Duration: 10/01/2002 to 09/30/2007

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Statement of Issues:

The US dairy industry is a changing and dynamic industry. Changes in the infrastructure, workforce, political and social involvement, globalization of markets and culture, and enhanced information, and biological technologies continuously influence the US dairy industry. An understanding of, and ability to adapt to these changes are paramount to ensuring farm profitability, and quality of life. The traditional dairy farm has taken on a new dimension as farms modernize and expand, or diversify to support the income base of multiple employees. A business-minded dairy farming culture is emerging quickly. These infrastructure changes influence the demographics of regional consolidation and concentration of dairy businesses resulting in specific unique decision support and management needs. Dairy farming is a decision-intensive enterprise. It must incorporate a holistic systems approach to defining options to maintain a profitable system that can be accountable to consumers for animal well-being, environmental impacts, and product quality. The ability to plan and direct resources and labor will determine the profitability and sustainability of the dairy enterprise. Profitable decisions cannot be made without useful decision support systems. The primary focus of NC-1119 is to develop management strategies and decision support systems that facilitate profitable decision making by dairy producers. Management is defined as a set of strategic plans for the direction and allocation of resources that are in compliance with environmental and product quality assurance guidelines under conditions of risk and uncertainty.

Justification:

Changes and trends occurring in the US dairy industry are key considerations in the NC-119 revision. Consolidation and concentration of the dairy industry continue with fewer but larger players. In 1993, 36.3% of US milk was produced by herds over 200 cows; by 2000 this had risen to 53.8%. In recent years, cow numbers have declined in states such as WI, MN, PA, NY, and TX, but increased in CA, ID, AZ, and NM. In 2001, there were 9.12 million dairy cows in the US averaging 18,147 lbs milk/cow per year. The support price of milk has decreased from $13.18/cwt in 1980 to $9.80/cwt in 2001. Milk price across the US in 2001 averaged $15.08/cwt with predictions for a decline to just over $13/cwt in 2002 (Bailey, 2001). This illustrates the driving force of the market price on profit cyclicity. Farms that can maintain cost-competitive milk production will likely survive. Consolidation has enhanced regional competition among farms. Larger farms do have advantages (e.g., volume premiums, hauling discounts, and the potential for lower costs of production with economies of scale). Smaller farms may need to diversify with value-added enterprises and new market niches to stay profitable. The 22 NC-1119 member states include 18 of the top dairy states in the US. The 22 states account for 76% of total US milk production. This illustrates the important leverage that a strong NC-1119 project can exercise in maintaining a successful US dairy industry. The Midwest (basically the 12 states of the North Central Region), which comprises a significant number of NC-1119 members, has higher costs of production than the Pacific states. However, the best managed farms in the Midwest have the lowest cost of production, but many dairy farms in this region are more highly leveraged than those in other regions (Conlin, 2000). These situations demand decision support systems such as those to be developed in the proposed project.

Over 9 million replacement heifers are raised annually in the US. The changing demographics of dairy operations create a greater demand for, and shortage of, high quality replacements heifers. Dairy heifer enterprises are emerging as a self-supporting sub-industry. While most dairy heifers are still retained and raised in the dairy operation, an increasing number are raised in specialized operations under contractual agreements. Specialized heifer operations commonly move calves and heifers among multiple farm sites which increases transportation stress and biosecurity concerns. Environmental implications of nutrient excretion from heifer rearing operations are often overlooked. This is especially pertinent as volatilization of N becomes more of a concern in the next 5 years and is added to the current environmental compliance criteria. There is a dearth of dairy heifer nutrition and management information related to improving mineral utilization and reducing fecal and urinary excretion. This is especially true for P. Little technology has been employed to dairy heifer replacement management in a holistic systems approach to reduce costs, increase feed efficiency, and reduce nutrient losses to the environment. Success of the dairy heifer enterprise depends on optimal early health and growth of the young calf. A clearer understanding of the biological processes and interrelationships of calf morbidity will enhance our ability to optimize calf growth and health. Decision support programs are needed for short and long-term analyses of dairy heifer enterprise management strategies. Risk analysis will be a critical approach to evaluate options and define optimal solutions.

Doubtless, sustainable dairy production systems of the future will optimize utilization of physical (e.g., nutrients and energy), human, capital, and informational and analytical resources to strike an acceptable balance between profitability and environmental stewardship. Optimizing animal performance necessitates advances in management and feeding systems. Critical issues are improving cow health and well being, understanding cow group behavior, improving nutrient and energy utilization by cows and by farms, and improving management of groups of cows and heifers, and whole farm systems. Optimizing the above-mentioned factors as a collective set must result in effective environmental stewardship and sustainable dairy farming systems. Most importantly, as a focus of this project, future viability of dairy businesses will depend on comprehensive databases and management models. These decision support systems must integrate biological, management, and financial factors and provide analysis of appropriate options and directions. Volatile milk and feed prices, and narrowing profit margins all allow little room for error when management and financial decisions are made.

Decision support systems are broadly defined as any algorithm, decision aid tool, or management strategy, in written or computerized form. Bawden (1991) described the emergence of systems thinking and practice as the most useful paradigm for generating and transferring information to livestock producers. The NC-1119 committee proposes that a systems paradigm will remain the most useful approach to help dairy managers consider as many factors as possible prior to making a decision. The proposed project will have decision support systems addressing: 1) calf and heifer nutrition, management, environmental impacts, and economics; and 2) lactating and dry cow nutrition, management, environmental impacts and economics.

