the 23 farms, there were 6 corporations, 9 limited liability compan ies 1 S corporation, 3 s ole proprietorship s and 4 p artnerships. T he average number of full time employees was 16.2 33.4 and tended to be different among the different regions ( P = 0.06). The average number SD of full time employees were 5.1 1.4, 7.1 4.2, and 43.2 60.6 in Georgia, n orth Florida, and s outh Florida, respectively. The total number of heifers and cows owned by the 23 farms were 17,288 and 28,768, respectively. The average herd size in Georgia was 588 63 cows and 363 25 heifers wh ich were similar to the herd size in n orth Florida. In s outh Florida, the average herd size was much larger at 3 169 3 397 cows and 1 976 3 439 heifer s R egion s differed in average number of cows per herd o f cows ( P = 0. 02 0 ) but not in average number of heifers per herd ( P = 0. 1 58 ). T he total number of cows in n orth Florida was smaller ( P = 0.019) than the number in s outh Florida. Of the 23 farms, 18 would maintain ownership of their heifers from birth to calving Of these, 16 farms raised their own yo ung stock and 2 farms contracted with another party to raise their heifers. Five f arms sold their heifers but 2 would buy their own back later ; 3 of the 5 would buy other heifers. On e farm in s outh Florida did not have any heifers when surveyed which cause d the average number of heifer s per farm

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41 for that region to have a large SD. Of the 16 farms they raised their own heifers, the average of heifer to cow ratio was 63% (min = 25% and max = 107%). 4.2 Description of D airy B reed, Replacement and Genetic S elec tion G oals The distribution of cows among the different breeds is shown in Table 4 2. The dominant pure bred was Holstein with 20,328 cows on 17 farms, representing 71% of all cows in the survey O f the 23 farms, 17 farms (74%) had at least one Holstein. O n 3 farms, purebred Holsteins were less than 25% of all cows while on 9 farms more than 75% of all cows were Holsteins. Of all cows in the survey, p urebreds made up 75% of all cows and crossbreds the remainder 25%. After Holsteins, the second most common c ow breed was the Jersey x Holstein cross with 4,464 head distributed across 10 dairy farms. The purebred Jersey and the Holstein x Jersey cross made up 1,257 and 608 head respectively. The unspecified crossbreed s and other unspecified cows most ly came from local herd dealers. When asked about future breeding plans, 6 farms indicated they would like to expand their cross breeding program wh ereas 2 farms would like to reduce the crossbreeding on their farms. The interviewees were asked to rank at most 3 gene tic selection goals by level of importance ( Table 4 3 ). Twenty two farms responded. Eleven farms indicated that reproduction and longevity were important goals, with 9 and 5 farms responding that it was their first priority, respectively. Secondly, milk vo lume and udder composition were mentioned by 9 and 8 farms, respectively. Udder composition and feet and legs were mostly mentioned as the second or third goals. Of the 19 farms that had crossbreeds, 12 farms had the same genetic selection goals for the multiple (cross) breeds on their farms. Seven farms reported different genetic selection goals depending on the specific (cross) breed however Three farms

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42 decide d on the breed of the service sire based up on the size of the cow ; that is small cows were br ed to Holstein sires and large cows were bred to Jersey sires. The annual cull rate of the 23 farms was 22 9%. The annual cull rate varied greatly between farms and (cross) breeds (Table 4 2). The pure breed Holstein and unspecified cross breed had the h ighest average annual cull rate, which were 28% 10% and 27% 10%, respectively. The lowest annual cull rate was for the Jersey x Milking Shorthorn cross. The average annual cull rate of Brown Swiss, Norwegian Red x Holstein, and Jersey x Holstein x Swed ish Red were below 20%, being 14% 10%, 17%, and 16% respectively. Nineteen farms reported that failure to get pregnant was an important culling reason, with 9 farms reporting it as their primary reason and 6 farms reporting it as their secondary reason (Table 4 4) Low milk production and mastitis were the other 2 culling reasons most often mentioned. Bad udder composition and feet and leg problems were both reported by 8 farms respectively and were most ly mentioned as second and third reason s Death was reported on 5 of the 23 farms to be within the top 3 culling reasons. 4.3 Milk Production and Procedures The milk production was dissimilar among the 23 farms ( Figure 4 1 ) Milk production in the winter ( 26.5 6.6 kg/cow/day ) was greater ( P < 0.001) than in the summer ( 19.0 6.8 kg/cow/day ) All 23 farms reported a lower milk yield in the summer compared to the winter. I n the winter t he highest reported milk yield was 38.1 kg/cow/day and lowest milk yield was only 6.3 kg/cow/day. On the same 23 farms, t he highest milk yield in the summer was 33.6 kg/cow/day and lowest was only 4.5 kg/cow/day. The summer/winter milk yield ratio (summer milk yield divided by winter milk yield an indicator of seasonality ) ranged from 0. 42 to 0. 90. The average

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43 summer/winter milk yield ratio was 0. 71 0. 14. The average annual rolling (moving average) herd milk yield reported was 7,671 1,614 kg/cow/year on 19 farms Four farms could not report their annual rolling herd milk yield. SCC were greater ( P < 0.001) in the summer than in the winter on all 23 farms ( Figure 4 2 ) The averages of the SCC were 249,826 68,894 cells/m l and 368,130 79,085 cells/m l in the winter and summer, respectively. The highest and lowest SCC in the winter were 396,000 and 150,000 cells/m l In the summer, the highest and lowest SCC were 540,000 and 200,000 cells/m l respectively. The summer/winter SCC ratio (summer SCC divided by winter SCC) varied between 2 29 and 1 07 with an average of 1 52 0. 33 cells/m l The correlation between summer/winter ratio of SCC and summer/winter ratio of milk yield was 0.27 ( P = 0.207). Only 11 farms participate d in a Dairy Herd Improvement Association (DHIA) program. Seven farms participated with monthly milk testing while 1 farm only tested a few times during the s ummer and 1farm tested quarterly The remaining 2 farms used DHIA only for records keeping. However, not all farms that tested measured milk yield fat, protein and SSC. Only 2 farms tested for fat, protein, and SCC in addition to milk yield. Two farms te sted for milk yield and SSC but not for milk components. One farm tested for milk yield monthly but tested for fat, protein, and SCC only 3 times per year. In addition, one farm tested for milk only and another farm tested for milk monthly and for SSC as n eed ed Twenty farms milk ed cows twice per day year round. One farm milked only once per day. One farm milked 3 times per day and another farm milked 4 times per day year round.

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44 Milking procedures are reported in Table 4 5 The milking procedures varied ac ross the 22 farms that answered th is question The most common milking procedure (7 farms) was to not wash udders but to strip, and pre dip, wipe, and post dip. Four farms only pre dipped, wiped and post dipped. Udder washing was not done on 15 farms whe reas stripping was not done on 11 farms. Pre dipping and wiping udders were both not done on 1 farm each. All 22 farms reported to use post milking teat dipping. Three farms pre dipped and wiped only when it was raining and the cows were dirty. Only 7 far ms used automatic removal of the milking machine whereas the other 14 farms used manual removal of the milking machine. 4.4 Reproduction Pro grams Age at first calving was 24.0 1.6 months with a range from 20 months to 28 months among the 22 reporting far ms. One farm did not know the age at first calving because they purchased heifers without knowing the age of heifers. Figure 4 3 is a pattern of the intensity of calving of heifers during the year on the 23 farms. Farm 1 only provided the time when most h eifers calv ed ( June and July ) Farm 2, 3 and 4 were 100% seasonal breeding farms such that all their heifers calve d only in a narrow window of a few months. Farm 11 had a minimal number of calvings in July and August, while farm 12 had calving minimal numb er of calvings in the summer. An all year calving pattern was applied on 8 farms which impl ies that 15 farms had one or more months without barely any calving s Five of the 8 year round calving farms still reported a seasonal calving pattern Several r easons were given for these heifer calving patterns M ost of farms tried to avoid calving in the hot summer. However, 3 out of 23 farms planned for calving in summer to fill the need for milk sales caused by the lack of cow calving during the

