Dairy Records and Models for Economic and Financial Planning1

SYMPOSIUM: MEETING THE INFORMATION NEEDS OF THE DAIRY INDUSTRY Dairy Records and Models for Economic and Financial Planning 1 M. A. DeLORENZO and C. V. THOMAS2 Dairy Science Department, University of Florida, Gainesville 32611·0920

ABSTRACT

INTRODUCTION

Managers are challenged to translate farm goals into specific strategies within organizational areas such as production and finance. To the extent that the goals include profitability, economic criteria should be used to determine the strategies. The economic criteria should be evaluated with data that are specific to the individual farm rather than assuming average relationships between production practices and profitability, which mayor may not be relevant in specific economic situations with constraints. Farm data from production and financial records can be used to analyze past performance, and then these data can be integrated with decision models to yield predictions about future performance that are critical for cash, credit, and financial planning. Information gathered regularly on current milk production, stage of lactation, age, reproductive status, health, feeding practices, performance, costs, and market prices, including forecasts, can be continuously included for management monitoring and control. Economic analysis includes opportunity costs and risk assessment. An example of a current operational system is given that analyzes past performance but also uses simulation and optimization models to forecast and analyze alternatives. Optimization, although never perfect, is considered to be a valuable part of the system to generate alternative strategies to reach farm goals and to make the system more than merely descriptive. ( Key words: dairy records, dairy management, economics)

Planning and controlling farm operations require information for describing past performance, monitoring ongoing performance, forecasting future performance, and choosing and taking appropriate actions in a continual process of adjustment while seeking to achieve business goals. Information needs depend on the extent to which managers pursue these activities. The information can be generated from the interplay of data, data processing, norms and standards, quantitative and qualitative models, rules, and optimization procedures. Many dairies have well-developed descriptive production information systems (e.g., DHI records). Most production record systems have evolved from cow testing schemes and thus remain individual cow record systems. With such systems, production goals can be easily defined and measured and are clear and concrete. These goals are often assumed to be an adequate proxy for more difficult economic goals. Descriptive economic and financial information systems similar to production record systems have not been as widely developed or adopted beyond the requirements of tax reporting. However, comprehensive accounting systems are important because they are the source of inventory, cost, and price information essential for economic evaluation required for effective planning systems. Economic analysis demands the simultaneous consideration of input costs, output prices, input and output quantities, and the functional relationships among inputs and outputs. Thus, production records or financial records alone are inherently unable to provide sufficient information to identify economically better alternatives for dairy management. Interactions among production factors, such as milk production, nutrition, reproduction, health, and culling, and their impact in the context of prices and costs need to be included. Economic models must contain causal physical relationships among inputs and outputs because algorithms that search for better economic solutions must be based on the physical biological system. Without causal models, we can only predict using empirical models based on historical data if circum-

Abbreviation key: DP :: dynamic programming, FDMP :: Florida dairy management project, MIS = management information system.

Received October 27, 1994. Accepted August 17, 1995. IFlorida Agricultural Experiment Station Journal Series Number R-04032 2Present address: MSU Extension, 37 Austin Street, Sandusky, MI 48471. 1996 J Dairy Sci 79:337-345