Qualifications of Technical Committee Members that contribute to the advantages of a cooperative approach: We believe strongly that effectively integrated decision support systems only can be developed and implemented through a multidisciplinary team approach. Members of this Committee are experts in dairy management, data and statistical analyses and computer information systems, operations management, animal science, nutrition and health, animal behavior and well-being, and economics. Research data and information from all of these disciplines are needed for evaluation and incorporation into dairy management decision support systems. Proposed work will be possible only through extensive collaborative efforts among Technical Committee members. Most members from the current project (1997-2002) will continue as participating states plus new members have been added. In the current project, a total of 21 states were active. Ten states contributed to Objective 1 and 15 states to Objective 2. Without this comprehensive body of work by the Technical Committee over the next 5 years, there will be less information for decision support by dairy managers under future, environmental and quality assurance compliance regulations. In addition, the decision support information will be critical to complement the outcomes of other regional projects with implications to the dairy sector.

This revised project integrates in its proposed work certain agricultural production, processing and distribution priority crosscutting research areas and objectives established by NCA/NCRA Committees. These include: a) develop alternative agricultural production systems to enhance economic competitiveness in the rural landscape; b) develop improved animal, plant and microbial production, processing and marketing systems that are competitive, profitable and environmentally sound over the long term; c) construct an information base and methodologies to help form sound public policy that minimizes conflicts resulting from divergent viewpoints of citizens, both urban and rural; and d) assemble and maintain regional, national and international data bases on production systems and use them for modeling and decision support. The proposed project also relates to priorities established for FAIR (Food Animal Integrated Research) 2002. The project focus encompasses FAIR 2002 objectives: a) enhance production efficiency and economic strategies at the farm and ranch level; b) help producers, policymakers, and other stakeholders decide what animal agriculture will look like in the future; c) determine how production and processing practices affect food quality; d) develop optimal production practices that promote animal health; and e) reduce any adverse environmental effects of animal agriculture.

Related, Current and Previous Work

Research in dairy herd management has been directed largely at developing strategies to enhance profitability of dairy farming. To enhance herd profitability, adoption by producers can be quite rapid with involvement of researchers and extension personnel. Members directly associated with NC-1119 have research and extension appointments, enabling rapid dissemination of research outcomes (recommendations, decision support information and systems) of this regional project.

National and international visibility of NC-1119 members: Members of NC-1119 are prominent participants in American Dairy Science Association (ADSA) activities. The NC-119 Dairy Management Committee have organized special NC-119 symposia (see Appendix H) and presented papers at national ADSA meetings and Midwest ADSA/ASAS Sectional Meetings. The 7th Revised Edition of the Nutrient Requirements of Dairy Cattle (NRC, 2001) was a compilation by the Committee on Animal Nutrition and Subcommittee on Dairy Nutrition, the latter being well represented by two NC-1119 members. A number of members contributed to the establishment of the Professional Dairy Heifer Growers Association (PDHGA) in 1997. The NC-1119 members have remained active as organizers or participants in PDHGA activities at the national and regional levels during 1997-2001. Other involvement of NC-119 members is listed in Appendix E.

Brief perspective on the current NC-1119 project: The current project (1997  2002) identified the need for coordination of efforts and collaboration among scientists for an integrated systems approach. The last 5 years focused on integration of dairy management strategies to develop management and economic evaluation tools. Much was accomplished. A summary of publication by category are shown in Appendix D, Table 1. A complete listing of publications by state is summarized in Appendix I. This project will build on, and extend, collaborative interstate strengths and integrate expertise of members in dairy management, data and statistical analyses and information systems, operations. A new focus will address the integration of environmental impacts into management decision strategies. During 1997-2002, NC-1119 collaborative research studied nutrient requirements emphasizing dry matter intake and nutritional management for calves, heifers, and lactating cows (Greenfield et al., 2001; Franklin et al., 1998; Mashek and Beede, 2000; Quigley et al., 2000; Radcliff et al., 2000; Hoffman et al., 2001; Lammers and Heinrichs, 2000; Bethard et al., 1997; Bethard et al.,1998). Other research focused on animal behavior, stress management (Eicher et al., 2001) and general management (Bewley et al., 2001; Wagner et al., 2001), and herd health and biosecurity (Berry et al., 1997; Berry, 2001; Faust et al., 2001). NC-1119 also contributed important information to the understanding of milking center operation (Smith et al., 1997) and housing modifications (Meyer et al., 1999). Various research components were integrated into decision aid tools for improvement of management practices as exemplified by Web-based expert system software like Dairy Manager and Dairy MAP in Georgia and Florida. Among others, Hoekema et al. (1999), Hoffman et al. (1999), Jose and Grant (1998), DeVries and Conlin, 2000, St-Pierre and Glamocic (2000), St-Pierre and Jones (2001), and Gabler et al. (2000) provided new information and methodologies for financial analyses, decision making, and management tools.

Identifiable Research Areas: The focus of this project revision will be a systems approach, beginning from basic (e.g., digestive tract function or individual animal responses) to whole-farm levels (e.g., a multi-criteria optimized animal performance and nutrient management system). This approach entails evaluation of a range of management options and strategies. Identifiable research areas will focus on components that will contribute to an understanding and economic evaluation of the whole-farm dairy enterprise. New research will focus on acquiring knowledge that will enhance predictions for a specific management system, while avoiding unnecessary duplication of efforts.

Research related to calves and heifers has been a component of NC-1119 (Albright, 1996). A core group of the committee has focused efforts on calves and heifers which has provided continuity to interstate collaboration and dissemination of information. States that will contribute to youngstock objectives include PA, WI, NH, NY, VA, IA, MN, KY, KS, LA, IN, SD, and MI. Raising replacement heifers efficiently and economically plays a major role in the profitability of dairy farms and specialized commercial heifer operations. The cost of rearing heifers is the second largest behind feed costs in annual operating expenses of dairy operations (Cady and Smith, 1996). An Intuitive Cost of Production Analyses (ICPA) program was developed to show economic costs and labor efficiencies of raising dairy heifer replacements on commercial WI farms (Hoffman et al., 1999). These guidelines indicate the large variation at all phases of production. To examine the economic impact of different nutritional management strategies, it is essential to consider the overall herd structure and dynamics as well as the heifer (Tozer and Heinrichs, 2000). To define the economic impacts or the least risky option for rearing heifers, management and productivity data need to be collected and analyzed using techniques such as partial budgets, linear and dynamic programming, and risk analysis. These management options lend themselves to both simple and complex analyses as demonstrated in previous dairy heifer (Mourits et al., 1999; Gabler et al., 2000; Cady and Smith (1996); Martin and Wiggins, 1973). These economic analyses did not examine different nutritional strategies and their impact on rearing costs, productivity, longevity, and herd profitability.