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45 summer. Moreo ver, interviewees believed t hat heifers, especially crossbreeds, could better handle the heat stress and had fewer problems during calving in the summer compared with mature cows Two farms mentioned the availability of grass as a reason for a heifer calvi ng pattern concentrated in either the fall or spring. The cows calving pattern ( F igure 4 4) was similar to the heifers calving pattern. Farm 1 only reported their most intensive calving month s for cows but not the least intensive All other farms, excep t one farm (farm 21) which had a similar number of calving s each month, had their heaviest calving season in the fall or winter with the least amount of calving s occurring in the summer. Also, farm s 2, 3 and 4 were strict ly seasonal breeding farms such tha t all their cows calve d only in a narrow window of a few months, the same as their heifers calving pattern. Ten of the 23 farms reported that their cow calving pattern was similar to their heifer calving pattern. Three farms (farm s 1, 5, and 11) managed t heir heifers to calve earlier in the calendar year than their cows. One farm (farm 8) indicated that they managed their cows to calve earlier in the year than their heifers. Seven farms (farm s 5, 9, 10, 13, 17, 18, and 21) applied a n all year round calving pattern for cows as well as for their heifers. Only 1 farm (farm 14) had an all year round calving pattern for heifers but had a seasonal calving pattern for their cows. Six cow calving year round farms (farms 5, 9, 10, 13, 17, 18) and five heifer calving year round farms (farms 5, 9, 14, 18, 21) had months with the most calvings. April to August was the least common period for cows to calve Twelve of the 23 farms, which include the 100% seasonal farms, reported that their cows did not calve in July and August. All farms preferred their cows to calve most ly in either fall or winter.

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46 Figure 4 5 shows the seasonal insemination patterns on the 23 farms. These patterns follow ed the calving patterns with the main insemination season starting approximately 2 m onths after the main calving season. The months with the most inseminations were primarily in the winter. A primary insemination period was reported by 22 farms. Twelve farms reported a specific time as the primary insemination p eriod, but 9 farms reported an insemination period, no matter the intensity Others had similar numbers of inseminations in each month. The most common primary insemination period was January to March Only 6 farms inseminated their cows year round but 4 of them still reported a se asonal insemination period. Six farms inseminated many cows or heifers in the summer. They either bred the heifers or selected the younger cows to inseminat e in the summer. To manage their calving pattern, 18 of the 23 farms they had a specific no ne insemi nation period ( Figure 4 5). Exclud ing the 6 all year round calving farms, the other 17 farms had a do not breed period. The most common months of the do not breed period were October and November. On e farm that claimed to calv e year round indicated that an imals were not inseminated for several weeks from late October to Thanksgiving. Six farms reported no breeding in the summer. Table 4 6 summarizes the main reasons for a reduced or limited insemination period. The most popular reason given by 10 of the 2 3 farms for having a no n breeding period was to avoid calving and thereby calving problems in the summer. The next important reason was the lower pregnancy rate in the summer reported by 7 of the 23 farms. Q uality and quantity of grass, labor availabili ty, and maintain ing the seasonality of milk production was mentioned by 4 farms each. The effect of summer h eat stress on

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47 animals was reported as a reason by 3 farms. One farm mentioned that feed availability was a reason for no t breeding. None of the 23 f arms indicated th at time off or vacation was a reason for no t breeding. Table 4 7 lists the main insemination methods used on the 23 farms. The sequence of insemination methods in this list start s with the first method followed by the next method and so on There were 9 different combinations. Among the 23 farms, 6 farms only u sed natural service bulls (NS) for inseminations Five farms applied estrus based artificial insemination (EAI) first and then continued with NS. Four farms applied timed artificial i nsemination (TAI) for the first insemination followed by NS. TAI followed by EAI was applied on 2 farms. A mixture of TAI and EAI applied at the same time followed by NS was applied on 2 farms as well. The 4 remaining farms applied combinations of EAI, TA I, and NS. Natural service bulls were used widely on the farms which may explain why 10 farms had incomplete records of reproduction performance because inseminations were not recorded S exed semen was used on 7 of the 23 farms. All 7 farms used sexed sem en on their heifers at least on c e, and all of them applied it in the cooler winter months One farm also used sexed semen on their higher producing Jersey cows and Brown Swiss cows. None of the farms employed embryo transfer. Reproductive performance whic h included 21 day insemination rate, conception rate and 21 day pregnancy rate was not well measured by many farms. Ten farms had no idea about the ir reproductive performance, including 6 farms where NS was the main breeding method. Only 8 farms reported t he 21 day insemination rate during their main insemination period. The 21 day insemination rate during the main insemination season

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48 averaged 58 17%. The conception rate dur in g the main insemination season was reported by 14 farms with an average of 44% 12%. Outside the main insemination period the insemination rate was reported by 5 only farms with an average of 51 18%. Outside the main insemination period, the conception rate provided by these farms was on average 22% 13%. 4.5 Facilities and Time Budgets Sixteen farms reported a time budget for cows that totaled 24 hours. The o ther 7 farms merged locations, such as cooling pond on grazing paddocks or cooling pond and feeding barn on the grazing paddocks and tree s on the grazing paddocks so that t hey could not be determine how many hours the lactating cows spen t i n each location. For lactating cows, 12 farms out of 16 put the lactating cows on grazing paddocks for an average of 17.5 5.4 hours and 16.0 6.4 hours in winter and summer respectivel y (Table 4 8) Dirt lot s were u sed more in winter (12.0 8.5 hours) than summer (9.6 7.6 hours) In s ummer, lactating cows spent more time in a feeding barn than in winter ( 8.1 4.5 hours vs 3.5 2.1 hours respectively ). Within the 16 farms, 3 farm s did not have any dry cows in winter because calving was very seasonal Dry cows from the other 13 farms spent more than half of the day either on dirt lot s or on grazing paddocks. Two farms had their dry cows spend 2 hours every day in the holding areas for health check s Compared with lactating cows, more farms put their dry cows under tree s instead of on dirt lot s or grazing paddocks with a cooling pond. A variety of cooling systems were applied on the farms (Table 4 9). I n winter, 7 o ut of 23 farms di d not use any cooling system for lactating cows 6 farms used cooling pond s, 5 farms used trees, 3 farms applied irrigation and cooling water through a center

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49 pivot 3 farms used shade clot h, 1 farm set sprinklers outside, 1 farm s provided a shade barn and 1 farm kept their lactating cows in a shade barn with fans and sprinklers to reduce heat stress. Five farms used 2 cooling methods. Only one farm kept their lactating cows most of time inside and only one hour per day on the dirt lots. In the summer, 2 f arms kept their lactating cows under a shade barn most of time and only spent less than 4 hours on dirt lots Eight of the farms provided the cooling pond s for their lactating cows, 7 farms let their cows graze under the center pivot with irrigation water, 5 farms us ed tre e s and 3 farms applied shade cloth. Three farms use d sprinklers, a shade barn and a shade barn with fans and sprinklers respectively Five farms used more than one cooling method. One farm did not use any cooling system. The o utside coo ling systems for dry cows are presented in Table 4 9. More than half of the 23 farms us ed trees to help reduce the heat stress on dry cows in both the winter and the summer. Five farms in the winter and 1 farm in the summer reported that no outside cooling system was used. Cooling pond s were mentioned as the cooling method by 4 farms in winter and 6 farms in summer. Center pivot s with irrigation water was used on a 5 farms in both the winter and the summer. A t otal of 6 farms u sed shade cloth as the cooling method in winter and summer for dry cows Moreover, 1 farm housed cows inside a barn without access to the outside, and 1 farm reported that a shade barn was employed on the farm. Inside cooling systems for lactating and dry cows in summer and winter are present ed in T able 4 9. Although most of the 23 farms were pasture based, s ome of them also applied a cooling system in the feeding barn or exit lane. Eleven farms never put lactating cows in a feeding barn either in winter or summer. Every farm provided at

