Trends in milk component production in dairy herds in Ontario:

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1 Trends in milk component production in dairy herds in Ontario: J. M. Sargeant 1, K. E. Leslie, M. M. Shoukri, S. W. Martin, and K. D. Lissemore Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada N1G 2W1. Received 31 October 1997, accepted 24 March Sargeant, J. M., Leslie, K. E., Shoukri, M. M., Martin, S. W. and Lissemore, K. D Trends in milk component production in dairy herds in Ontario: Can. J. Anim. Sci. 78: The objectives of this study were to investigate the dynamics of milk component production in dairy herds in Ontario, Canada, over time and to assess the impact of changes to the allocation of milk quota and payment systems on component production. The data consisted of monthly production records from all of the approximately farms that shipped milk in Ontario between March 1985 and July Farm mean yields of milk and milk components have increased over the past decade. The mean fat percentage among the farms increased from 3.85% in 1985 to 3.99% in 1994; protein percentage remained relatively constant at 3.30% in 1985 to 3.32% in The percentages of protein and fat showed a seasonal pattern. Relatively high component percentages were observed during the fall of each year, and low component percentages were observed during the summer months. Mean fat and protein percentages did not appear to change in response to changes in the allocation of quota or pricing systems. Farms with high (or low) relative protein percentage in one year tended to maintain high (or low) relative protein percentage in the subsequent year, suggesting that herd level factors are related to protein percentage. Key words: Milk components, time trends, dairy Sargeant, J. M., Leslie, K. E., Shoukri, M. M., Martin, S. W. et Lissemore, K. D Évolution de la composition du lait des troupeaux laitiers en Ontario de 1985 à Can. J. Anim. Sci. 78: L objet de notre analyse était de suivre l évolution dans le temps de la composition du lait dans les exploitations laitières de l Ontario (Canada) et d évaluer dans quelle mesure elle s est ressentie des changements affectant l attribution des contingents de production laitière et les régimes de tarification du lait. Les données utilisées provenaient des relevés de production mensuels de toutes les quelque exploitations livrant du lait dans la province entre mars 1985 et juillet Le rendement moyen à la ferme du lait et de ses composants a augmenté au cours de la décennie. Le taux butyreux moyen est passé de 3,85 % en 1985 à 3,99 % en Le pourcentage de protéine, pour sa part, restait relativement stable : 3,30 % en 1985 et 3,32 % en Ces deux taux suivent une courbe saisonnière, avec des valeurs relativement élevées en automne et basses durant l été. Les proportions en pourcentage moyennes de m.g. et de protéine ne semblent pas avoir été influencées par les changements touchant l attribution des contingents ou les régimes de tarification. Les exploitations faisant état de teneurs en protéines relativement élevées ou basses dans une année conservaient la même tendance l année suivante, ce qui porte à conclure que les facteurs de production du troupeau sont reliés à la teneur en protéine. Mots clés: Composants laitiers, tendance chronologique, exploitation laitière 1 To whom correspondence should be addressed. Food Animal Health and Management Center, Veterinary Clinical Science Building, Kansas State University, 1800 Denison Avenue, Manhattan, Kansas, USA, sargeant@vet.ksu.edu. 413 In the past two decades, consumer preferences for dairy products have changed considerably. In Canada, the percapita consumption of whole milk and butter has decreased, and the per-capita consumption of cheese has increased (Statistics Canada 1985, 1994). These trends are not restricted to Canada; in the United States, consumption of fluid milk and butter have also decreased, and cheese consumption has increased (Cropp and Jesse 1992). These changes are related to demographic changes in the population, and a reduction in the consumption of foods that are perceived to be high in dietary fat (Roepken 1988; Cropp and Jesse 1992). Concurrently, there have been changes in the demographics of the Canadian dairy industry. Over the past several decades, the number of dairy farms has decreased, but the productivity per cow has increased. In 1971, there were just over dairy farms that shipped milk or cream in Canada compared with farms in 1994 (Dairy Farmers of Ontario 1994). Milk production per cow per lactation has increased from a mean of 4137 kg from 1979 through 1981 to 5650 kg in 1994 (Dairy Farmers of Ontario 1993, 1994). The changes in production reflect improvements in management, including nutrition, and genetics (Radostits et al. 1994). Lee et al. (1985) estimated that the annual genetic change in lactation production for registered Holstein cows in the United States between 1971 and 1979 was approximately +55 kg for milk and +1.5 kg for fat. In Canada, dairy products are produced within a supplymanaged system. This system attempts to match farm production to consumer demand by setting production quotas. Abbreviations: AR[1] = autoregressive error term of order 1; MCP = multiple component pricing