Optimum feed bunk management resulted in 10-15% increases in feed efficiency of yearling beef steers (Bierman and Pritchard, 1996). Target feeding concepts have also supported economic efficiencies (Schmidt et al., 2001a,b; Shirley et al., 1999; Isch et al., 1999). Such improvements in feed efficiency of dairy heifers could reduce producer costs and decrease manure (nutrient) output. Technologies of prescription feeding and bunk management have not been implemented or are not utilized in feeding dairy heifer replacements under different housing environments. Integration of pasture into a growing heifer operation provides another opportunity to reduce input costs (Emmick and Toomer, 1991). Precise environmental impacts of pasture-based heifer raising systems have not been clearly defined.

The recent NRC (2001) publication of nutrient requirements of dairy cattle has comprehensive chapters related to calves and heifers. Substantial progress has been made in predicting requirements for dairy heifers using NRC (2001) and the Cornell Net Carbohydrate and Protein Systems (CNCPS) model (Van Amburgh et al., 1998). Cole et al. (2000), Garthwaite et al. (1999), Hoffman et al. (2001), Gabler et al. (2001) evaluated N requirements of dairy replacement heifers. From these studies, reasonable dietary N guidelines are available, but the guidelines did not implement nutritional strategies to optimize dietary N feeding to reduce N excretion. Models need to be developed to pinpoint protein requirements for calves and heifers in relationship to managing N and environmental impacts, similar to approaches for dairy cows (Kohn et al., 1998; Dou et al., 1996). Requirements for macromineral elements have not been adequately evaluated in light of modern genetics, although optimal growth curves have been adjusted upward over the last decade (Hoffman, 1997; Heinrichs and Losinger, 1998). Recent developments in feeding programs are encouraging accelerated growth patterns (Drackley, 2001; Diaz et al., 2001; James, 1998). These may result in overfeeding of nutrients with implications to environmental concerns, especially P. The authors of this cooperative project suspect that unnecessary supplementation of P, is taking place in the dairy heifer industry. The same consideration should be given to key microminerals fed to heifers.

An integral part of successful heifer management system is to maintain a low incidence of disease. A number of studies addressed calf immunity, morbidity, mortality, and nutritional management on calf growth and survival (Goodier et al., 2001; Faust, 2001; Hammell et al., 2000; Miller, 2000; Donovan et al., 1998; Britney et al., 1984). There is still great variability in extent of calf morbidity among herds. It is hypothesized that there are complex biological processes in calves that contribute to this variation which should be elucidated. Knowledge of the social behavior of calves and their response to physiological or environmental stressors and management systems that reduce stress are important aspects of the calf and heifer enterprise (Eicher et al., 2001; Johnson et al., 2001; Hindhede et al., 1999; Reber et al., 1999; Reed et., 1999; Wilson et al., 1999; Jago et al., 1998; Place et al., 1998; Spain and Spiers, 1996). Transportation and comingling also present issues of biosecurity and stressors that have implications to disease resistance and growth patterns (Eicher, 2000; Schmidt et al., 2001a). The NC-1119 committee proposes to continue a focus in behavior and stress management of calves and heifers under varying nutritional and management systems.

The transition period from the non-lactating pregnant state to the lactating state is a key period in the lactation cycle. Yet, it also imposes challenges on dairy cows that may impair immune function, reduce feed intake, milk yield, and reproductive performance in the next lactation. Periparturient diseases and lameness are important determinants of premature culling (Gillespie, 1998). Fetal growth (Bell et al., 1995; McNeill et al,. 1997) and lactogenesis (VandeHaar et al., 1999) exponentially increase nutrient requirements during late gestation when feed intake declines (Bertics et al., 1992). Failure to meet nutrient requirements in late pregnancy can lead to negative energy balance (Park et al., 2000), metabolic problems, and poor lactational performance post-calving. Implementing management and nutrition practices to reduce health disorders associated with parturition and early lactation are paramount for herd profitability. Whereas, transition cows and their management was a focus in the current NC-119 project (e.g., Greenfield et al., 2001; Mashek and Beede, 2000; Moore et al., 2000), other new and innovative management and nutritional strategies for dairy systems are yet to be developed and tested.

Studies must challenge conventional dogma about dairy management practices. Van Amburgh et al. (1997) showed that delayed initiation of insemination and pregnancy, and administration of exogenous bST altered the shape of the lactation curve and challenged conventional ideas about standard lactation lengths. Other potential management strategies that must be evaluated or re-examined as applicable in future dairy management systems include: 1) milking pregnant heifers and cows before parturition (pre-milking) (Schutz et al., 2001); 2) altering length of or eliminating the conventional 60-day dry period; 3) implementing programmed exercise for dry cows (Davidson and Beede, 2001); 4) developing and instituting management/nutritional strategies to boost immune function; and 5) prepartum habituation to postpartum routines (e.g., holding pen and milking parlor procedures; Pajor et al., 2001). After studies characterize cows responses to potential management strategies, modeling will be needed to gain insight into herd profitability (e.g., influence of length of lactation in combination with length (or existence) of dry period, and other possible management strategies). In the past, NC-119 members focused on cow behavior and feeding systems (Grant and Albright, 1995). However, little baseline data on cow behavior in confinement systems with realistic group sizes are available. A priority will be to gather time-series behavioral data from cows in larger management groups to assemble time budgets that reflect what is expected in typical free stall housing systems.