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50 least one additional cooling method such as fans or sprinklers, for their lactating cows if they put them in a feeding barn in summer. In the winter, 3 farms did not use any cooling system. Fans and sprinklers were applied on 6 farms in winter and on 1 0 farms in summer for lactating cows. Showers in the exit lane were reported by 1 farm in both during the winter and summer. Fans only were applied on 6 farms for lactating cows in both seasons. In addition, in both winter and summer, 3 farms used another cooling system such as tunnel vent ilation misters and soakers and mister only. For dry cows, only one farm put their dry cows in a barn and applied fans with sprinklers in both the winter and summer. The c ooling system s used in the holding area and milk ing parlor are listed in T able 4 10. In the holding area in the winter, 4 farms used no cooling system 9 farms applied fans with sprinklers and the other farms either applied showers fans or misters in the exit lane. In the summer, only one farm did not use a cooling system in the holding area. Thirteen farms applied fans with sprinklers. Showers, fans only and misters were employed in the exit lane on 5, 4 and 5 farms respectively. C ows outside with access to cooling pond during the summer produced on average more milk ( LS means = +4.7 kg/cow/day, P = 0.042) than cows outside without access to a cooling pond. In the winter, the cows outside with access to cooling pond produced on average numerically more milk ( LS means = +2.7 kg/cow/day, P = 0.267) than the cows outside without access to a cooling pond. Cows with access to inside cooling during the summer tended to produce on average more milk ( LS mean = +3.6 kg/cow/day, P = 0.071) than cows without access to inside cooling. But in the winter, cows with access to inside cooling produced on

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51 average numerically more milk ( LS mean = +2.7 kg/cow/day, P = 0.207) than cows without access to inside cooling. Cooling systems were used in the milking parlor in different ways as well (Table 4 10). In the winter, 10 farms did not use any cooling system. Fans with sprinklers or showers in the exit lane were applied on 4 farms. Another 9 farms employed fans only. In the summer, 7 farms did not use any cooling system and 13 farms applied fans only. Others used fans with sprinklers or showers in the exit lane.

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52 Table 4 1. Business structure of the 23 surveyed dairy farms in three regions Characteristic Georgia North Florida South Florida Total # of farms 4 13 6 23 Corporation 0 3 3 6 Limited liability company (LLC) 3 5 1 9 S corporation 0 0 1 1 Sole proprietorship 0 2 1 3 Partnership 1 3 0 4 # of full time employees per farm 5.1 1.4 7.1 4.2 43.2 60.6 371.5 # of heifers per farm 363 25 306 274 1976 3439 17,288 # of cows per farm 588 63 569 589 3169 3397 28,768 Table 4 2. Description of adult dairy cattle breeds and cull rates % of the total herd Breed or cross 1 <25% 25 75% >75% Total # farms # cows Annual cull rate (%) Brown Swiss 4 0 0 4 11 14 10 Holstein 3 5 9 17 20,328 28 10 Jer sey 5 1 0 6 1,257 24 6 Holstein x Jersey 3 3 0 6 608 21 2 Jersey x Holstein 2 7 1 10 4,464 22 12 Montbeliard x H 1 0 0 1 20 20 Norwegian Red x H 1 0 0 1 30 17 Jersey x Milking Shorthorn 0 0 1 1 31 5 Unspecified crossbreed 1 1 0 2 296 27 10 H x J x Swedish Red 1 0 0 1 6 22 J x H x Swedish Red 0 1 0 1 125 16 J x H x S/M/A 0 2 0 2 300 25 0 Other unspecified 1 0 1 2 570 20 0 1 H = Holstein, J = Jersey, M = Milking Shorthorn, A = Ayrshire, S = Swedish Red

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53 Table 4 3. Genetic selection goal s Importance Breeding goals 1 # of farms # as top 1 # as top 2 # as top 3 Reproduction 11 9 1 1 Longevity 11 5 2 4 Milk volume 9 4 3 2 Udder composition 8 0 2 6 Feet and legs 6 0 3 3 Calving ability 5 1 4 0 Net merit dollars 2 2 0 0 Fluid merit d ollars 2 0 2 0 Body capacity 3 0 3 0 Strength 1 1 0 0 Fat and p rotein 1 0 0 1 Functional type 1 0 0 1 Once per day milking 1 0 0 1 1 Each farm reported up to 3 goals Table 4 4. Primary culling reasons Importance Cull reasons 1 # of farms # as top 1 # as top 2 # as top 3 Failure to get pregnant 19 9 6 4 Low milk production 14 5 5 4 Mastitis 11 4 3 4 Bad udder composition 8 2 4 2 Feet and leg problems 8 2 3 3 Death 5 0 2 3 Disease 3 0 0 3 Temperament 1 1 0 0 1 Each farm reported up to 3 goal s

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54 Table 4 5. Milking procedures Wash udders Strip Pre dip Wipe Post dip # of farms No Yes Yes Yes Yes 7 Yes Yes Yes Yes Yes 4 No No Yes Yes Yes 4 No No Yes 1 Yes 2 Yes 3 Yes No Yes Yes Yes 2 Yes No No Yes Yes 1 No No Yes No Yes 1 1 On fresh cows o r during raining weather only 2 When dirty Table 4 6. Reasons for a reduced or limited insemination period Reasons # of farms Calving problems in the summer 10 Failure to get cows pregnant 7 Matching q uality and quantity of grass 4 Labor availabili ty 4 Maintain the seasonality of milk production 4 Heat stress 3 Feed availability 1 Time off or vacation 0

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55 Table 4 7 Insemination methods S equence 1 # of farms NS 6 EAI + NS 5 TAI + NS 4 TAI + EAI 2 MIX + NS 2 NS + TAI + NS 1 EAI 1 TAI + E AI +NS 1 MIX 1 1 NS = natural service EAI = estrus followed by artificial insemination (only) TAI = time artificial insemination (only) MIX = estrus followed by artificial insemination and timed artificial insemination Table 4 8. Time budget for l actating cows in each location Locations Summer (hours) # farms Winter (hours) # farms Dirt lot 9.6 7.6 5 12.0 8.5 6 Grazing paddocks 16.0 6.4 12 17.5 5.4 12 Feeding barn 8.1 4.5 7 3.5 2.1 5 Free stall 19.0 1 19.0 1 Holding area + milking parlor 3.6 1.2 16 3.6 1.2 16 Cooling pond 9.0 1 11.0 1 Under trees 0 0 0 0

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56 Table 4 9. Cooling systems used on pasture and while cows are grouped for milking and supplementation Cooling system # of farms Winter Summer Lactating Dry Lactating Dry Cows do not go outside 1 1 2 1 Outside cooling system: No cooling system 7 5 1 1 Cooling pond 6 4 8 6 Under center pivot 3 1 7 4 Under shade cloth 3 2 3 4 Under trees 5 13 5 14 Others 1 3 1 3 1 Cows do not go inside 11 22 11 22 Inside cooling system No cooling system 3 0 0 0 Fans with sprinklers 6 1 10 1 Showers in the exit lane 1 0 1 0 Fans only 2 0 4 0 Others 2 3 0 3 0 1 Includes only cows under sprinklers outside 2 Includes tunnel vent, misters and soakers Table 4 10. Cooling system at holding area and milking parlor Cooling locations and methods # of farms Winter Summer In holding area No cooling 4 1 Fans with sprinklers 9 13 Showers in the exit lane 3 5 Fans only 4 4 Misters 4 5 In milking parlor No cooling 10 7 Fans with sprinklers 3 3 Showers in the exit lane 1 2 Fans only 9 13

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57 Figure 4 1. Average daily milk production in the winter and summer seasons Figure 4 2. Somatic c ell c ou nts in the winter and summer seasons

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58 Farm ID Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 2 3 4 5 1 6 7 8 9 1 10 1 11 12 13 1 14 1 15 16 17 1 18 1 19 20 21 1 22 23 Figure 4 3. Heifers calving pattern with minimal number or no heifer calvings. = months with a regular amount of heifer calvings. 1 Farms calve all year round

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59 Farm ID Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 2 3 4 5 1 6 7 8 9 1 10 1 11 12 13 1 14 15 16 17 1 18 1 19 20 21 1 22 23 Figure 4 4. Cows calving pattern. = months with minimal number or no cow calvings. = mon ths with regular cow calvings. 1 Farms calve all year round.

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60 Farm ID Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 2 3 4 5 6 7 8 9 1 10 1 11 12 13 1 14 15 16 17 1 18 1 19 20 21 1 22 23 Figure 4 5. Cows insemination pattern. = months with minimal number or no cow inseminations. = months with regula r amount of cow inseminations. 1 Farms calve all year round.

