An analysis of the implications of a change to the seasonal milk supply profile in the Irish dairy industry utilizing a seasonal processing sector model

U. GEARY; N. LOPEZ-VILLALOBOS; D. J. GARRICK; L. SHALLOO

doi:10.1017/S0021859612000020

An analysis of the implications of a change to the seasonal milk supply profile in the Irish dairy industry utilizing a seasonal processing sector model

Published online by Cambridge University Press: 28 February 2012

D. J. GARRICK and

U. GEARY: Affiliation:
Livestock Systems Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
N. LOPEZ-VILLALOBOS: Affiliation:
Institute of Veterinary, Animal and Biomedical Science, Massey University, Palmerston North, New Zealand
D. J. GARRICK: Affiliation:
Institute of Veterinary, Animal and Biomedical Science, Massey University, Palmerston North, New Zealand
L. SHALLOO*: Affiliation:
Livestock Systems Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
*: *To whom all correspondence should be addressed. Email: laurence.shalloo@teagasc.ie

Article contents

Summary
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
CONCLUSION
References

Rights & Permissions

Summary

A seasonal processing sector model was developed to simulate dairy product manufacture in Ireland. Outputs include the quantity of product manufactured, net returns and component values of milk (protein and fat) per month of year. Two milk supply profiles representative of mean calving dates of mid-February and mid-March were evaluated across three milk processing plants with differing capacities for cheese and casein. The analysis was carried out based on average Dutch National quotations over the period 2008–10. The mid-February mean calving date resulted in a lower peak supply with proportionately more milk produced in the months of January–March and October–December, which resulted in higher net returns across the three capacities analysed. Increasing the cheese and casein processing capacity resulted in higher net returns being generated.

Information

Type: Animal Research Paper
Information: The Journal of Agricultural Science , Volume 150 , Issue 3 , June 2012, pp. 389 - 407
DOI: https://doi.org/10.1017/S0021859612000020 [Opens in a new window]
Copyright: Copyright © Cambridge University Press 2012

INTRODUCTION

Economic growth has markedly slowed down during 2009/10, with many countries experiencing negative growth. In contrast to this, the dairy industry has seen a recovery in milk price, stabilization of production costs and there is modest growth in global milk supply (Breen et al. Reference Breen, Cushion, Donnellan, Hanrahan, Hennessy, Kinsella, Martin, Moran, Quinlan, Ryan, Thorne and Moloney2011). EU milk supply will be further bolstered by the removal of the milk quota by 2015 (European Commission 2008), whereby for the first time since 1984 milk production will no longer be constrained. It is argued that trade protection, in the form of EU supports, contributed to distorting dairy markets and has prevented sustainable growth in global dairy (A. Ferrier, unpublished results). The Common Agricultural Policy reform which is striving to remove dairy support and output restrictions will allow expansion within the dairy industry.

Although policy reforms present opportunities to producers and processors alike the threat posed through milk price volatility must be acknowledged, which is inherent in a market which is not constrained by supply. Demand for dairy produce is relatively inelastic since it is a basic food commodity in developed countries (Graugaard Reference Graugaard2010). Milk supply is also inelastic (Pieters Reference Pieters2010) with only c. 0·06 of world milk production being traded internationally (Graugaard Reference Graugaard2010); therefore, a small change in supply has a large impact on price. It is forecast that 0·80 of the growth in world dairy production will come from developing countries like India and China (Graugaard Reference Graugaard2010), and therefore the impact these countries will have on the milk price will grow (J. Pieters, unpublished results), both through increased supply and demand.