NC-1119 members through the current project made major contributions to the new dairy NRC (2001) publication. However, much is yet to be learned about provision of specific amounts and quality of nutrients and synchrony of supply to optimize utilization. The NRC (2001) identified major gaps about provision of certain dietary nutrients (e.g., mineral elements, carbohydrates, and protein) and feeding of dairy cattle. NRC (2001) did not incorporate physically effective neutral detergent fiber (peNDF) into the model because of lack of uniform methodology for measuring peNDF and paucity of accurate values for the wide range of dairy feeds (particularly byproducts). It is necessary to determine peNDF values of byproducts and their interactions with different forages. Dairy enterprises also often purchase waste products (e.g., feed byproducts or co-products) from other industries. We need to accurately model animal responses and economic and environmental consequences of feeding byproducts to dairy cattle to develop profitable and sustainable feeding systems.

Improvement in protein utilization in the rumen and more accurate determination of metabolizable amino acid requirements can result in greater milk and milk protein yields. Of even greater importance in future dairy systems will be improved dietary protein utilization to reduce N loss to the environment (Tyrrell, H.F., personal communication, 2001; NRC, 2001). Many U.S. dairy operations are facing environmental constraints that are expected to be major determinants of future milk production and profitability. Nitrogen and P excretion can be reduced by feeding closer to animals nutrient requirement (NRC, 2001). Management strategies such as tighter grouping of animals to pinpoint nutrient requirements can reduce N excreted (Dunlap 1997; St. Pierre and Thraen, 1999). A general method for deriving the production response of groups of animals to nutrients in diets was proposed (St-Pierre and Thraen, 1999). One new approach in the replacement project will be unique in that it will factor in variance (nutritional risk) in feed composition and will quantify biological and economic components to develop an objective function suitable for whole farm system economic optimization while minimizing P and N excesses. A simple but relatively accurate method of estimating urinary N excretion of dairy cows was reported (Kauffman and St-Pierre, 2001) and a model also was evaluated for P excretion (Beede and Davidson, 1999). Thus, we shall be able to estimate influence of dietary manipulations on air (NH3) and water quality.

Further modeling approaches will address how animal flow, and optimal culling and replacement policies are affected by environmental constraints. In future dairy enterprises, the ability to integrate data from production, financial and management databases into information and decision support systems will be paramount to optimize efficiency and economic sustainability. In the last 5 years, several member states (GA, FL, NE, VA, MI, TX) developed and (or) assessed financial databases (e.g., MI Telfarm). These databases provide the framework for further expansion and development of financial benchmarks. Wolf et al. (2000) examined cost of production using the database and enterprise accounting methods. Workers in GA, FL, and TX developed web-based programs for analysis of records. These programs will be expanded in the new project to include economic data in an expert system that will integrate financial and herd performance records for decision support. Culling decisions represent a major challenge to dairy managers (Lehenbauer and Oltjen, 1998). Historically, culling and replacement, and breeding (insemination) decisions have been based on simple, independent empirical targets or thresholds. Descriptive information (e.g., DHIA) reveal an average culling rate that is consistently different from the optimal rate for maximum economic return. Most advanced replacement models are based on dynamic programming (DP; e.g., DeLorenzo et al., 1992; Houben et al., 1994). These models are less than realistic because they assume a constant herd size and unrestricted supply of replacement animals, and do not consider specific constraints (e.g., a minimum monthly cash flow requirement). Ben-Ari and Gal (1986), Kristensen (1992), and Houben et al. (1995) combined DP with other approaches to address herd level constraints; but, their approaches remain computationally intense and non-optimal. New methods coupled with increased computer power can make these models possible and more realistic. Economics of reproductive management (Wolf, 1999) and reproductive decision making under labor constraints (Risch and Wolf, 2001) were evaluated. Committee researchers must develop further and implement animal culling and replacement models/strategies that integrate interrelationships and constraints of important economic, biological, financial and management factors.

A review of the current active Northeastern, North Central, Southern, and Western State Associations of Agricultural Experiment Station Directors Multi-State Project listing in conjunction with State Agricultural Cooperative State Research Education and Extension Service (CREES) administrative directives was undertaken to confirm the relevance of the focus for the NC-1119 revision. Of the 274 multi-state projects in the current listing, none address the holistic systems application to dairy farm and dairy herd decision support systems with the cooperative approach possible with NC-1119. Other information relative to this review is in Appendix F.

Objectives

1. To evaluate and develop nutrient utilization schemes for dairy herd replacement heifers with implications to economic efficiencies and environmental impacts. <ol type=a><li>To enhance understanding of nutrient requirements of young calves and growingdairy heifers and identify nutritional strategies that maximize the efficient use of dietary nutrients and reduce excretion into the environment<li>To evaluate effects of different nutritional management strategies on performance and economic efficiencies of dairy replacement heifers<li>To elucidate metabolic processes and endocrine interrelationships in the biology of colostrum and nutrient intake, and environmental stressors on immune function and calf performance</ol>
   
2. To develop strategies and systems to optimize nutrient utilization, economic and financial returns, and environmental goals for management of dry, pregnant and lactating dairy cows.<ol type=a><li>To develop and evaluate management and feeding systems for optimal cow performance, comfort, well-being, health and behavior<li>To address environmental challenges of dairy production and determine strategies to achieve environmental goals<li>To develop and expand financial, production, and management databases, perform financial analyses, and integrate data and information into decision support systems to optimize efficiency of dairy management systems<li>To develop strategies and models for more profitable culling, replacement and breeding decisions - replacement economics </ol>
   