2 414 CANADIAN JOURNAL OF ANIMAL SCIENCE Total requirements for dairy products are determined at the national level, and marketing boards in each province are responsible for allocating the available provincial quota allotment among dairy producers. The criteria for the allocation of quota and the method of establishing milk prices vary between provinces. The dairy industry in Ontario has responded to changes in consumer preferences for dairy products by changing the allocation and pricing of quota (Ontario Milk Marketing Board 1994). However, no formal studies have been conducted to quantify the response of dairy producers to these changes. This work is essential to evaluate the impact of policy changes and to predict future production trends. The goal of this study was to evaluate the dynamics of milk, fat, and protein production in the dairy industry of Ontario between 1985 and The specific objectives were to describe the seasonal and temporal trends in milk and milk component production, to investigate the impact of changes to quota policy between 1985 and 1994, and to describe the dynamics of milk component production within farms over time. MATERIALS AND METHODS Data Collection The data were from all of the approximately farms that shipped milk in Ontario between 1985 and 1994 and were obtained from the Dairy Farmers of Ontario (formerly the Ontario Milk Marketing Board). Because this industry is supply-managed, all of the milk produced in Ontario is sold to the Dairy Farmers of Ontario, which subsequently sells it to milk processors. Monthly data on all farms that produced milk continuously during the period or had entered or left production at some point during the period were included. The data included total litres of milk shipped per farm per month and the mean monthly fat and protein percentages. The component percentages were expressed as kilograms of fat or protein per litre of milk shipped. Total kilograms of protein and fat shipped per farm per month and the protein to fat ratio were calculated from the total milk production and the component percentages. Statistical Analysis Data from the farms were consolidated into a provincial mean for each of the 113 mo of production between March 1985 and July The mean monthly production of milk and milk components were described graphically to depict trends in production over time. TIME SERIES ANALYSIS. Time series regression analyses were used to model the variation between months for three dependent variables: the monthly provincial means for protein percentage and fat percentage, and the protein-to-fat ratio. The explanatory variables consisted of linear and quadratic time trends, measured by month (sequentially from 1 to 113), and seasonality, coded by dummy variables corresponding to calendar months. The explanatory variables were offered into a stepwise least squares linear regression model for each of the three outcomes. The dependent variables were mean monthly production levels. Consequently, the data represented repeated measurements of average production values from the same population of farms over time. Failure to account for the correlation between data points over time could lead to inappropriately small standard errors on parameter estimates and the identification of spurious associations (Shoukri and Edge 1996). Therefore, the regression models included an autocorrelation error term to account for correlation over time. After adjusting for autocorrelation, parameters or groups of dummy variables that were not significant at P 0.05 were removed from the model. Thus, the model for each production outcome was y i = µ + β 1 (time i ) + β 2 (time i ) 2 + β j (calendar month j ) + where µ = the intercept term, β = regression coefficients, i = 1 to 113 months of observation, j = 1 to 12 calendar months and = an autoregressive error term of order 1 (AR[1]) (Cryer 1986). The Ljung and Box statistic (Ljung and Box 1978), which tests the null hypothesis of no correlation over time, was used to assess the appropriateness of the AR[1] error term. The AUTOREG procedure of the SAS Institute, Inc. (1990a) was used for the time series analyses. IMPACT OF POLICY CHANGES. Two potentially important time points during the study period were identified. The first, August 1990, corresponded to the allocation of quota solely on the basis of kilograms of butterfat per year instead of litres of milk (for fluid quota) and kilograms of fat per year for market share quota (milk intended for industrial use). The second time point was January 1992 when multiple component pricing (MCP) was implemented in Ontario. For each of the policy changes, hierarchical dummy variables were coded such that time points previous to the policy change were coded as 0, and time points including and after the policy change were coded as 1. These variables were offered into the time series models in a stepwise fashion. DYNAMICS OF COMPONENT PRODUCTION. For each year from 1989 to 1993, farms were classified into quintile groups according to protein and fat percentages. The boundaries for the groups were defined independently for each year. The movement of farms among quintile groups over time was examined using state transition models (Carpenter 1988). A state transition model consists of two parts: the state (quintile group) and the transition. The transition represents the movement of a farm from one state to another. If the initial state is represented by S i (i = 1 to 5), there is a probability that the next state will equal S j (j = 1 to 5). The probability that a farm will move from state i to state j is represented by p ij = Pr[S j S i ], where i, j = 1, 2,..., 5. Together, the probabilities define a transition matrix; rows represent the state i, and columns represent the state j. Each probability has a value between 0 and 1, and the sum of the probabilities for each row is equal to 1. The probabilities were obtained using the FREQ procedure of the SAS Institute, Inc. (1990b). For each year, state transition matrices were calculated for the probability of the movement of a farm from state i to state j in the subsequent year for the quintile groups based on fat and protein percentages.