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61 CHAPTER 5 CHARACTERIZATION OF PASTURE AND FEEDING MANAGEMENT 5.1 Land U se The total hectares per farm ranged from 23 to 1,395 among the surveyed 23 farms ( Figure 5 1 ) Improved grass paddocks were used on 17 farms. The presence of un improved grass paddocks were reported by 8 farms. Dirt lots were reported on 9 farms and 1 far m did not specify their field type The total area of the g razing paddocks per farm was 181 277 hectares. Except for the 3 farms that did not provide useful data 20 of the 23 farms had less than 225 hectares, which was 14 to 100% of their total land ar ea dedicated to grazing. The average percentage for grazing land of their total farm size was 93% 39%. The average size of a grazing paddock for milking cows was 6.4 8.3 hectares ; and the average size of a grazing paddock for dry cows was 13.3 15.4 hectares Permanent paddocks were used on 16 farms with on average 38 53 paddocks with a range from 8 to 200 paddocks. Paddocks were laid out in 4 different ways as is shown in Table 5 1. The most popular way was fixed sized lots only. These lots had co nstant sizes due to permanent fencing. One farm laid out the fixed sized lots as a traditional pie chart shape. Another 5 farms applied a center pivot with a traditional pie chart for both their lactating cows and dry cows. Only 2 farms reported that they used fixed sized lots with a center pivot and laid the lots out as traditional pie chart for lactating cows. Center pivots with a double circle of pie chart laid out paddocks was used on 1 farm for both their lactating cows and dry cows. Finally, 1 farm di d not let their lactating cows go outside and another farm did not put their dry cows outside.

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62 5.2 Pasture Utilization The number of lactating cows on each hectare of paddock in the summer was reported by 17 farms ( Figure 5 2). The average stocking densit y of lactating cows was 19 21 cows per hectare. In the winter, the stocking density of lactating cows w as reported by 15 farms with the average of 19 22 cows per hectare. For dry cows, the stocking density was reported by 13 farms with on average 6 5 cows and 8 12 cows per hectare in summer and winter, respectively. The time cows spent on each pasture before the pasture was given a rest time to regrow grass was widely different. For lactating cows, 13 farms reported th is pasture utilization period In the summer, the longest pasture utilization period was 60 days and the shortest was 0.5 day, and the average of the pasture utilization period was 6 17 days. In the winter, the pasture utilization period was very similar, with on average 6 18 days a s reported by 11 farms with the longest 60 days and shortest 0.5 day. For dry cows, the pasture utilization period was provided by 8 farms in the summer and by 7 farms in the winte r with the average 109 175 days and the range from 1 day to 365 days in bo th summer and winter. In addition to a large variation in the time cows spent on a pasture, the rest time for each pasture was very different between farms as well Rest time was reported by 14 farms in the summer and by 13 farms in the winter for lactati ng cows (Figure 5 2). For lactating cows, the shortest time was 4 days in both summer and winter while the longest time was 180 days in both summer and winter. The average rest time was 37 64 days and 49 62 days in summer and winter, respectively. Farm s with h igh stocking density did not use their paddock s as grazing pasture. According to the Figure 5 2,

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63 there was no relationship between the stocking density and rest days per one day in the pasture. For dry cows, the rest time for each pasture was repo rted by 4 farms with the average 15 13 days in the summer and 43 40 days in the winter. The time range was from 1 to 30 days in the summer and 1 to 90 days in the winter For pasture management, the h eight of the grass was estimated by 15 farms, with 10 farms estimating the height visually; 3 farms used visual observation plus pasture plate measurements. One farm estimated grass height by using a pasture stick and the other farm estimated height by using a pasture plate meter. Grass height was estimate d daily by 7 farms, weekly by 2 farms, twice monthly by 1 farm, and monthly by 2 farms. One farm estimated grass height only during the winter. Only one farm measured the height of the grass to decide on the rest time of the paddock for lactating cows. Th is farm set 41 to 46 centimeter as the move in grazing target and set 10centimeter as the move out target for lactating cows and no target for the dry cows. All other farms counted time since the cows moved in or out of the pasture and estimated grass heig ht visually to decide when to move cows in or out of a paddock. Time varied base d on rain and grass grow th The grazing plans varied among the 23 farms as well The f irst grazing model, used by 3 farms, was that dry cows followed lactating cows after the lactating cows had left the paddocks. Secondly, dry cows were always kept on the same paddock on 2 farms. Thirdly, 5 farms arranged for the high milk yield cows to get the fresh est grass first which then were followed by lower milk yield cows. Another 2 fa rms forced the cows that were later lactation to eat more grass and fresh cows had less time to eat grass,

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64 but the fresh cows were allowed more time in the feed barn. The others farms did not make special arrangements that depend ed on the group. Informati on o n whether the purchased feed price affected grazing management was provided by 20 farms. Thirteen farms indicated that the purchased feed price affected their grazing plans. First, as the feed price increased, they tried to use more grass and reduced t he amount of concentrates fed. Secondly, some of farms tried to grow more of their own feed. Thirdly, some of the farms stored more feed when the price was low and less when the price was high. One farm reported that they also picked out the fat cows and g ave them less concentrates. But in general, as the feed price increased farms cut down on the amount of concentrate fed and tried to use more grass All but 2 farms had water piped to the paddocks to provide water for grazing animals The 2 farms did n ot provide water in the paddocks. Three farms also used center pivots to provide water to their herds and 2 farms had natural water sources on their grazing paddocks. 5.3 Grass and Forage Species Grown on Farms A total 26 different grasses and forages s pecies were planted among the 23 farms ( Table 5 2) Eight species were cool season annual plants, 10 species were warm season perennial grasses, 4 species were warm season annual grasses, 2 species were cool season perennials, 1 specie was native perennial and 1 specie was unspecified. During the warm season, all 23 farms grew warm season perennial grasses such as bahiagrass or bermudagrass species. During the cool season, 18 farms grew cool season annual grasses. For the grasses, 15 farms reported that the y made balage or

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65 hay or silage to help control the grass quality and preserve the grasses. Animals could graze on grass in 17 farms out of 23 farms. Both corn and sorghum were planted on 6 farms, 2 farms only planted corn and 1 farm only planted sorghum as warm annual grasses. All the corn and sorghum grown on farms was made into silage. The t otal area of warm season perennial grassland was 5,012 hectares with mixed species pastures occupying 2,630 ha (52%) and non mixed pastures occupying the remaining 2, 382 hectares Of the non mixed grass pastures, 878 hectares (37%) was bermudagrass ( Cynodon spp. ) which included Tifton 85, common bermudagrass, Florakirk bermudagrass and coastal bermudagrass; 1,114 hectares (47%) was stargrass ( Cynodon nlemfuensis ); 100 hectares (4%) was limpograss ( Hemarthria altissima ); and 289 hectares (12%) was bahiagrass ( Paspalum notatum ), including Pensaco la, Tifton 9 and Argentine. The t otal area of cool season annual grasses was 1,475 hectares with mixed cool season annual gras ses on 878 hectares (59%) and non mixed cool season annual grasses on 678 hectares (41%) Of the non mixed grasses, oat ( Avena sativa ) was the most common (482 hectares 71%), followed by triticale (x Triticosecale spp.) on 144 hectares (21%) and annual ry egrass ( Lolium multiflorum ) on 52 hectares (8%). The most popular mixture of cool season gras ses was annual ryegrass and oat established on 374 hectares (43%). Warm season annual grasses were established on 2,358 hectares with corn ( Zea mays ) on 938 he ctares (40%), sorghum ( Sorghum bicolor ) on 850 hectares (36%), crabgrass ( Digitaria sanguinalis ) on 400 hectares (17%) and pearl millet ( Pennisetum glaucum ) on 168 hectares (7%).