Methods

One or more experiment stations will hold responsibility for development and dissemination of research findings and (or) decision support models produced under each objective or sub-objective. Lead and contributing states to each objective and sub-objective are indicated throughout the Methods description. Objective 1: To evaluate and develop nutrient utilization schemes for dairy herd replacement heifers with implications to economic efficiencies and environmental impacts. A. To enhance understanding of nutrient requirements of young calves and growingdairy heifers and identify nutritional strategies that maximize the efficient use of dietary nutrients and reduce excretion into the environment (Lead: NY, NH, WI, and PA: contributors: MN, VA, KS, SD, IA, LA, and KY). We propose to develop nutritional and management strategies for utilization of energy, protein, amino acids and selected macrominerals that meet requirements of young calves and growing heifers for optimal growth, and reduced excretion into the environment. Studies will be used to validate and (or) refine the current NRC (2001) requirements. During the previous project period, an effort was initiated to define requirements for total protein and specific amino acids, and the protein:energy interrelationships for rapidly growing dairy calves and heifers. Dietary protein (N and amino acids) requirements of replacement heifers have been investigated, but little research has focused on maximizing dietary protein utilization. Requirements of macrominerals for optimal utilization in growing heifers have not been evaluated in light of modern genetics under varying nutritional management strategies. Pre- and post weaned heifer studies will compare conventional nutritional strategies with intensive management of feeding regimens. Identifying nutritional strategies that maximize efficiency of use of dietary protein (N and amino acids) and minimize N excretion (NY, NH, WI, PA, VA, IA, SD, KS, and LA) and that maximize the utilization of dietary Ca and P and minimize excretion of these minerals (WI, MN, and KY) will be the focus of studies. Example procedures are outlined in Appendix G. We shall use standard procedures for measurements and data collection to allow for integration of data summaries among stations. Measurements may include body weight, wither or hip height, length (pins to withers), girth, and body condition from birth to first calving. Performance, feed intake, housing and health management will be documented. Additional measurements may include blood metabolites and nutrient digestibility parameters. Where possible, growth and lactation performance will be followed through first lactation. B. To evaluate effects of different nutritional management strategies on performance and economic efficiencies of dairy replacement heifers (Lead states: PA, WI; contributors MN, IA, KS, VA, MI). We propose to examine the economic impact of different nutritional management strategies for dairy replacement heifers in relation to the overall herd structure and dynamics in conjunction with risk management analyses of the heifer enterprise. Management, and productivity data will be collected and analyzed using techniques such as partial budgeting, linear and dynamic programming, and risk analysis. The goal is to determine the most cost effective and (or) least risky methods of rearing replacement heifers. Specific studies will be implemented under varying housing environment and nutritional management regimens. Pasture based systems will be compared to confinement feedlot scenarios. Group sizes and feeding strategies (e.g., prescribed feeding and use of ionophores) will be varied with age to explore options for application in the commercial operation to support optimal growth, health and body condition. A focus will be feed bunk management and prescribed feeding under the housing, management and nutritional scenarios imposed. Of particular importance will be the overall effect on the longevity of the dairy heifer in herds. Risk analyses of results from different nutritional management strategies on the performance and economic efficiencies of dairy replacement heifers will be coordinated by PA, with contributing states (WI, MN, MI, IA, VA, and KS). The development of prescribed feeding and growth systems for dairy heifer replacements that optimize feed efficiency and productivity, and minimize nutrient (N,P) excretion will be a collaborative effort of KS, WI, MN, MI, NY, PA, and IA in coordination with lead states in subobjective 1A. Minnesota and WI will coordinate the economic evaluation of various dairy heifer-housing systems (confinement and pasture). The economic evaluation of feed bunk management strategies for dairy heifer feeding systems will be a focused effort by WI. C. To elucidate metabolic processes and endocrine interrelationships in the biology of colostrum and nutrient intake, and environmental stressors on immune function and calf performance (Lead: NY, KY, IN; contributors: KS, IA, LA, VA, SD, PA, MN.) We propose to elucidate biological processes that differentiate the ability of individual calves to resist or succumb to high morbidity beyond that traditionally associated with passive immunity transfer. This will allow for a greater understanding of mechanisms governing calf health and growth. We also propose to develop integrated management and decision support procedures for the effects of environmental stressors on immune function, performance, and well-being of calves. Morbidity and mortality in dairy calves are largely attributed to a lack of passive transfer of immunity from the dam via colostrum. However, other factors contribute to morbidity in calves, including nutrition of the dam, timing of colostrum feedings, quantity of colostrum fed, colostrum quality and handling, environmental stressors, and absorptive capabilities of the calf. The increasing number of specialized contractual rearing operations of dairy calves and heifers has changed demographics of these systems as more animals are moved between farm sites. This subjects animals to transportation stress and general health issues that influence the profitability of youngstock operations. Nutritional supplements and vaccinations for dry pregnant cows for enhancing colostrum quality will be evaluated (KY, MN, IA, and LA). New York will coordinate studies with contributors KY, KS, SD, MN, NH, IA, PA and VA to define factors that contribute to the growth of calves and affect the homeorhetic mechanism that allows for additional nutrients to be partitioned away from growth and towards defense mechanisms. These factors will be measured in relation to passive transfer of immunity and serum protein status. Indiana and KY will focus efforts on effects of stressors such as water quality, environmental temperature, humidity, and transportation on the immune system of calves and heifers as well as their performance and well-being. Indiana in collaboration with MN will initiate a study on effects of disease and stress on calf mineral element status. Objective 2: To develop strategies and systems to optimize nutrient utilization, economic returns, and environmental goals for management of dry, pregnant and lactating dairy cows (Leads: KS, MI, MN, NE, NH, OH, PA; contributors: CA, FL, IA, IN, KY, NY, SD, VA, WI). A. To develop and evaluate management and feeding systems for optimal cow performance, comfort, well-being, health and behavior. We propose to: 1) develop management and nutritional strategies to optimize performance and profitability of transition cows (from dry to lactating state) during the ensuing lactation; and, 2) discover and identify economical and environmentally effective feeding and grouping strategies throughout lactation and the dry period. Management strategies during the transition period have large impact on productivity, health, and profitability of lactating dairy cows. By coordinating research plans via NC-1119, core measurements that best quantify and characterize the effectiveness of potential management strategies and systems will be made at each station to reduce duplication and allow replication of these measurements across different geographical regions of the U.S (MI, OH, VA, FL, and NY). See Appendix G for some study method options. Other research will assess digestive function and ruminal adaptations (microbial and fermentation) of the transition cow and how these functions affect subsequent lactational performance (KS). Diet formulations will be assessed with the aim of optimizing diets to meet nutrient and energy requirements during transition and to prevent ruminal and metabolic disorders (KS, IN). A sub-acute ruminal acidosis (SARA) model, developed during the previous NC-1119 project (DeFrain et al., 2001), will be used to evaluate diet formulations for early lactation cows. Studies also will assess dietary factors that may have benefits on immuno-competence and health of periparturient cows and their offspring (KY, MI). Several gaps in our knowledge of dairy nutrition were identified in the NRC (2001) publication. We propose to evaluate biologically and economically (benefit-to-cost ratios; income over feed costs, and critical decision points) four major components of the nutritional management system: 1) water nutrition and quality (MN, MI, IN, KY, SD); 2) mineral