3 SARGEANT ET AL. TRENDS IN MILK COMPONENT PRODUCTION 415 Table 1. Annual farm means for litres of milk shipped per month, kilograms of protein and fat shipped per month, and mean protein and fat percentages for dairy herds in Ontario from 1985 to 1994 Milk shipped Protein shipped Fat shipped per Protein Fat Year per month (L.) per month (kg) month (kg) percentage percentage (+0.1) z 608 ( 0.2) 718 (+1.7) (+5.0) 638 (+4.9) 752 (+4.7) (+4.3) 679 (+6.4) 794 (+5.6) ( 0.4) 677 ( 0.3) 799 (+0.6) (+4.8) 709 (+4.7) 834 (+4.4) (+2.5) 728 (+2.7) 858 (+2.9) ( 2.3) 711 ( 2.4) 852 ( 0.7) (+0.8) 710 ( 0.1) 850 ( 0.2) y z Numbers in parentheses represent the percentage change from the previous year. y Data only available from January to July. The state transition probabilities were evaluated for randomness at one extreme and stability at the other. If the transition process was completely random, the probability of moving from one state to the next in the subsequent year would be equal for all transition probabilities; therefore, the 95% confidence interval on the estimate of the transition probabilities would include Conversely, if the transition process was completely stable, farms would be expected to remain in the same quintile group from year to year; thus, the 95% confidence interval for the transition probability for moving from state i to state j would include 1.0 when i equalled j and would include 0 when i did not equal j. The assumptions of randomness and stability between subsequent years were assessed by estimating the standard error of the transition probability according to the following equation: se(p ij ) = [(p ij *(1 p ij ))/n i ] 0.5 where n i = number of farms occupying state i in the initial period (Snedecor and Cochran 1989). Approximately 95% confidence intervals were calculated as the transition probability ±1.96 * se(p ij ). RESULTS Descriptive Statistics The study period extended from March 1985 to July 1994, inclusive. In March 1985, there were farms that shipped milk in Ontario. The number had declined to 8229 by July During the study period, 1788 new farms entered production, and 4738 farms ceased milk production. Table 1 shows the annual provincial means for litres of milk shipped per farm per month, kilograms of protein and fat shipped per farm per month, and protein and fat percentages. The percentage change between subsequent years is shown for litres of milk and kilograms of protein and fat shipped per farm per month. Percent change is not shown for 1993 through 1994 because data for 1994 were only available for January to July. The mean total litres of milk and the mean total kilograms of protein and fat shipped per farm per month are shown in Fig. 1. There was an increase in the provincial mean for total litres of milk and kilograms of milk components produced over time. The data showed a seasonal pattern; relatively more milk was shipped in May of each year, and relatively less milk was produced in November and December. A second decline in the total litres of milk shipped occurred in February of each year. However, this decline was entirely a function of the smaller number of days in the month of February. Yields of protein and fat showed the same seasonal patterns, although they were somewhat less pronounced for protein yield than for than for fat yield, especially after Figure 2 shows the mean fat and protein percentages and the mean protein-to-fat ratio over time. The data showed a seasonal pattern. However, the relationship between total milk yield and milk components, as a percentage, was reversed; high protein percentages corresponded to months with lower relative milk production. The months with high protein percentages corresponded to months with lower relative milk production. The months with high protein percentages also had the highest fat percentages. Initially, the lowest fat percentages occurred approximately 1 mo after the lowest relative protein percentages. By the end of the study period, low protein and fat percentages occurred during the same months. Peak protein-to-fat ratios occurred during months with relatively low component percentages. Beginning in 1992, there appeared to be a change in the pattern of the protein-to-fat ratio; the seasonal pattern appeared to be the same, but there was a reduction in the seasonal variation. Time Series and Impact of Policy Changes Time series analysis was performed to describe seasonality and time trends relative to component production and to evaluate the response to specific events in the allocation of quota. The AR[1] term, which adjusts for the correlation of each time point with the month immediately preceding, showed a good fit for the protein percentage model (Ljung- Box statistic P-value for no correlation of 0.76). Similarly, the AR[1] error term showed a good fit for the fat percentage model and the protein-to-fat ratio model. Thus, this error term was used for modelling each of the outcomes. The final time series models for protein percentage, fat percentage and the protein to fat ratio are shown in Tables 2