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application of the appropriate economic decisionmaking rule to maximize profit. Because economics is about making the best choices, identification of all possible alternatives is important. The technical relationships among inputs and outputs determine the production possibilities. These relationships include predicting milk production from different diets; knowing the quantitative responses to biological stimulae, such as hormones, antibiotics, or feed additives; knowing the effect of additional labor on productivity; and predicting crop yield changes as increasing amounts of fertilizer are used. Knowledge of these relationships permits marginal analysis rather than analysis on average costs and returns. Yet, representation of business decision problems is difficult because of many interacting factors, jointly determined products, and regulatory constraints, Glen (5) has pointed out that dynamic interactions exist among several factors such as feeding, replacement, and breeding in livestock enterprises and that other operations, such as crop production, may interact with these factors. Glen (5) argues that decision models should incorporate these interactions. Typically, activities, such as feeding strategies, culling strategies, and genetic selection, are analyzed independently. Problems in these areas are more easily ECONOMICS represented by the economic models of single production function than production economists prefer ( 17), Economic Evaluation but these models can ignore interrelationships among Profitability may not be the only goal of all dairy different activities of the dairy business and usually businesses, but it is assumed to be the primary goal of do not consider opportunity costs. most. Finance deals with the flow of funds and measOptimization using detailed models with a high ures financial performance, but economics is broader degree of biological realism is difficult, and whole and includes concepts of valuing resources as they farm optimization may be too complex at this time. contribute to profitability. These concepts include op- Henryet al. ( 7) used whole system optimization with portunity costs, marginal analysis, equimarginal linear programming to evaluate nutrient recycling returns, input substitution, and the time value of alternatives to optimize nutrient recycling strategies money (discounting), components that are not typi- considering cropping systems, purchased commodically considered to be a part of financial accounting. ties, dairy cattle rations, and regulatory constraints. These concepts are, however, important in economic However, the detailed underlying information that is analysis when determining what actions might potenrequired for realism was limited about nutrient tially increase profit. losses, movement of nutrients in the soil, and uptake Whenever profitability can be increased, it is ecoof nutrients by crops. Other approaches (15) have nomically rational to do so, other things being equal. used programming techniques, such as constraint Thus, economic planning and management require satisfaction for large complex problems, especially knowing not only what is profitable, but also what is when meeting constraints rather than optimization most profitable. Optimization is fundamental to clasappeared to be the most practical goal for managesical economics, which is largely the science of choice. ment. Current optimization techniques may be most Business goals and availability of limited resources applicable for well-structured, repetitive decisions, force choice. such as diet formulation, replacement, insemination, The paradigm for the application of classic principles of production economics follows three steps: 1) and sire selection for which data exist to support specification of the quantitative (technical) relation- detailed analysis. Experience, knowledge, and constraints may be ships among inputs and outputs, 2) evaluation of inputs and determination of output worth, and 3) relatively important for large, complex, strategic deci-

stances are substantially identical to the past. Improvements in management, production performance, or other factors render historical data less meaningful. In addition, Hogeveen et al. (8) showed that information systems incorporating causal models can arrive at solutions when rule-based systems break down, leaving unresolved problems. Bywater (1) argued that the development of an integrated management information system (MIS) should contain predictive models linked to a continuously updated record system (database). These models should be presented simply enough to permit their adoption, but still preserve enough of the complexity, dynamics, and holistic aspects of the production system to be useful. Such models should be disaggregate enough to model interacting factors, or causal components, so that they are useful for prediction. An integrated information system should be a combination of data gathering, data summarization, rules, simulation, and optimization to produce information that is descriptive, diagnostic, predictive, and prescriptive. A key feature is the integration of the production, economic, and financial models. Some systems (1, 8) that have appeared in concept suggest some integration.

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sions but can be difficult to build into optimization models. Simon (14) has argued that the use of optimization forces simplifications of real world problems into computable representations of reality and assumes unrealistic omniscience with respect to model structures, site-specific parameter values, future product prices, input costs, and other model variables. Yet, even with these problems, to abandon all optimization as futile and noninstructive ignores the fact that human managers make mistakes, often have limited vision, and are limited in cognitive and calculation abilities. Even though explicit production functions for classical optimization may not often be realistic, economic concepts can still be applied in a useful way. For example, van Arendonk (19) pointed out that two popular measures of dairy cow profit, total lifetime profit and profit per day of herd life, are incorrectly applied for culling decisions, relying on untenable assumptions. These measures are simply accounting identities, assuming either that no replacements enter the herd or that each cow is replaced by a heifer having identical characteristics as the culled cow. The application of correct theory (in this case, replacement theory) and dynamic programming (DP) for optimization eliminates confusion promulgated by the ad hoc definitions that superficially appear to make, or to reflect, common sense. Similarly, approaches based on annuities calculated over an arbitrary planning horizon and ignoring likely future voluntary culling are equally unreasonable economically. Correct approaches indicate that solutions need to be specific to the context. Houben (9) showed the correct replacement strategy under a quota system depends on the presence or absence of opportunity costs for labor, housing, machinery, and other items unrelated to production. That study also showed the impact of clinical mastitis on optimal culling policies, which differed among herds. McCullough ( 12) showed that insemination and replacement policies should vary depending upon management, prices, and costs. Simulation models can also be used to incorporate interactions among components of complex systems for practical planning if the models contain enough detail about the real system (5). Simulation is useful to predict future milk production, animal sales, and required inputs. Thus, simulations produce a detailed budget for the dairy. Outputs of these models can be part of the integrated planning system because they can be used to create cash flow budgets, inventories, income statements, replacement forecasts, and future feed needs. One example, described by Harsh et a1.