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66 Oat and annual ryegrass as the most popular cool season annual forages were planted on 13 and 11 farms, respectively. Annual rye grass mixed with other grasses was reported by 7 farms with the average mixture at 55 16%. An unknown mixture based on annual ryegrass was reported by 2 farms. Two farms reported to grow annual ryegras s wit hout an additional mixture. Oat was planted as mix grass on 5 farms with the average mixture at 43 7% oat Further, pure oat was planted on 6 farms, and with an unknown mixture planted on 2 farms. Many different warm season perennial grasses wer e used. The most popular were Coastal bermudagrass, common bermudagrass, Argentine bahiagrass and Tifton 85 bermudagrass. The use of Coastal bermudagrass was reported by 11 farms with an average mixture of 50 26% on 8 farms and without mixture on 3 farms Common bermudagrass was planted on 9 farms with an average mixture of 43 25% on 4 farms, an unknown mixture on 4 farms and without mixture on 1 farm. Tifton 85 bermudagrass was used on 8 farms with an average mixture of 43 11% on 2 farms, an unknown mixture or not mixed on 3 farms each. The use of Argentine bahiagrass was reported by 8 farms with an average mixture at 32 21% on 6 farms and non mixture on 2 farms. A total of 13 out of 18 farms growing winter season grasses or forages over or resee ded their oat and ryegrass on existing growth. The other 5 farms reported that they always grew winter season grasses or forages on a new bed. The annual sequence in which crops were grown varied among farms ( Table 5 3). The most common sequence was corn, followed by sorghum or corn, and cool season annual grasses. For example, the first planting was corn, then sorghum, then oat In addition, a sequence of cool season annuals, warm season annuals and cool

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67 season annuals was also applied. Specifically, 3 farms grew a seq uence of corn, sorghum, and oat on a total of 244 hectares; 2 farms grew corn followed by sorghum on 364 hectares. Eight different farms grew eight other sequences. 5.4 Insect and Weed Control, and Fertilization For insect control in padd ocks, 1 farm used poultry (chickens and turkeys) as a biological control for pasture caterpillars (armyworm and looper), which are the most common targets of insect control. Insecticide was applied by 13 farms for caterpillar control and 9 farms did not us e any insect control. For weed control, 2 farms did not use anything, 5 farms only used herbicide, 10 farms applied herbicides and machine mowing, 3 farms used hand harvesting only, 2 farms used manual and machine control, and 1 farm employed herbicide an d manual control. When farms were asked how they determined the nutrient profile of grass paddocks, 8 farms replied that they used nothing as the cues to estimate the nutrient of grass paddocks. Among the 23 reporting farms, 9 farms indicated the historic sample analysis, 4 farms indicated current sample analysis and 4 mentioned book values of forages as the cue for determining the nutrient profile of grass paddock. In addition, 1 interviewee mentioned that she personally tasted the grass as the cue to help determine reported they might chop and sample the grass to help determine the nutrient profile. Ten farms reported that they did not use any manure or commercial fertiliz er on paddocks. Solid manure was applied by 6 farms, with 2 farms applying solid manure daily, 3 farms applying it monthly, and 1 farm applying it once every 3 year. Liquid manure was used by 10 farms. The frequency was from daily to once every 6 years.

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68 Co mmercial fertilizer was used on 11 farms with widely different application plans from monthly to one time per 10 to 18 years. On cropland, 10 farms reported that they did not use any manure or commercial fertilizer. The application frequency was higher th an on paddocks and more complicated among farms. Solid manure was used by 12 farms. The highest frequency was daily and the lowest frequency was yearly. Other frequencies used were from once per 3 days to once a year. Liquid manure application was reported by 13 farms. The highest frequency was daily and the lowest frequency was 2 times per year. Others frequencies that were used ranged from once per 3 days to monthly. The use of commercial fertilizer on crop land was reported by 10 farms. The use frequenci es mostly depended on the type of plants growing. The frequency of application ranged from twice per year to 8 times per year. Among the total 23 farms, 16 farms kept written records of where, when and how much manure was applied. Seventeen farms had a ce rtified nutrient management plan certified by an outside agency. The irrigation methods used by t he 23 farms are shown in Table 5 4 One farm did not use any irrigation at all and 2 farms did not use any irrigation on paddocks. Most farms employed center p ivots or travelling guns on their paddocks and cropland. Effluent was delivered through the center pivot on 4 farms to their paddocks and on 5 farms to their cropland. Fresh water was delivered by a center pivot on 9 farms to paddocks and on 7 farms to cro pland. By using a travelling gun, 4 farms delivered effluent to their paddocks and 6 farms delivered it to their cropland. Similarly, 1 farm used a travelling gun to deliver fresh water on paddocks and 3 farms used them on

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69 cropland. Others irrigation metho ds include d the use of sprinklers to deliver fresh water on paddocks and a dry separator to deliver effluent on paddocks. 5. 5 Dairy Cow s Nutrition and Feed Intake The DMI were reported by 21 farms for lactating cows and dry cows. One farm had no idea about how much dry matter they fed because they used buckets to measure feed quantities. One farm just applied a total mixed ration and did not provide DMI. In the winter for lactating cows, 2 farms let their cows graze ad libitum and did not know how much dry matter the cows ate. One farm offered ad libitum amounts of balage and silage. In the summer, 3 farms allowed for ad libitum fresh grass intake and 1 farm provided ad libitum balage and silage but they could not report the dry matter intake For dry cows, 4 farms offered ad libitu m grazing, 1 farm offered ad libitum silage and 1 farm offered ad libitum hay in the summer. In the winter, 2 farms offered ad libitum grazing and 2 farms offered ad libitum hay to the dry cows. None of the farms reported the amou nt of DMI from ad libitum fed feeds. Therefore, we used the average weight of each category to estimate the DMI for the ad libitum fed cows. A large variation in sources and DMI was observed among farms ( Figure 5 3). For lactating cows in winter, the aver age DMI was 20.0 4.0 kg/cow/day. The average DMI from grazing was 2.9 1.9 kg/cow/day among 14 farms. Here 2 farms provided ad libitum fresh grass to graze also they could not report the dry matter intake Hay was provided by 7 farms with the average DMI from hay estimated at 4.7 2.8 kg/cow/day. Balage and silage were used on 10 farms with the average DMI at 3.4 1.2 kg/cow/day and 6.2 2.0 kg/cow/day, respectively. One farm indicated that they provide balage and

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70 silage ad libitum. The average DMI from concentrate was 12.1 4.6 kg/cow/day among the 21 farms. One farm indicated they provided 0.9 kg of dry matter from molasses. For dry cows in the winter, the reported DMI also varied widely. The average DMI was 11.4 3.7 kg/cow/day among 18 farms. Close to calving, 3 farms reported that they decreased DMI from grazing from 7.7 kg/cow/day to 1.8 kg/cow/day, increased DMI to 9.1 kg/cow/day from balage as grass intake decreased and also provided 1.8 kg/cow/day dry matter from concentrate. In addition t o those 3 farms, grass was provided ad libitum on 2 farms and hay was also provided ad libitum on 2 farms. In general, fresh grass was provided on 13 farms with the average DMI of 6.5 3.2 kg/cow/day, hay was fed on 10 farms with the average of 7.1 4.0 kg/cow/day, balage was fed on 6 farms with an average of 2.9 3.3 kg/cow/day, silage and concentrate were fed on 4 and 17 farms with an average of 2.1 2.0 kg/cow/day and 4.8 2.2 kg/cow/day, respectively. The reported summer DMI for lactating cows amo ng 21 farms are shown in Figure 5 4. The pattern was similar to that reported for the winter. Three farms fed ad libitum grass for grazing and 1 farm provided ad libitum balage and silage. For lactating cows in the summer, the total DMI was 17.7 4.9 kg/c ow/day. In total, 15 farms provided their lactating cows fresh grass by grazing with the average DMI reported at 6.4 3.8 kg/cow/day. One farm applied green chop with 3.2 kg/cow/day DMI. Hay was provided by 5 farms with the average DMI at 2.8 0.9 kg/cow s/day. Balage was used on only 3 farms with the average DMI at 2.5 1.2 kg/cow/day. The DMI from concentrate was about 9.8 4.3 kg/cow/day among the 21farms. Silage was provided on 9 farms with the DMI at 6.3 2.1 kg/cow/day.