4 416 CANADIAN JOURNAL OF ANIMAL SCIENCE to 4, respectively. In the protein percentage model, there was a significant seasonal effect and a small but significant linear increase in protein percentage over time (linear trend); the quadratic term was not significant. Neither of the impact time points were significantly associated with protein percentage when the linear trend was included in the model. The R 2 of this model, including the AR[1] term, was 0.82, meaning that more than 80% of the variation in the provincial means for protein percentage was explained by the linear trend over time, season, and autocorrelation with the previous month. In the fat percentage model, there was a significant linear increase in the percentage of fat and a significant seasonal effect. Neither of the impact time points were significant. The R 2 of this model was Season was significantly associated with the protein-to-fat ratio. The Fig. 1. Mean herd milk production (l) and production of protein (n) and fat (Ò) per farm per month for all dairy farms in Ontario between March 1985 and July Fig. 2. Mean herd percentages of protein (n), percentages of fat (Ò), and protein to fat ratio (s) per farm per month for all dairy farms in Ontario between March 1985 and July introduction of MCP had a significant negative effect on the protein-to-fat ratio. Initially, the linear trend was statistically significant in the model for the protein-to-fat ratio. However, after inclusion of the variable corresponding to the introduction of MCP, the linear trend was no longer significant. Nonetheless, the linear trend parameter was left in the final model to clarify the interpretation of the impact point. The R 2 of this model was Plots of the observed versus the fitted values for all three models showed that the models fit the data well. Dynamics of Component Production State transition models for the probability of remaining in the same quintile group from one year to the next were calculated for four subsequent year comparisons: 1989 and

5 SARGEANT ET AL. TRENDS IN MILK COMPONENT PRODUCTION 417 Table 2. Least squares linear regression, with an autoregressive process of order 1, of the mean percentage of protein on time and month for dairy herds in Ontario from 1985 to 1994 β SE(β) P Intercept Linear trend, per month January February March April May June Referent July August September October November December , 1990 and 1991, 1991 and 1992, and 1992 and The state transition matrices for farm classification of protein and fat percentages between 1990 and 1991 are shown in Tables 5 and 6, respectively. These transition matrices were representative of the transition matrices between other years. Quintile group 1 represented the lowest one-fifth of the mean protein or fat percentages across farms, and quintile group 5 represented the highest one-fifth of the mean protein or fat percentages across farms. As shown by the 95% confidence intervals of the transition probabilities, relative protein percentage and fat percentage were neither completely random nor completely stable. However, based on the probabilities in the state transition matrices, the most likely state in the next year was the same as the state in the current year. Except for the lowest quintile of herds, there was no probability greater than 0.10 of a herd shifting more than one quintile classification from the current level in the subsequent year. The largest transition probability corresponded to farms in the highest quintile group. The transition probabilities within a row sum to 1, excluding rounding errors. Therefore, the probabilities within a row were not independent. Hence, the overall probability of one or more type I errors is not equal to 0.05% (95% confidence interval). Because the individual transition probabilities were shown with 95% confidence intervals to provide an indication of the variability of the probability estimates, rather than for specific hypothesis testing, no correction for non-independence was performed. Table 3. Least squares linear regression, with an autoregressive process of order 1, of the mean percentage of fat on time and month for dairy herds in Ontario from 1985 to 1994 β SE(β) P Intercept Linear trend, per month January February March April May June Referent July August September October November December Table 4. Least squares linear regression, with an autoregressive process of order 1, of the mean protein to fat ratio on time and month for dairy herds in Ontario from 1985 to 1994 β SE(β) P Intercept Linear trend, per month January February March April May June Referent July August September October November December MCP z z Multiple component pricing. DISCUSSION Time Series and Impact of Policy Changes Time series methodology can be used to describe changes in a measured outcome over time and to evaluate the impact of policy changes that potentially influence that outcome. Schukken et al. (1992) used a time series model to evaluate the response of the dairy industry to the introduction of a somatic cell count penalty program in Ontario. In Ontario, over the past decade, there have been several changes to the allocation of dairy quota in response to consumer preferences toward dietary fat. The first time point considered in this study, August 1990, represented the change to allocation of quota based entirely on kilograms of fat. Prior to this time, quota had been allocated on the basis of litres of milk produced for fluid milk purposes and kilograms of fat produced for milk intended for industrial purposes. In a supply-managed industry, such as the Canadian dairy industry, production quotas are allocated to meet domestic demands with little or no dairy products exported or imported. As the consumer demand for dietary fat declines, there is the potential for an increasing surplus of milk fat. For this reason, the allocation of quota in Ontario was changed to kilograms of fat to better meet domestic requirements. However, as the demand for milk fat decreases, the response of a supply-managed system would be to reduce the amount of quota available to producers. Under this scenario, the point could be reached where the production of dairy products based on milk fat production would result in the underproduction of milk protein. Although the