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( 6 ), used a group of linked models ranging from crop growth models that were dependent on weather patterns to a macroeconometric model of US agriculture J alvingh (10) reviewed some other dairy simulation models. Risk

The stochastic nature of milk and crop production, biology, disease, weather, changing input costs, milk prices, and other uncertainties create risk. Large capital investments needed for dairy production are also subject to institutional risk from changing price support programs; regulatory activities, such as environmental policy; export policy and trade relations; or even supply management policy. Empirical evidence suggests that decision makers frame their decisions on risk perceptions (20). Investments in projects may require returns three to four times the cost of capital, or businesses may operate for some time while absorbing an operating loss because 1) actions usually create fixed costs that may be impossible to recoup; 2) the economic environment is uncertain; and 3) the investment opportunity rarely disappears, making the manager's decision not only whether to take action, but when to act, because delay may cost a premium (3). Monte Carlo simulation is often used to describe variability of expected outcomes. Decision rules based on risk efficiency criteria can then be applied to evaluate the desirability of alternative risky prospects ( 13). Risk efficiency criteria separate all risky prospects under consideration into a risk efficient set preferred by the decision maker and a risk inefficient set containing those prospects deemed to be undesirable (11). For example, Thomas (16) used stochastic dominance, a risk efficiency criterion that evaluates the cumulative probability of return distributions for investment alternatives to identifY preferred investments in various milking parlor designs, sizes, and management strategies. In this study, a network simulation model of milking parlors to predict returns from investments in different milking parlor sizes and designs contained stochastic elements. Thus, the desirability of competing parlor alternatives were not compared solely by their expected mean performance or on a single-valued measure of their economic worth, but rather on the distribution of economic returns that each could be expected to produce. A USABLE MIS

The Florida Dairy Management Project (FDMP) uses an MIS that embodies the aforementioned conJournal of Dairy Science Vol. 79, NO.2, 1996

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cepts. The MIS has been used on a routine basis by a small number of commercial dairy producers and is discussed because the authors are most familiar with it. The purpose is to illustrate the use of combining production and financial data to generate economic information in a useful format for dairy managers, not to promote this prototype over other systems. The primary focus is support of tactical and operational decision-making, rather than long-term strategic decision-making. The FDMP MIS integrates dairy-specific data on production and economics through the use of causal models, simulations, decision rules, and optimizing programs to provide dairy producers with monthly information that is descriptive, diagnostic, predictive, and prescriptive. The system consists of data, models, and reports that are shown schematically in Figure l. Interaction between a FDMP consultant and the dairy manager is also a main component.

also are provided directly by the dairy producer. Data are summarized and maintained in a database. Typical data categories are shown in the Appendix. Milk price and input cost predictions can be made using price histories and knowledge of current industry trends and the forces operating on pricing mechanisms. Financial records also document variations in economic situations and are the basis for identifying seasonally repeatable patterns in milk price, feed costs, and other product prices and input costs. These patterns create economic opportunities to increase profits. Models

Data

Dairy-specific data come from farm production testing and financial records. Additional data on the actual milk sold per month, cattle inventories, feedstuff nutrient analysis, ration composition, and feed usage

A DP model, an economic model, and a simulation model are central to the FDMP MIS. These models 1) predict production possibilities based on characteristics of individual cows and herd production that are specific to each dairy, 2) integrate production and economic data specific to each dairy, 3) base optimization on economic criteria, and 4) generate predictive and prescriptive information to support management decision-making. All information is specific to the production and economic conditions in existence or predicted for the dairy under consideration.