The competitiveness of the Irish dairy industry lies in its low cost, grass-based production system (Prospectus 2003). The system relies heavily on grass, which has a seasonal growth profile; as a result the Irish milk supply profile is seasonal, with a peak-to-trough ratio of 5·9:1 (Central Statistics Office Ireland 2010). Processors have invested in capacity to accommodate this seasonal supply profile; however, this capacity is only fully utilized for c. 3 months of the year, which leads to higher processing costs. It is anticipated that by 2020, milk production in Ireland since 2010 will have increased by 50%, in a post-quota environment (DAFF 2010). However, if the 50% increase is applied to the current seasonal supply profile, more investment will be required at the processor level to process this higher peak. While there is a significant advantage associated with the seasonal nature of milk supply at the farm level (Shalloo et al. Reference Shalloo, O'Donnell and Horan2007), there is an associated cost in the processing sector brought about by higher processing costs. In order to complete an economic appraisal of the effect of differing levels of seasonality (in terms of peak-to-trough ratio and the proportion of total milk supplied February–April) within the dairy industry, an evaluation of the effect within the processing sector must be completed.

Processing sector models have been utilized by the dairy industry worldwide to help with the decision making process: Benseman (Reference Benseman1986) in New Zealand, Pratt et al. (Reference Pratt, Bishop, Erba, Novakovic and Stephenson1997), Papadatos et al. (Reference Papadatos, Berger, Pratt and Barbano2002) and Nicholson & Fiddaman (Reference Nicholson, Fiddaman, Eberlein, Diker, Langer and Rowe2003) in the U.S. and Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010) in Ireland.

The objective of the current study was to determine the impact of a change to the mean calving date within the current spring system operated in Ireland on processor returns, milk price and the component values of milk paid to farmers per month. Two spring calving milk supply profiles based on standard lactation curves (Olori & Galesloot Reference Olori and Galesloot1999) were examined, one which was representative of the current national spring calving pattern with a mean calving date of mid-March and an alternative supply profile representative of the optimum at farm level with a mean calving date of mid-February (Shalloo et al. Reference Shalloo, O'Donnell and Horan2007). A number of milk processing capacity constraints were simulated across the two calving patterns.

MATERIALS AND METHODS

Model description

An annual time step milk processing sector model has been developed for the Irish dairy industry (Geary et al. Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010). The model presented in the current paper expands that model to function on a monthly time step, with 12 independent sets of inputs and outputs. As outlined in Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010), the approach uses a mass balance milk processing-sector model that accounts for all inputs, outputs and losses involved in dairy processing. The model is a mathematical representation of the conversion of milk into dairy products. Figure 1 provides a schematic of the model. Within the model, the production of cheese, casein, butter, whole milk powder (WMP), skim milk powder (SMP) and fluid milk is simulated with the by-products of butter milk powder (BMP), whey and cream all being further processed or sold. The volume and composition of raw milk intake, product portfolio and its composition are included as model inputs which are used in the simulations. The quantities of products and by-products that can be produced from the available milk pool to meet product specifications are calculated. Processing costs are simulated, the return from raw milk is calculated and the values per kg of fat, per kg of protein and carrier costs per litre are calculated in the model.

Fig. 1.

Schematic of the dairy processing sector model.

Dairy product production

The proportion of milk that is directed toward the production of each product is specified in the model. Some of the milk is separated into cream and skim milk based on the composition of: (a) the milk and (b) the final product to be manufactured (Table 1). The volumes of whole milk, cream and skim milk from separation are reconstituted in differing proportions to meet final product specifications. Excess cream not used in the production process can be sold or used in butter manufacture, with excess skim milk remaining from butter manufacture being used in the production of SMP. As this is a mass balance model, all components of the milk received are accounted for, whether they are utilized in product manufacture or lost in the production process. The compositions of the dairy products produced in the model are presented in Table 1 and remained constant throughout the year. The simulation for each of the dairy products produced in the model is described in detail below.

Table 1.

Composition of dairy products simulated in the model

* WMP, whole milk powder; SMP, skim milk powder; BMP, butter milk powder.

Cheese

Van Slyke & Price (Reference Van Slyke and Price1949) developed a cheese yield equation which is the industry norm today:

$Y = ((0 {\cdot} 93X + 0 {\cdot} 78X_{\rm P} - 0 {\cdot} 1) \times 1 {\cdot} 09)/(1 - W)$

where Y is the yield of cheese, X is the percentage fat in the milk, X _P is the percentage protein in the milk and W is the water content of the cheese.