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71 In the summer for dry cows a total of 20 farms reported the DMI with an average of 10.5 4.8 kg/cow/day. Two farms only provided fresh grass ad libitum and 1 farm used silage ad libitum only. Four farms fed fresh grass ad libitum. Fourteen farms fed fresh grass by grazing with an average DMI of 8.4 1.9 kg/cow/day, 4 farms fed hay with an average of 2.3 1.0 kg/cow/day, 3 farms fed balage with an average of 6.3 0.9 kg/cow/day, 5 farms fed silage with an average of 4.3 2.2 kg/cow/day. Only 11 farms fed a concentrate with 3.2 2.0 kg/cow/day. Seventeen farms consulted a nutritionist for review and rebalancing of their feed rations at least once per year. Five farms reviewed rations less than monthly while 1 farm reviewed them quarterly. Eleven farms reviewed rations at least monthly. Twenty two farms indicated that when they balanced their feed rations they considered at least one of the following areas: soil analysis, forage analysis, pasture intake, season of the year and price of feed stuffs. Details about the consideratio ns by each farm are shown in Table 5 5 The price of feed stuffs was the most common consideration by farms, which was mentioned by 17 farms. Season of the year and forage analysis were also important when balancing feed rations as was indicated by 16 farm s. In addition, 9 farms mentioned pasture intake and 5 farms reported soil analysis decisions of what plants to grow. 5.6 Electricity Use and Future Outlook The electr icity use was often different between the summer and winter But 5 farms reported that electricity use was similar in the summer and winter. Fourteen farms reported that they used more electricity in the summer. Specifically, 9 farms reported that more than 30% more electricity was used in the summer; 5 farms indicated that

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72 less than 30% more electricity was used in the summer. On the other hand, 4 farms indicated that they used less electricity in the summer. To be specific, 1 farm reported that they used m ore than 30% less electricity in the summer compared to the winter and 3 farms reported that in the summer they used less than 30% less than in the winter. There were limitations for growth on 22 farms. The reported major limitations are shown in Table 5 6 Only 1 farm indicated they were still growing and did not see any major limitations. The average number of reported limitations for each farm was 3 2. The most important limitations were high purchased feed price, low milk price and land availability. A limited customer base was a limiting factor on one farm, which marketed their milk by themself. Simply no desire to grow was reported by one farm. That pasture based dairy farming was too much work or low reproductive performance were not reported by any farm. Among the 23 farms, 5 farms indicated that they planned to obtain more cows and improve efficiency. An additional 5 farms said they were not sure about expansion with one respondent reporting that if they expanded they would go to a more confined system. Moreover, 7 farms reported a desire to keep the same pasture based model, with 2 of them indicating that they will decrease cow numbers but will maximize the use of grass, and improve the quality of grass. In addition, 2 farms wanted to purchase mo re land if possible. The other farms just wanted to survive and make a living. Due to the currently high feed prices, 7 farms indicated that they wanted to use more grass and to make a higher quality of balage in the next several years. Another 7 farms ex pressed that they will keep the same pasture use and try to maximize the benefits from pasture. An additional 1 farm reported that because of the long distance to

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73 the milking parlor, they would keep the same pasture use. Finally, 5 farms wanted to use less grass since they were limited on acreage were thinking of using more confinement in the future. Table 5 1 Ways the paddocks were laid out Characterizations # farms for lactating cows # farms for dry cows Fixed size lots only 14 16 Center pivot + tradi tional pie chart 5 5 Fixed + center pivot + traditional pie chart 2 0 Center pivot double circle 1 1 Does not apply 1 1

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74 Table 5 2. Types of grasses and forages used on farms Type Grass/forage # farms Total (Hectares) Mixture (%) 7 Pure 8 Unknown 9 Irrigated Non irrigated Cool season annuals CA 1 Annual ryegrass 7 (55 16) 2 2 747 206 CA 1 Arrow leaf clover 1 (7.5) 0 0 25 6 CA 1 Cereal rye 1 (50) 0 0 141 20 CA 1 Crimson clover 0 0 2 202 0 CA 1 Oat 5 (43 7) 6 2 793 174 CA 1 Rye 0 2 0 20 26 CA 1 Triticale 0 1 0 145 0 CA 1 Wheat 0 1 0 42 48 Cool season perennials CP 5 Red clover 1 (7.5) 0 0 25 6 CP 5 White clover 1 (33) 0 0 0 53 Warm season annuals WA 3 Corn 0 8 0 947 0 WA 3 Crab grass 0 0 4 356 11 WA 3 Pearl millet 0 3 0 153 16 WA 3 Sorghum 0 7 0 661 218 Warm season perennials WP 2 Argentine bahia grass 6 (32 21) 2 0 38 1,436 WP 2 Coastal bermuda grass 8 (50 29) 3 0 240 385 WP 2 Common bermuda grass 4 (43 25) 1 4 413 1,804 WP 2 Florakirk bermuda grass 0 1 0 48 22 WP 2 Jiggs b ermuda grass 1 (50 ) 0 1 222 808 WP 2 Limpograss 0 1 1 222 747 WP 2 Pensacola bahia grass 4 (27 9) 2 0 210 145 WP 2 Stargrass 3 (57 35) 2 1 250 2,947 WP 2 Tifton 85 bermuda grass 2 (43 11) 3 3 540 78 WP 2 Tifton 9 bermuda grass 0 1 0 12 47 Native perennials NP 4 Panicum 0 0 3 251 0 Unspecified UNS 6 Smut grass 1(25) 0 0 0 1,042 1 CA = cool season annuals 2 WP = warm season perennials 3 WA = warm season annuals 4 native perennials 5 cool season perennials 6 unspecified 7 grow n with other grass or grasses with a known mixture ra te 8 grow n one kind of grass only 9 grow n with other grass or grasses with an unknown mixture rate

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75 Table 5 3. Crop sequence applied on farms Crop sequence # farms # total hectares Corn, sorghum, oats 3 244 Corn, sorghum 2 364 Corn, corn, pearl millet 1 32 Corn, corn, rye 1 51 Corn, pearl millet 1 81 Annual ryegrass, corn, sorghum, wheat 1 42 Annual ryegrass, sorghum, wheat 1 48 Annual ryegrass/oats, pearl millet, annual ryegrass/oats 1 36 Rye, pearl millet 1 20 Oats, sorghum 1 31 Table 5 4. Irrigation methods Irrigation methods Paddocks (# farms) Cropland (# farms) Effluent Fresh water Effluent Fresh water Center pivot 4 9 5 7 Stationary gun 1 0 0 0 Travelling gun 4 1 6 3 Hand hose 1 0 1 1 Moving line 0 1 0 0 Others 1 1 0 0 Tab le 5 5 Considerations for balancing feed rations Considerations # farms Forage analysis, season of the year and price of feed stuffs 5 Soil analysis, forage analysis, pasture intake, season of the year and price of feed stuffs 4 Season of the year and price of feed stuffs 3 Forage analysis, and price of feed stuffs 2 Forage analysis only 2 Forage analysis, pasture intake, season of the year, and price of feed stuffs 2 Soil analysis, pasture intake, season of the year, and price of feed stuffs 1 Fo rage analysis, and pasture intake 1 Pasture intake and season of the year 1 Price of feed stuffs only 1 None 1

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76 Table 5 6 Major limitations for growth of the farm Growth limitations # farms High purchased feed price 13 Low milk price 10 Land ava ilability 10 Do not want to borrow any more money 6 Lack of borrowing capacity 5 Environment regulations 4 Grass quality 2 Lack of owned capital 2 No desire to grow the farm 1 Low milk production 1 No successor 1 Hard to find experience employees 1 Limited customer base 1 Too much work 0 Low reproductive performance 0

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77 Figure 5 1. Land usage in each farm Figure 5 2 Stocking density and pasture rest period (days)

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78 Figure 5 3. Total dry matter intake for l actating cows in winter Figure 5 4. Total dry matter in take for lactating cows in the summer

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79 CHAPTER 6 DISCUSSION 6 .1 Survey Participation The objective of the study was to characterize pasture based dairy farms in Florida and Georgia with regards to milk production, reproduction, facilities, cattle breeds, cow nutrition, and pasture management. A survey form was developed with the intent to conduct personal interviews via farm visits to obtain the greatest participation and clarify questions and a nswers. Each interview lasted approximately 1.5 hours and participants were offered $100 for a completed survey. Farmers were less willing to participate than expected due to numerous time constraints. In the end, survey results were obtained from 23 far ms: 18 from Florida and 5 from Georgia The survey covered a total of 28,768 cows; which represented about 15% of the total number of cows in Florida and Georgia Some participants from Florida of similar farms with similar management practices and the same owner or upper management but at different locations. Therefore more than 23 locations were included. Given lists obtained from the Florida Extension personnel, ap proximately 50% of the pasture based farms in Florida were likely included. The participation rate for Georgia was much lower. Therefore, the survey results may not be as accurate a representation of pasture based dairy farms in Georgia On the other hand no major differences in the characteristics of pasture based dairy farms in Florida and Georgia are expected. Low participation in DHIA and lack of concrete knowledge about production measures, such as reproductive performance, illustrates the percept ion that record