Modeling Production Data

-

Milk Production Feed Use Reproductive Performance Replacement

i+Economic Data

-

Producllon Expense. (e.g., feed, replacement and other costs) Production Revenues (e.g., milk and cull cow income)

SimUlation Herd Dynamics Feed Intake Milk Producllon Milking Operations Feed, Crop, and Waste Nutrient Balance Disease/Health OptimIzation DP Insemlnallon Replacements LP Nutrients Decision Rules

Reports Forecasted Normallve

r----

Physical Resuns Financial Resuns

Reports Databases

l+

Production Feed FInancial

l+-

Historical Summarization Production Finance

Figure 1. Components of an operational system to generate information about different types of production, financing, and economics. The diagram indicates that both dairy production data and financial data are maintained together. These data are not used only for summarization, but also for estimation of parameters and variables in models to forecast future performance and to suggest actions to increase profitability. DP = Dynamic programming; LP = Linear programming. Journal of Dairy Science Vol. 79, No.2, 1996

SYMPOSIUM: MEETING THE INFORMATION NEEDS OF THE DAIRY INDUSTRY

The DP model (2) determines insemination and culling policies that maximize profit from each cow position in the herd. The model finds policies based on maximized net present values for possible cow states. In the current version of the model, a state is defined by parity (1 to 12), month of insemination (3 to 11), month of lactation (1 to 19), within-herd production (1 to 5), and month of calving (1 to 12). The model makes one of three decisions for every cow: 1) keep, 2) replace, and 3) if a cow is kept and is open, whether to breed at next estrus. The decisions, in the form of policies, are reported as breeding and culling guides. The decision to keep or to replace considers the opportunity cost of postponed replacement, because the cow is kept only if her net present value is higher than the mean first parity cow competing for the position in the herd. The production and economic characteristics of the first parity cows are based on expected performance in the month of replacement in the herd. The economic model defines the economic characteristics of the cow states. Only milk production and feed costs are discussed. Milk production for each state is determined by 1 of 24 lactation curves fitted to production records that are specific to the dairy. Lactation curves are fitted by using fifth-order polynomials by month of calving for first parity cows and for second and greater parity cows (12). Curves are recalculated approximately four times annually to account for changes in production conditions, management, and environment. Future expected changes are modeled as well. Feed costs are calculated for each cow state using algorithms developed by Fuentes-Pila (4). In those algorithms, the feed cost for a particular cow state is driven by feed cost per unit of DM and DMI. Dry matter intake is a function of the DM demand of milk production, ration fiber content (NDF), BW at calving, milk production, parity, and stage of lactation. The stochastic simulation model is integrated with the DP and uses the same definitions of cow states. When a specific herd is simulated, the cows in the current dairy herd are assigned to appropriate states according to the values of their state variables. Thus, based on the herd structure for the current month as the starting point (and monthly conception rates, estrus detection rates, and replacement heifers scheduled to calve), the simulation moves the herd through time and calculates its aggregate production characteristics on a monthly basis, typically for the next 12 calendar mo. The simulation model predicts the monthly milk production, feed costs, herd structure (total herd size, number of dry and milking

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cows), number of culls, number of calvings, number of pregnancies, or other descriptive statistics the dairy producer requests. An important feature of the FDMP MIS IS the flexibility of the DP and simulation models in allowing a wide variety of possible production and economic scenarios to be examined in addition to the two that are routinely provided. Dairy producers often request special scenarios examining a multitude of possible management strategies (e.g., bST use and AI versus natural service breeding), alterations to farM structure (e.g., expansion, heat stress abatement structures, and equipment), and changes to herd structure (e.g., timing effects of additional heifer purchases). The breeding and culling model is particularly useful in Florida because of the complex seasonal patterns of milk price, milk production, and reproductive performance. The DP is linked to programs that compute herd characteristics if the optimal insemination and replacement policies are followed. An advantage of this DP is that the economic value of the scenarios can be compared with optimal insemination and replacement policies that have been determined for each scenario separately. This comparison is important if there is interaction between decisions among and within scenarios. Management Reports