nutrition, especially as related to P excretion for sub-objective 2B (MI, SD, OH); 3) carbohydrate utilization as related to peNDF and byproducts (NE, SD, KS); and, 4) ruminal degraded and metabolizable protein, and amino acid requirements pre- and postpartum (NH, IN, OH, NE, and SD). Critical decision points in the process of developing optimal management and nutrition programs will be identified and economic impact of alternative decisions evaluated (FL, OH, and VA). Housing systems, feeding systems, animal handling options, animal behavior, and time and operations management budgets will be evaluated (NE, IN, KS, and WI). A major focus will be to evaluate profitability, labor efficiency, and environmental impacts of alternative feeding strategies in grazing versus confinement feeding systems (NE, IN, MN, PA, KS, and WI). Economic consequences of periparturient disease incidences, lameness, and their interrelationships with premature culling and financial implications will be studied and modeled (CA, FL, SD, IA, PA, and MI). A simulation model will be built from differential equations describing epidemiology of diseases in question. B. To address environmental challenges of dairy production and determine strategies to achieve environmental goals (Lead: NE, OH, MI, VA; contributors: CA, FL, GA, IA, IN, KS, KY, MN, NH, PA, SD, WI). We propose to evaluate utilization of byproducts from ethanol and beverage production and wet and dry milling in rations at various stages of the lactation cycle. Another major goal of this sub-objective is to examine interrelationships of nutritional, environmental, and economic (financial) components to develop decision support tools and systems. Typically these feeds are relatively high in P and low in protein concentration and (or) quality. Feeding experiments will be conducted to determine optimum extent of replacement of feeds with byproducts. Test diets will be formulated using NRC (2001) recommendations. Feeding wet versus dry byproducts and methods of storing wet byproducts will be evaluated (NE, KS, and SD). An initial phase will be to build a database of nutrient analyses to estimate correlations and covariances among nutrients within a set of feeds (including byproducts) commonly fed. This database will be augmented with analyses conducted by project collaborators as they evaluate various byproducts plus the large database of feed analyses from NRC (2001). These variance and covariance estimates will allow development of the nutrient response model and will be used in mathematical formulations of rations as described by St-Pierre and Harvey (1986b) (OH, PA). Participants in this objective will meet twice annually (Midwest and national ADSA meetings) to coordinate activity in addition to annual NC-1119 meetings. A goal of this sub-objective is to develop preliminary data and analyses that will allow submission of an IFAFS proposal by 2004. The nutrient response model will be developed according to methodology of St-Pierre and Thraen (1999). Validation of the function will be according to methods described by St-Pierre (2001). For on-farm nutrient management to achieve environmental goals, research to identify optimal grouping strategies will utilize algorithms to determine how best to group cows and when to move cows from group to group throughout lactation and the dry period (OH, FL, SD, KS, MI). Experiments will be conducted to generate data to develop decision aids and these aids will be refined based on patterns of changes in milk yield, body condition score, and body weight (OH, FL, MI). Research described and data collected under this sub-objective also will be used to optimize the influence of and potential alleviation of dietary contributions to nutrient excretion affecting air (ammonia emissions) and water quality (contamination by P and N). There are extensive efforts nationally to address whole-farm nutrient flows, including P and N flows through dairy systems. The approach in this project will be unique in that it will factor in variance (nutritional risk) in feed composition (based on the feed ingredient composition database describe previously). The approach will also quantify biological and economic components to develop an objective function suitable for whole farm system economic optimization while minimizing P and N excesses (OH, MI). A goal is to develop a set of preliminary data and analyses for submission of an IFAFS proposal by 2003. Concurrent and in coordination with other efforts under sub-objective 2B will be examination of energy flow and optimization of energy use in dairy farms (VA, OH, GA). The approach to the problem will start with a search of scientific literature followed by construction of theoretical energy flow diagrams for typical dairy farms. An important contribution will relate and integrate the widely different forms of energy imported and exported from dairy farms (e.g., electricity, fuel, feed, milk). The review and theoretical framework should reveal relationships for which parameters are not known. A goal will be energy cycle models for dairy farms that also are tied by price relationships to traditional measures of energy availability and cost in society. Validated models will be used to delineate management responses to volatile or high-energy costs. C. To develop and expand financial, production, and management databases, perform financial analyses, and integrate data and information into decision support systems to optimize efficiency of dairy management systems (Lead: GA, TX, FL; contributors: MI, NE, and VA). We propose to develop pertinent databases plus expansion of existing databases for performance analyses and to integrate different kinds of data into decision support systems under all sub-objectives of Objective 2. Core parameters for financial, production, and management databases will be identified by Technical Committee members (GA, TX, FL, MI, NE, VA). An attempt will be made to provide for central data format and storage so that all members can access unified databases. Financial databases also will be developed based on interviews and surveys of dairy producers for management and production data. Financial data will be collected through the Telfarm financial/accounting system at Michigan State University. Databases will then be analyzed to create financial benchmarks. These benchmarks will be tied to different production systems and used for evaluation. Financial data also will be connected to production benchmarks that are created from DHIA (or other) production performance data from various participating states. These benchmarks will be used in a decision support system (expert system) to evaluate producers practices and to make recommendations. These evaluation systems will be Web-based and readily accessible. D. To develop strategies and models for more profitable culling, replacement and breeding decisions - replacement economics (Leads: FL, TX, MI; contributors: GA, PA, VA, and WI). Culling, replacement, and breeding decisions based on sound economic frameworks are crucial. However, these decisions must be based on multiple input that vary through time and in different environmental conditions. Culling and replacement models will be developed and evaluated using several approaches. Capitalizing on the databases constructed as part of sub-objective 2C, TX will develop a systematic plan for culling based on historical and projected management strategies. In MI, culling decision models will be built from previous work utilizing stochastic DP and portfolio theory. Models will be validated both using large data sets from DHIA and the National Animal Health Monitoring System (NAHMS) and farms. A fast DP model (DeVreis, A., personal communication) similar to that of DeLorenzo et al. (1992) has been developed for research in FL. The underlying biological model of cows currently is updated. The DP model also ranks cows for future profitability at any point in time as a consequence of (Harris, 1990). The different approaches to develop culling and replacement models will be evaluated and compared through sensitivity analyses utilizing independent data sets. In FL, breeding (reproductive) policy decisions will be assessed using a guided search method or algorithm. The model also will be used to evaluate various reproductive management strategies. Additionally, MI will evaluate and compare reproductive technology adoption decisions estimated from NAHMS and survey data using a multivariate logit model. Functional form of the farm-level technology adoption model will be a multivariate logit with explanators being farm characteristics (e.g., herd size), operator characteristics, and industry conditions (milk and feed prices). From the farm-level decision model, results will be aggregated to industry level impact figures. Additionally, reproductive decisions under constraints will be modeled in a LP framework with labor and capital constraints.Work in WI will characterize prevalence and severity of abnormally high culling rates in commercial dairy herds. Culling rates will be compared for individual herds. A follow-up survey of producers will determine reasons animals left herds and any changes to on-farm management practices. Herd production and culling information will be summarized and related with facility and management procedures reported.