6 418 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 5. State transition matrix for the mean protein percentage on dairy herds in Ontario: Transition probabilities (P) and 95% confidence intervals for farms moving among quintile states from 1990 to 1991 Quintile Quintile P 95% CI P 95% CI P 95% CI P 95% CI P 95% CI Table 6. State transition matrix for the mean farm fat percentage on dairy herds in Ontario: Transition probabilities (P) and 95% confidence intervals for farms moving among quintile states from 1990 to 1991 Quintile Quintile P 95% CI P 95% CI P 95% CI P 95% CI P 95% CI demand for fluid milk products has declined over the past two decades, the demand for milk protein products has increased (Statistics Canada 1985, 1994). Thus, MCP was introduced in Ontario in January 1992 with an increased financial emphasis on milk protein yield. The goal was to encourage dairy producers to increase protein yield within an allocated level of fat yield. Multiple component pricing is not unique to Canada. The relative importance of milk protein production globally was illustrated in an international survey of farm payment schemes from 1992 to 1994, where 20 of 21 countries surveyed indicated that milk payment schemes included true protein (two countries) or crude protein (18 countries) as a parameter in their payment system (International Dairy Federation 1995). The component percentages increased over the course of the study period, as shown by the significant linear trend over time. This trend incorporates changes due to any or all of nutrition, genetics, management, and herd demographics. Specific details on these factors were not available for individual herds. The aim of this study was to describe changes, rather to test hypotheses regarding possible explanations for these changes. Using hierachical variable coding of the impact points allowed production in all months, including and after a policy change, to be compared with a baseline of all previous production when multiple impact point variables were including in the model (Walter et al. 1987). Given that the data had a seasonal pattern and component percentages appeared to increase over the study period, seasonality and the linear trend over time were controlled in all of the models. This allowed the identification of changes in production specifically associated with changes to the allocation of quota. Hence, the models did not test whether component percentages increased after change to policy, but rather whether the increases in the component percentages were significantly greater than the underlying changes already observed over time. Neither the change in the allocation of quota to kilograms of fat, nor the introduction of MCP resulted in significant changes in the provincial means for component percentages. The only statistically significant changes were a decline in the mean protein-to-fat ratio. This result appeared to be due to a synchronization in the seasonal production; the nadir of milk fat percentage shifted to slightly earlier in the summer to correspond with the months that had the lowest protein percentage. The decline in the mean provincial protein-to-fat ratio was unexpected given the increased importance of protein production relative to fat production. Further work is necessary to monitor this trend over time and to determine the reasons for it. The increase in farm mean fat percentage over time and the decrease in the protein to fat ratio associated with the introduction of MCP suggest that production trends over time are not responding to consumer requirements for decreased milk fat and increased milk protein. The purpose of this study was to describe production trends in the Ontario dairy industry as a whole, and to investigate whether overall production levels had changed in response to changes in quota allocation. Therefore, all farms in production during a given month were included in the provincial mean for that month. Restricting the analyses to only those farms which were in continuous production might not have adequately reflected the overall changes occurring at the industry level. Sargeant (1996) compared production levels between herds which left the Ontario dairy industry during each year between 1989 and 1994 to herds which remained in continual production during this time. There were no differences in protein percentage between herds leaving the industry and those remaining. The only differences in production between Ontario dairy herds leaving the industry and those remaining were a reduced mean fat percentage in herds leav-