Routinely, cash flow and income statements for the next 12 calendar rna, including up to 25 expense categories are computed based on historical data, the simulation, and the DP. This final link in the process allows dairy managers to ascertain the economic consequences of following status quo breeding and culling policies, optimized breeding and culling policies, or any proposed management change capable of being captured by the DP and simulation models. Expected results are compared with the past results. Because optimization models may be rigid and incorporating experience, knowledge, and constraints may be difficult, comparison of optimal simulations with any other strategy under consideration is both possible and valuable. This process is illustrated in Figure 2. Subsidiary production and economic summaries and graphs are also prepared. These summaries and graphs may contain all types of information (descriptive, predictive, and prescriptive) or may be limited to descriptive and diagnostic information for monitoring purposes. Such items as milk production, income per month, purchased replacements, labor costs per hundred weight of milk, DMI, and feed cost per hundred weight of milk are summarized monthly. Journal of Dairy Science Vol. 79, No.2, 1996

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When a specific scenario is requested by a dairy manager, a full set of reports is provided. Reports received by participating dairy managers each month include a set of breeding and culling guides. Guides of this type, based on optimal policies as determined by DP, were first suggested by van Arendonk (18), although not on a within-herd basis. The Appendix shows a portion of an example of a breeding guide and a portion of an example of a culling guide. These guides suggest, for the current month, which cows in the herd should receive the highest priority for breeding and which cows should be culled for maximal profit. In the breeding guide, each open cow in the herd has a value indicating the expected net discounted return from insemination at the next estrus versus waiting. The relative breeding values can be viewed as a priority list for breeding. Thus, cow 3019 should receive the highest estrus detection intensity. Values increase rapidly toward the end of the breeding period because the culling risk increases for open cows. The culling guide shows the net present value of keeping a cow versus replacement with an average first calf heifer calving in the month of replacement. For example, it is recommended that cow 6 be culled, other things being equal, even though the expected net cash return over the next 12 mo is positive, because a first calf heifer taking that position would

provide a higher net present return ($117). These examples are specific reports that recommend specific herd management actions to increase farm profitability. Further, because cows are ranked by expected net present value, if immediate replacement is not feasible or the herd is expanding, cows are ranked correctly by economic criteria. Human Factors

Interactions between the dairy manager and the FDMP consultant determine the ultimate use of the MIS described. Experience indicates that optimization by DP, with an objective function that includes the opportunity cost of postponed replacement, is not quickly appreciated by many dairy managers. To avoid risk, dairy managers typically try to minimize replacement and inseminate cows at every opportunity. Culling is perceived as a loss of capital, but consideration of the opportunity cost of postponed replacement can lead to more aggressive replacement policies, especially when involuntary loss is reduced. Initially, dairy managers are suspicious of complicated models that contain details they do not understand, and they tend to prefer heuristic rules that are perceived to have underpinned past business success and thus seem to be valid. Dialogue between the consultant and manager can resolve this issue, and, if

Steady-State Computation

I Stochastic Variables

...

State Values

Cow States Current Alternative

~

Dynamic Optimization

•

CUlling Guides

,

Breeding Guides

,

Optimized Herd structure, replacement, production, revenues and costs.

I Forecasting Simulation

, ..

Compare

I

Forecasted Herd structure, replacement, production, revenues and cost••

Figure 2. Comparison of future alternatives generated from an optimization program versus simulation. The dynamic programming model generates suggested culling and insemination policies and steady-state computations determining herd characteristics aggregated from individual cow decisions. Forecasting simulation allows determination of effects of constraints and management changes not in the dynamic programming model, testing scenarios, or policies of interest to the dairy producer. Journal of Dairy Science Vol. 79, No.2, 1996