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80 keeping at pasture based dairy farms is not a priority. As a result, many farms either could not report production measures, or gave rough estimates. 6.2 Cattle Breeds Genetic Selection, and Culling The survey results on cattle breeds wer e in agreement with the literature on grazing farms. Approximately 95% of dairy operations in the U.S. had at least one Holstein cow (NAHMS, 2007). Our survey results showed that 74% (17 farms) had at least one Holstein cow which number is lower. In the N AHMS (2007) report, Holsteins represented around 90% of all cows. In our study, 71% of all cows (n = 20,328) on the 23 farms were Holstein. In our survey, 93% of all cows reported some Holstein genetics. Gay (2012) only reported that 70% of all farms inclu ded in his grazing survey reported at least some Holstein genetics. Both NAHMS (2007) and Gay (2012) reported the second largest breed was Jerseys. The results from a dairy grazing practice survey in Wisconsin (Paine, 2013) stated that the largest group wa s pure breed Holstein s (62%), but the second largest group was cross breeds (27%) Among the 23 farms in Florida and Georgia the pure breed Holstein was the largest group and represented 71% of the total cows included in the survey. The second largest grou p was cross breeds and the third one was Jersey. It was similar to the results from Paine et al. (2013). Probert (2013) reported that the most important trait of genetic selection considered by pasture based producers was reproduction, followed by body si ze, udder health, longevity and feet and legs respectively. Gay (2012) ranked traits considered by grazing producers from most to least important, as longevity, udder composition, and feet and leg. Reproduction ranked 6 th .But the results from our survey sh owed that

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81 farmers preferred reproduction and longevity first, followed by milk volume, udder composition and feet and legs. Thus, selection criteria differed from the literature. The annual cull rate varied greatly between farms and (cross) breeds in in o ur study. The annual cull rate for each breed was lower than the average annual cull rate of 34% as reported by De Vries (2009) for all Florida farms participating in DHIA. The (2012) was average cull rate of 18%. Pasture based dairy farms appear to have lower cull rates than confined dairy farms. De Vries (2009) further reported that reproduction problems was the most often cited culling reason for herds participating in the D HIA program. Additionally, Pinedo et al (2010) indicated that death, reproduction, injury or other, mastitis, and low production were the top 5 reasons for culling in a similar data set. Also, Gay (2012) reported that the top culling reasons in grazing far ms were fertility, high somatic cell count, low production, and feet and legs problem. Although, Smith et al. (2000) reported death losses were high in South, the death losses in our study were not high. Reproduction or failure to get pregnant has been fre quently reported as a primary reason for culling, followed by low milk production, udder composition, and feet and leg problems in our study. These results were very similar to national results from Gay (2012). 6.3 Milk Production and Milk Quality Results for milk production and milk quality were generally in agreement with the literature on grazing farms. The rolling herd average milk yield among the 19 farms that reported results was 7,671 kg/cow, which was lower than the reported nationwide milk produc tion of 9,702 kg/cow, and lower than the Florida average milk production of 8,667 kg/cow, and lower than the average Georgia milk production of 8,343 kg/cow (USDA

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82 NASS, 2011). But out result was higher than the milk yield among all breeds on grazing dairy farms in Wisconsin, which was 7,005 kg/cow/year (Paine, 2013). The average milk 21.3 kg/cow/day The average reported SCC in the summer and winter among the surveyed farms was 368,130 cells/ml and 249,826 cells/ml, respectively, which were higher than the 237,320 cells/ml in summer, and 233,420 cells/ml in winter as reported by Gay (2012). Especially milk qu ality in the summer was worse. This result is likely caused by the more humid and hotter weather in Southeast. The mean SCC of the surveyed farms, regardless of season, was 308,978 cells/ml, which was higher than either the Florida mean SCC (267,000 cells/ml) or the Georgia mean SCC (280,000 cells/ml), and much hi gher than the national average SCC (200,000 cells/ml) (Norman, et al., 2013). 6.4 Reproduction Most pasture based dairy farms calved seasonally with an emphasis on late fall calving. Most of them had a no breeding time so these farms were deliberately sea sonal. The calving problems in the summer and failure to get cows pregnant were the most common reasons not to inseminate. In addition, some farms targeted the calving pattern to match grass availability by matching peak milk production with grass growth i n the spring and summer. The major breeding method was NS. Many farms used a bull, either immediately after the voluntary waiting period, or after an unsuccessful AI period. This strategy was also reported by Washburn et al. (2002b) for grazing farms. One farm began using a bull before 80 days after calving in our study.

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83 Reproductive performance of the surveyed herds during the main breeding season was on average good as could be expected for farms that use pasture to graze cows. Conception rate in the pri mary insemination season was 58 17% in our study,). Results for the Wisconsin grazing study were not available. Following to the average insemination rate (44%) and conception rate in the primary insemination season, the pregnancy rate can be calculated at 25% from our study. This result is much higher than the winter pregnant rate, of 18% in Florida and Georgia DHIA in 2002 (De Vries and Risco, 2005). 6 .5 Feed Intake There were large variations in the objectives for their pastures among the survey farms It appeared from the farm visits that several farms used the paddock primarily as a holding area and just put the cows on it for several hours per day. Grass intake was not a goal for these farms. Other farms managed grass really well and applied a grazi ng rotation management. Cows on these farms spent most of the time on the pastures. These farms provided concentrate based on grass availability. In our study, 12 farms allowed lactating cows to graze on pasture in both the summer and winter. All farms made their own balage and silage. Grazed grass was often not a major feed source, however. For lactating cows, 71 28% of the dry matter intake came from stored feed in the summer and 90 11% came from stored feed in winter. Farms were not clear enough about the how much their cows ate when they were on pasture. Dry matter intake might have been be estimated by farmers which could be far from the truth.

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84 6.6 Electricity Consumption and Future Prospects Annual patterns in electricity usage varied remarkab ly with some farms using significantly more and other farms using significantly less electricity in the summer Some farms used more electricity in the winter because they had more cows to milk. Other farms used more electricity in the summer due to more i rrigation and cooling The major limitations for future growth were high purchased feed costs and low milk price. Some farms wanted to expand with pasture based farming where others looked into more confined systems. This survey highlighted the large var iation that exists among pasture based farms in Florida and Georgia. It is unclear from this study if the variation in management practices resulted from a lack of knowledge or other constrains that prevented implementation of best pasture base practices. Future research might investigate the greatest needs that pasture based farms have regarding education and support. Future research might also determine the effect of various practices on profitability and lifestyle.

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85 CHAPTER 7 CONCLUSIONS Genetic sele ction, culling reason, milk production and reproduction, pasture management and utilization, grass varieties, fertilizer practices, feed management, all varied widely among pasture based dairy farms in Florida and Georgia However, average results regardin g breeds, milk production and reproduction were in agreement with results for grazing based dairy farms elsewhere in the U S. It is unclear from this study if the variation in management practices resulted from a lack of knowledge or other constrains that p revented implementation of best pasture base practices. Survey results will help direct subsequent research and extension programs to gather more information, help promote sustainable agriculture and meet on. Future studies should also focus on financial performance to describe the development potential of pasture based dairy farms.