Measurement of Progress and Results

Outputs

• <B>Objective 1A</b><ul> <li>standard procedures data collection and measurements established <li>maximize efficiency of dietary protein and minimize N excretion for conventional and intensive nutritional management systems; validate or refine NRC. <li>nutritional strategies for utilization of Ca and P to minimize excretion; validate or refine existing prediction models (NRC); new spreadsheets and software available to update protein and mineral requirement predictions</ul> <b>Objective 1B</b><ul> <li>validation of scenarios for application to nutritional management data collection from controlled studies and commercial farms <LI>risk analyses to determine best management practices; monitor overall effect on heifer longevity</ul> <b>Objective 1C</b><ul> <LI>establish standard procedures for data collection. Investigate calf morbidity variability in multi-locations; <li>define more precisely biological factors that account for variability in calf morbidity and develop support computer simulation models for field application. <LI>establish nutritional and health management strategies to overcome effects of stress on immune system suppression; develop decision support tools for field application.</ul> <B>Objective 2A</b><ul> <li>definition of optimal dry period length; risk analysis for variable dry periods; implications to culling decisions <LI>economic and management strategies formulated for communication to producers ands professionals <LI>critical decision points for optimal nutritional programs defined and economic impact of alternative decisions <LI>time budget spreadsheet development; publication on behavioral impact of overcrowding/comingling <LI>biosecurity investment model and risk assessment <LI>financial implications of periparturient disease research; survival and economic analysis publications</ul> <b>Objective 2B:</b><ul> <li>publications of database development; variability of nutrient composition of feeds studied for adjustment of ration formulations to account for composition variance <LI>summary of published research from literature <LI>summary bulletin available <LI>multi-criteria for optimization model for N and P in dairy farm systems using Windows based software</ul> <b>Objective 2C</b><ul> <li>database programs to evaluate dairy systems and alternatives</ul> <b>Objective 2D </b><ul> <LI>updated biological and optimization models of culling and replacement policies under herd constraints <LI>economic benefit analyses; real-time decision model for stakeholders and extension; dairy management protocols for dairy farms; publications</ul>

Outcomes or Projected Impacts

• Producer adoption of project outcomes and best management practices related to the dairy heifer enterprise to yield more profit and reduce nutrient excretion to the environment
• Provide producers with an understanding of the risk factors that affect the costs of raising dairy heifers
• Decision aids for prescribed feeding and growth systems for dairy heifers
• Unique N and P nutritional regimens for dairy heifers incorporated into prediction equations (NRC)
• Precise description of the biological processes to reduce calf morbidity and mortality to improve longevity, performance and profitability of the enterprise
• Outcome/Impact 6; Enterprise-level economic analyses of alternative management, nutrition (e.g., dietary supplements and byproduct feeds) and feeding systems; Outcome/Impact 7; Provide management and nutritional recommendations for transition and lactating cows to be integrated into whole-herd analyses and decision support models; Outcome/Impact 8; Decision support systems for dairy producers and their advisors to aid in making profitable and environmentally sustainable on-farm decisions; Outcome/Impact 9; Improved understanding of methods and tools applicable to dairy herd management research (critical to future NC-1119 objectives), and identification of direction and needs for further dairy management research.