7 SARGEANT ET AL. TRENDS IN MILK COMPONENT PRODUCTION 419 ing the industry during 1992 and reduced milk production (litres/month) for herds leaving the industry in The measured outcomes in this study were provincial means. Therefore, the results do not necessarily indicate that individual farms have not changed component production over time. The type of production response that would be apparent using time series modelling would be changes that could occur during a relatively short time, such as changes in nutritional management. Production effects caused by changes in breeding strategies in response to policy changes would be slower to realize. Because of the generation interval in cows, if producers responded to changes in the allocation of quota by changing their criterion for bull selection, the resulting offspring would not, on average, enter the milking herd for 3 yr. The study period extended 4 yr after the first impact point, and 2.5 yr after the second. Thus, there was insufficient time in this study for changes in breeding strategies associated with changes to quota allocation to be realized. Dynamics of Component Production The state transition analyses examined individual farm production relative to the production of other farms, rather than to production level per se. The relative percentages of protein and fat achieved by farms were not stable between successive years (Tables 5 and 6), suggesting that farms are not entirely capable of maintaining their relative component percentages over time. This result has repercussions when discussing the relationship between management and production because many farm management practices, such as housing and feed delivery systems, are relatively stable over time. The instability of relative production over time suggests that other more dynamic factors are related to protein and fat percentages. That the change in relative performance is entirely a function of genetics is unlikely, as genetic changes occur over a comparatively long time and would not likely be reflected in yearly fluctuations. The range of percentage values that define the performance categories were relatively narrow. Thus, it is possible that some of the fluctuation in relative farm production could have been due to measurement error. However, the same pattern of relative performance was observed in Dutch Holstein cows when relative performance was defined by three levels: high, medium, and low protein percentages (Scholl 1992). Although relative performance is not entirely stable, it is encouraging to note that it is not entirely random. The diagonal elements of the transition matrices had the largest probabilities, meaning that the most likely state for a farm in the subsequent year was the same state as the current year. Farms that changed in relative performance tended to change by only one level. Thus, farms with high relative protein percentages during one year tended to have high protein percentages during the next year, and farms with low relative protein percentages tended to remain low in the subsequent year. This result suggests that there are some characteristics of a herd that allow them to realize high or low relative production with some consistency over time. The greatest degree of stability in relative protein and fat percentage over time was achieved by the highest production group. This was not observed by Scholl (1992) in the Netherlands, where all of the herds studied milked Holstein cows. In Ontario, the majority of cows are Holstein. However, approximately 10% of cows enrolled in official milk recording in Ontario in 1994 were non-holstein, including Jersey, Ayrshire, Guernsey, Brown Swiss and Milking Shorthorn (Ontario Dairy Herd Improvement 1995). Breed differences in milk and milk component production are well documented; Ayrshires and the Channel Island breeds produce milk with higher component percentages (Blake et al. 1980). Therefore, non-holstein herds would tend to have higher component percentages. Although these herds would likely be subject to the same herd characteristics that influence component production, they would be expected to fluctuate around a higher mean value because of their inherent biological capacity for component production. Thus, the expected higher means for non-holstein herds may explain the difference in the stability of high performance farms between the two studies. Data on breed structure were not available for all of the herds in this study. However, based on differences in protein yields and percentages between individual cows of different breeds, changing the breed structure within a herd may allow some control of relative component production. CONCLUSIONS Mean production of milk and milk components among farms in Ontario has increased over the past decade. Seasonal trends were apparent. Low component percentages occurred during the summer months, and the highest percentages occurred during fall. There do not appear to have been any changes in component percentages specifically associated with changes in dairy policy, namely, the change in the allocation of quota to kilograms of fat and the introduction of MCP. Relative farm production of component percentages over time was not completely stable. However, relative farm production was not completely random, which indicates that there may be factors specific to the farm that allow or force a herd to maintain high or low relative production of milk components. ACKNOWLEDGEMENTS Financial support and the historical production data for this project were provided by the Dairy Farmers of Ontario. 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