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86 APPENDIX SURVEY

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104 LIST OF REFERENCES Adams, D. C. 1992. Cattle gain faster on Tifton 85. Agricultural Rese arch. 40:19 Al Katanani, Y., D. W. Webb, and P. J. Hansen. 1999. Factors affecting seasonal variation in 90 day nonreturn rate to first service in lactating Holstein cows in a hot Climate1. J. Dairy Sci. 82: 2611 2616. Amaral Phillips, D. M., R. W. Hemken J. C. Henning, and L. W. Turner. Pasture for dairy cattle: challenges and opportunities. Accessed July 22, 2013. http://www.uky.edu/Ag/AnimalSciences/pubs/asc151.pdf Arriaga Jordan, C. M., and W. Holmes. 1986. The effect of cereal concentrate supplementa tion on the digestibility of herbage based diets for lactating dairy cows. J. Agric. Sci. 106:581 592. Bianca, W. 1965. Reviews of the progress of dairy science. Section A. Physiology. Cattle in a hot environment. J. Dairy Res. 32:291 345. Bela, B., G. Nag y and I. Vinczeffy. 1995. The influence of grazing on milk production and productive lifetime. Page 179 in t he 46th Annual Meeting of the European Association for Animal Production Prague, Germany. Cartmill, J. A., S. Z. El Zarkouny, B. A. Hensley, T. G. Rozell, J. F. Smith and J. S. Stevenson. 2001. An alternative AI breeding protocol for dairy cows exposed to elevated ambient temperatures before or after calving or both. J. Dairy Sci. 84:799 806 Combellas, J., R. D. Baker, and J. Hodgson. 1979. Concent rate supplementation and the herbage intake and milk production of heifers grazing Cenchrus ciliaris. Grass and Forage Sci. 34:303 310. Cornell Dairy Farm Business Summary 2008. Intensive grazing farms New York 2007. Accessed June 4, 2013. http://ageconse arch.umn.edu/bitstream/121811/2/Cornell_AEM_eb0822.pdf Coulon, J. B., and B. Remond. 1991. Variations in milk output and milk protein content in response to the level of energy supply to the dairy cow: a review. Livestock Prod. Sci. 29:31 47. Cowan, R. T., tropical grass legume pasture on grazing time and milk yield of Friesian cows. Tropical Grassl. 10:213 218. D airy Records Management Systems (DRMS) 2013. Dairymetrics. Raleigh, NC. Ac cessed June 4, 2013. 2013. http://www.drms.org/

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105 Dartt, B. A., J. W. Lloyd, B. R. Radke, J. R. Black, and J. B. Kaneena. 1999. A Comparison of p rofitability and economic efficiencies between management intensive grazing and conventionally managed dairies i n Michigan. J. Dairy Sci. 82:2412 2420. Davison, T. M., D. Williams, W. N. Orr, and A. T. Lisle. 1991. Responses in milk yield from feeding grain and meat and bone meal to cows grazing tropical pastures. Aust. J. Exp. Agric. 31:159 163. De Vries, A. 2009. Ranking cows for culling decisions. Pages 20 28 in Proc. Southeast Dairy Herd Management Conference. Macon, Georgia. De Vries, A. and C. A. Risco. 2005. Trends and seasonality of reproductive performance in Florida and Georgia dairy herds from 1976 to 2002 J. Dairy Sci. 88:3155 3165. De Vries, A., C. Steenholdt, and C. A. Risco. 2005. Pregnancy rate and milk production in natural service and artificially inseminated dairy herds in Florida and Georgia. J. Dairy Sci. 88:948 956. Delaby, L., and J. L. Peyraud 1997. Influence of concentrate supplementation strategy 137 in XVIII Int. Grassland Conference. Winnipeg, Manitoba, Saskatoon, Saskatchewan, Canada. Dillon, P., J. R. Roche, L. Shalloo, and B. Horan. 2005. Opt imising financial return from grazing in temperate pastures. Utilisation of grazed grass in temperate animal systems. Page 131 147 in Proc. A satellite workshop of the XXth International Grassland Congress, Cork, Ireland. Netherlands. Eberhart, R. J., L. J. Hutchinson, and S. B. Spencer. 1982. Relationships of bulk ta n k somatic cell counts to prevalence of intramammary infection and to indices of herd production. J. Food Protect. 45:1125 1128. Elbehri, A., and S. A. Ford. 1995. Economic a nalysis of m ajor d airy f orage s ystems in Pennsylvania: The Role of Intensive Grazing. J. Production Agr. 8,4:501 507. Fike, J. H., C. R. Staples, L. E. Sollenberger, and D, A, Graetz. 1997. Intensive rotational grazing systems for dairying in a subtropical environment: anim al, plant, and soil responses. Section 29:93 94 in proc. 18 th Int. Grassl. Congr., Winnipeg, Saskatchewan, Canada. Fike, J. H. 1999. Grazing Systems and Management Strategies for Lactating Holstein Cows in Florida. Ph.D. Thesis, University of Florida, Unit ed States -Florida. Fike, J. H., C. R. Staples, L. E. Sollenberger, J. E Moore and H. H. Head. 2002. Southeastern pasture based dairy systems: housing, posilac, and supplemental silage effects on cow performance. J. Dairy Sci. 85:886 878

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106 Fontaneli, R. S ., L. E. Sollenberger, R. C. Littell and C. R. Staples. 2005. Performance of lactating dairy cows managed on pasture based or in freestall barn feeding systems. J. Dairy Sci. 88:1264 1276. Gay, K. D. 2012. Strategies for Genetic Selection in Pasture Based Dairy Production. M.S. Thesis, Purdue University, Indiana. Gillespie, J., R. Nehring, C. Hallahan and C. Sandretto. 2009. Pasture based dairy systems: who are the producers and are their operations more profitable than conventional dairies? J. Agric. Resou r. Econ. 34:412 427. Golding, E. J., J. E. Moore, D. E. Franke, and O. C. Ruelke. 1976. Formulation of hay grain diets for ruminants. II. Depression in voluntary intake of different quality forages by limited grain in sheep. J. Anim. Sci. 42:717 723. Gwaz dauskas, F. C., W. W. Thatcher, C. A. Kiddy, M. J Paape and C. J. Wilcox. 1981. tham salt induced luteal regression in heifers. Theriogenology. 16:271 285. Hahn, G. L., and D. D. Osburn. 1969. Feasibility of evaporative cooling for dairy cattle based on expected production losses. Trans. ASAE. 13:289 291 Hansen, P. J. 2008. Getting cows pregnant when it is hot a growing probem with some novel s olution. Accessed June 4, 2013. http://www.animal.ufl.edu/ hansen/MSS/partners%20in%20reproduction%20newsle tter_4.pdf Hansen P. J. and C. F. Archiga. 1999. Strategies for managing reproduction in the heat stressed dairy cow. J. Anim. Sci. 77:36 50. Hanson, G. D., L. C. Cunningham, S. A. Ford, L. D. Muller, and R. L. Parsons. 1998. Increasing intensity of past ure use with dairy cattle: An economic analysis. J. Prod. Agric. 11:175 179. Holmes, W., and J. G. W. Jones. 1964. The efficiency of utilization of fresh grass. J. Br. Grassl. Soc. 19:119 129. Holter, J. B., J. W. West, and M. L. McGilliard. 1997. Predict ing ad libitum dry matter intake and yield of Holstein cows. J. Dairy Sci. 80:2188 2199 Holter, J. B., J. W. West, and M. L. McGilliard,and A. N. Pell.1996. Predicting ad libitum dry matter intake and yield of Jersey cows. J. Dairy Sci. 79:912 92 1 Hovela nd, C. S. 1996. Warm season perennial grasses. The Georgia Cattlema n. July. Accessed June 4, 2013. http://www.caes.uga.edu/commodities/fieldcrops/ forages/documents/GC9607.pdf Ingraham, R. H., R. H. Ingraham, R. W. Stanley and W. C. Wagner. 1979. Seasonal effects of tropical climate on shaded and non shaded cows as measured by

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111 BIOGRAPHICAL SKETCH Fei Du was born in Beijing, China. In 2004, s he entered the Beijing University of Agriculture for Bachelor degree in Veterinary Science. During her course of study at Beijing University of Agriculture, she was awarded the Outstanding Students Leader Awa rd (2004 2007) thrice received a Professional Scholarship, and an American Pet Food Institute Scholarship. S he also pursued a management and feeder intern ship ( 2005 2007 ) at the Beijing Zoo during the summer Her senior project at Beijing University of Ag riculture focus ed on d eveloping experiments concerning immunity to astragalus polyose in dogs Three and half years later, she transferred to the University of Tennessee, Knoxville and got her B.S. in a nimal s cience Magna Cum Laude in 2010 During the tim e she studied at the University of Tennessee, Knoxville, she worked at the University Student Center as a student assistant for 3 years and got the Unsung Hero Award. Also, when she graduated, she received a Myron Taylor Myers Scholarship (2010), and was o n the After she graduated from the University of Tennessee, she worked for 6 months as a research assistant and focused on Alzheimer disease in the Pathobiology department, College of Veterinary Medicine at the University of Tennessee, Knoxville. In 2011, she moved to Gainesville, Florida and began her M.S. in animal sciences at the University of Florida. considers working in the animal agriculture industry