Milestones

(0): <tr> <td><B>Sub-objective<B></td><td colspan=5><B>Project Year </td></tr> <td><B>Objective 1A </td><td><B>1</td><td><B>2</td><td><B>3</td><td><B>4</td><td><B>5</td></tr> <TR> <td>Output-standard procedures data collection and measurements established</td> <td>X</td><td>.</td><td>.</td><td>.</td><td>.</td></tr> <tr><td>Studies Coordinated to understand protein requirements; output - maximize efficiency of dietary protein and minimize N excretion for conventional and intensive nutritional management systems; validate or refine NRC. </td> </td><td>.</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <tr><td>Establish commercial databases for macromineral intake on farms; initiate station studies to evaluate macromineral requirements; output  nutritional strategies for utilization of Ca and P to minimize excretion; validate or refine existing prediction models (NRC); new spreadsheets and software available to update protein and mineral requirement predictions; </td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <td><B>Objective 1B </B></td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td></tr> <TR> <td>Establish extreme scenarios to validate risk analyses techniques; output  validation of scenarios for application to nutritional management data collection from controlled studies and commercial farms.</td> <td>X</td><td>X</td><td>X</td><td>.</td><td>.</td></tr> <tr><td>Studies with varying housing, grouping, and nutritional management; output  risk analyses to determine best management practices; monitor overall effect on heifer longevity.</td> </td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <tr><td>Feed bunk management and prescribed feeding studies under varying housing scenarios; </td><td>.</td><td>X</td><td>X</td><td>X</td><td>.</td></tr> <td><B>Objective 1C </b></td><td>.</td><td>.</td><td>.</td><td>.</td><td>.</td></tr> <TR> <td>Output - establish standard procedures for data collection. Investigate calf morbidity variability in multi-locations; output  define more precisely biological factors that account for variability in calf morbidity and develop support computer simulation models for field application. </td> <td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <tr><td>Studies to evaluate effect of environmental stressors and transportation on immune function of calves and heifers; output  establish nutritional and health management strategies to overcome effects of stress on immune system suppression; develop decision support tools for field application.</td> </td><td>.</td><td>X</td><td>X</td><td>X</td><td>.</td></tr> </table>

(0): <tr> <td><B>Objective 2A </td><td><B>.</td><td><B>.</td><td><B>.</td><td><B>.</td><td><B>.</td></tr> <TR> <td>Characterize physiological and metabolic adaptations in transition cows; build computer simulation models; output - definition of optimal dry period length; risk analysis for variable dry periods; implications to culling decisions.</td> <td>X</td><td>X</td><td>X</td><td>.</td><td>.</td></tr> <tr><td>Studies on programmed exercise during dry period; feasibility of dry period exercise quantified; output - economic and management strategies formulated for communication to producers ands professionals.</td> </td><td>X</td><td>X</td><td>X</td><td>.</td><td>.</td></tr> <tr><td>Studies on nutritional programs; evaluate sub-acute ruminal acidosis (SARA) model for diet formulations; output  critical decision points for optimal nutritional programs defined and economic impact of alternative decisions.</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <tr><td>Animal housing, handling and behavior research; output - time budget spreadsheet development; publication on behavioral impact of overcrowding/comingling.</td><td>.</td><td>X</td><td>X</td><td>X</td><td>X</td></tr> <tr><td>Lameness scoring sheet and CD distributed; output - biosecurity investment model and risk assessment.</td><td>X</td><td>

Projected Participation

View Appendix E: Participation

Outreach Plan

Objectives 1 and 2: a) Study results presented at scientific meetings and published in scientific journals, extension fact sheets and popular press; b) Midwest NC-119 Symposia during the ADSA sectional meetings to facilitate information transfer; c) Establish dairy heifer and dairy producer network discussion groups; d) Conduct workshops within states and organize regional and national workshops; e) Distribute data spread sheets, software programs and decision support models based on outcomes for risk assessment and potential management actions; f) Conduct regional whole-herd analyses to develop spreadsheets and user-friendly computer programs for decision support and best management practices; g) Complete a dairy heifer and calf management bulletin and CD for distribution to clientele; h) Demonstrate decision models and support systems for use by dairy producers and their advisors to aid in making profitable and environmentally sustainable on-farm decisions; i) Distribute all pertinent information through the national Dairy InfoBase web site  ADDS (Agricultural Databases for Decision Support  www.adds.org).

Organization/Governance

Technical Committee. The Technical Committee shall consist of one officially designated representative from each participating Agricultural Experiment Station and(or) USDA group, regional administrative advisor (non-voting), and CSREES representative (non-voting). Participating stations and (or) groups are those written into the regional project or have an approved addendum.

Officers and Executive Committee. Officers shall be chairperson and secretary. A secretary shall be duly elected at the conclusion of the annual meeting of the Technical Committee and automatically succeed to the position of chairperson one year later. The Executive Committee shall consist of chairperson, secretary, and immediate past-chairperson. Executive committee, in conjunction with the Administrative Advisor, is authorized to function on behalf of the Technical Committee in all matters pertaining to the regional project requiring interim action. The chairperson, in consultation with the Administrative Advisor, shall arrange the time and place of meetings, prepare the agenda, preside at meetings of the Technical Committee, and is responsible for preparation of the annual progress report. The secretary records minutes, compiles station reports, and performs other duties as assigned by the Technical Committee or Administrative Advisor. Subcommittees will be appointed by the chairperson to complete specific assignments and to monitor progress within each of the main objectives. Subcommittees will meet at least once prior to the annual Technical Committee meeting.

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Attachments

Land Grant Participating States/Institutions

CA, FL, GA, IA, IN, LA, MD, MI, MN, NE, NH, NY, OH, PA, SD, TX, UT, VA, VT, WI

Non Land Grant Participating States/Institutions

USDA-ARS-Dairy Forage Research Center, Madison, WI