SYMPOSIUM: MEETING THE INFORMATION NEEDS OF THE DAIRY INDUSTRY

necessary, the proposed strategy may be modified to account for considerations outside the model. Managers differ in goals, abilities, resources, confidence in data and models, and acceptance of FDMP MIS recommendations when they differ from held beliefs. Monitoring and diagnosis of descriptive production and economic data help convince the dairy manager that the specific circumstances are understood and modeled correctly. When this realization occurs, the information is more difficult to ignore than if it describes only a representative farm. Some managers do not change their behaviors for many reasons, including lack of consensus about the technical details of the models and specific production constraints that are not contained in the model. However, two important results almost always are achieved: 1) the dairy manager becomes engaged in a process that ultimately leads to improved analysis, and 2) the consultant gains valuable insight into the dynamics of decision making that are present on the specific dairy, which increases the likelihood that future advice can be formulated in a way that results in adoption. CONCLUSIONS

Definition of management problems in economic terms is important. Much has been written about the cost of isolated individual production performance measures, but interactions among various factors must be considered. Classic production economics provides a well-structured approach for economic optimization, but makes heroic assumptions about the amount and quality of available information as well as the cognitive abilities of managers. Information and knowledge are always incomplete, and risk is a major consideration for decision makers. The use of causal models and optimization can generate future alternatives, and, although not perfect, predictions can help steer decisions and assess risk. Models that incorporate interactions among the various components of the production system are important. Records, models, and rules that codify experience are ultimately part of a knowledge-based system that ideally provides decision makers with a rich description of the manager's domain. The more complete and specific the description is, the better the decisions can be. The decision rules themselves can be relatively simple. Dairy records and models should provide managers with such descriptions. Within the realm of economic and financial planning, a comprehensive and flexible accounting system is absolutely fundamental.

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Once the dairy MIS is based on causal models and the production and economic entities of the business are integrated, the MIS can produce not only descriptive and diagnostic information for monitoring production and financial progress, but also, and even more importantly, 1) generate accurate predictive information needed to determine whether, when, and what management intervention is needed to meet business goals, 2) examine the consequences of management decisions when production and economic circumstances differ substantially from those in the past, and 3) provide prescriptive information to suggest the best course of management intervention necessary to increase economic performance. Increasingly, data will be maintained on the farm for daily, rather than monthly, management deciSIOn making. The information system needs to be flexiblp to accommodate growth, to enhance access to data to generate information on demand, and to have an interface with decision models. Real time data collection is useful for monitoring items such as milk production, feed intake, and physiological states to generate timely exception reports as models detect important deviations. This formalizes what superior managers have done for years on very small dairies where virtually all attributes on every cow could be noted and retained in the manager's memory. ACKNOWLEDGMENTS

Comments by reviewers were appreciated, especially those regarding Florida Dairy Management Program. REFERENCES 1 Bywater. A. C. 1981. Development of integrated management information system for dairy producers. J. Dairy Sci. 64:2113. 2 DeLorenzo. M. A., T. H. Spreen, G. R. Bryan, D. K. Beede. and J.A.M. van Arendonk. 1992. Optimizing model: insemination, replacement, seasonal production. and cash flow. J Dairy Sci. 75:885. 3 Dixit, A. 1992. Investment and hysteresis. J. Econ. Perspectives 6:107. 4 Fuentes-Pila, J. 1994. A model to predict feed intake of lactating dairy cows. M.S. Thesis, Univ. Florida, Gainesville 5 Glen, J. J. 1987. Mathematical models in farm planning: a survey. Operations Res. 35:641. 6 Harsh, S. B., J. W. Lloyd, and A. S. Go. 1995. Model for financial evaluation of alternative production strategies for Michigan dairy farms. Page 162 in Proc. Farm Anim. Comput. Techno!. Conf., Orlando, FL. 7 Henry, M. G., M. A. DeLorenzo, D. K. Beede, H. H. Van Horn. C. B. Moss, and W. G. Boggess. 1995. Determining optimal nutrient management strategies for dairy farms. J. Dairy Sci 78:693. 8 Hogeveen, H., E. ~. Noordhuizen-Stassen, J. F. Schreinemakers, and A. Brand. 1991. Development of an integrated knowledge-based system for management support on dairy farms. J. Dairy Sci 74:4377. Journal of Dairy Science Vol. 79, No.2. 1996

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DelORENZO AND THOMAS 15 Stone, N. D. 1995. Whole-farm planning for croplIivestock farms: integrating nutrient management. Page 255 in Proc. Farm Anim. Comput. Technol. Conf., Orlando, FL. 16 Thomas, C. V. 1994. Operations and economic models for large milking parlors, Ph.D. Diss., Univ. Florida, Gainesville 17 Trapp, J. N., and O. L. Walker. 1985. Biological simulation and its role in economic analysis. Page 13 In Simulation of Beef Cattle Production Systems and Its Use in Economic Analysis. T. H. Spreen and D. H. Laughlin, ed. Westview Press, Boulder, CO. 18 van Arendonk, J.A.M. 1988. Management guides for insemination and replacement decisions. J. Dairy ScI, 71:1050. 19 van Arendonk, J.A.M. 1991. Use of profit equations to determine relative economic value of dairy cattle herd life and production from field data. J. Dairy Sci. 74:1101. 20 Wilson, P. N., R. D. Dahlgran, and N. C. Conklin. 1993. Perceptions as reality on large-scale dairy farms. Rev. Agric. Econ 15: 89.

9 Houben, E.H.P. 1995. Economic optimization of decisions with respect to dairy cow health management. Ph.D. Diss., Wageningen Agric. Univ., The Netherlands. 10 Jalvingh, A. W. 1992. The possible role of existing models in onfarm decision support in dairy cattle and swine production. Livest. Prod. Sci. 31:351. 11 King, R. P., and L. J. Robison. 1984. Risk efficiency models. Ch. 6 in Risk Management in Agriculture. P. J. Barry, ed. Iowa State Univ. Press, Ames. 12 McCullough, D. A. 1992. Effect of model specifications and exogenous variables on a stochastic dynamic insemination and replacement model for dairy cattle. M.S. Thesis, Univ. Florida, Gainesville. 13 Selley, R. 1984. Decision rules in risk analysis. Ch. 5 in Risk Management in Agriculture. P. J, Barry, ed. Iowa State Univ. Press, Ames. 14 Simon, H. A. 1979. Rational decision making in business organizations. Am. Econ. Rev. 69:493.

APPENDIX Figure AI. Typical production and economic data used by the Florida Dairy Management Project, Production

Economic

Cow milk production history Current cow milk production Current cow reproductive status Herd milk production Milk fat percentage Milk protein percentage Monthly conception rates Estrus detection rates Ration NDF concentration Cow BW by parity Calf BW by sex and parity at calving

Actual milk price by month Forecasted milk price by month Milk fat differential Milk protein differential Feed cost per pound of OM (high producers) Feed cost per pound of DM (low producers) Beef salvage price Replacement cost Calf values Discount rate Short-term interest rate for purchased replacements Feedstuff prices Actual monthly revenues and expenses

Calf mortality by parity Involuntary culling rate Genetic progress per year Cattle inventory Actual pounds of milk sold per month Projected heifers to freshen by month for next 12 mo Feed information Feedstuff nutrient content Ration composition Group daily intake Cow numbers per group Minimum milk per day (high producers)

TABLE Al. Excerpt from sample breeding guide. Barn name

Lactation no.

Month milk

Cow ME1/ herd ME

305 ME

LTD2 Milk

3019 908 106 911

2 1 2 1

6.7 3.1 7.0 3.0

1.26 1.01 0.72 1.11

25,442 20,505 14,497 22,477

79 71 48 81

Reproduction Value of insemination code

($)

lMature equivalent 2Last test day. 3Cull. Journal of Dairy Science Vol. 79, No.2, 1996

C3

319 36 0 0

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TABLE A2. Excerpt from a sample culling guide. Barn name

Lactation

Month milk

Cow MEI/ herd ME

305 ME

9579 909 6 9396

(no. ) 4 1 2 1

2.2 6.3 5.9 10.9

1.19 1.14 0.83 0.93

24,103 23,172 16,749 18,761

LTD2 Milk

Keep vs. cull

118 70 55 38

1564 532

Net cash 12 mo ($ )

-117 -305

2227 769 371 -29

IMature equivalent. 2Last test day.

Journal of Dairy Science Vol. 79, No.2, 1996