Milk protein is made up of casein and whey, with the casein proportion going toward curd formation and the whey proportion leaving the cheese process in a liquid form. Cheese protein is calculated from the ratio of milk protein in the cheese yield (X _P/Y from Van Slyke), multiplied by the casein proportion of the protein (0·8), the efficiency of casein utilization (0·99) and an adjustment for non-casein protein in the cheese (1/0·98).

${\rm Cheese}\,{\rm protein} = \{ [((X_{\rm P} /Y) \times ({\rm casein}/X_{\rm P} )) \times 99\% ]/98\}$

The cheese yield is calculated by dividing the volume of cheese protein by the required protein content of the final cheese product, taken to be 0·245 in the current analysis (Table 1).

The whey by-product from cheese production is manufactured into whey powder. The volume of whey produced is calculated by subtracting the volume of cheese produced from the volume of milk to cheese less any calculated losses. The whey milk is evaporated to a water content of 480 g/kg then dried to a water content of 23·8 g/kg to produce whey powder. Cream left over from separation is used in the manufacture of butter.

Casein

The milk from separation is used in casein production. The casein yield is calculated by dividing the casein in cheese by the required protein content of the final casein product, assumed to be 0·89 in the current analysis. The whey by-product from casein production is manufactured into whey powder as described above. The cream from the separation process is used in the manufacture of butter.

Butter

Cream from separation and the surplus cream from other product processes (cheese, WMP, SMP, casein and fluid milk) are used in butter manufacture. The volume of butter that can be produced from this cream is calculated by dividing the volume of fat in cream by the required fat content in the final butter product, taken to be 0·84 in the current analysis (Table 1).

A by-product of butter manufacture is buttermilk, which is manufactured into BMP by evaporating to 500 g/kg water content then drying to 50 g/kg moisture content.

Whole milk powder

The standardized milk is evaporated to a moisture content of 480 g/kg then dried to 27 g/kg moisture content, holding fat, protein, lactose and minerals constant.

Skim milk powder

The standardized skim milk is evaporated to a moisture content of 480 g/kg and dried to a moisture content of 40 g/kg, holding fat, protein, lactose and minerals constant, to produce SMP.

Model outputs

Volume of milk used in the production of each product

The volume of milk used in the production of each product was calculated based on the defined product mix (monthly milk supply multiplied by the proportion of milk used in the production of each product per month).

Volume of products produced

The volume of product and by-product that can be produced based on the product mix, the volume of milk being processed, the composition of the milk being processed and the composition of the product being produced was calculated.

Total processing costs

Based on the product portfolio and the milk composition, the total costs (TC) per month of processing the volume of milk (using the volume-related processing costs and the volume of milk processed) and producing the product mix (using the product-related processing costs and the volume of products produced) were calculated.

Total revenue

The total revenue (TR) generated per month from the volume of products produced and the market value of the products produced was calculated.

Net milk value

The net milk value (NMV) per month, which is the difference between TR and TC, was calculated. The monthly NMV can also be calculated by multiplying the value per kg of fat by the total weight (kg) of fat plus the value per kg of protein multiplied by the total weight (kg) of protein minus the carrier cost per litre multiplied by the volume of milk processed per month.

The adjusted NMV was also calculated. When the mean calving date changed the milk solids concentrations per month also changed, which was incorporated into the overall analysis. However, to disentangle the milk solids concentration effect from the milk volume, product portfolio and processing cost effects the adjusted NMV was calculated. To calculate the adjusted NMV a standardized fat and protein proportion of 0·036 and 0·033, respectively, was assumed per month for both supply profiles. The adjusted NMV was then calculated as explained above using the calculated value per kg of fat, per kg of protein and per litre carrier cost, multiplied by the standardized weight (kg) of fat, protein and litres of milk.

Milk price

The monthly milk price per litre was calculated by dividing the NMV per month by the volume of milk being processed per month.

Component values of milk

As explained in Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010) the component pricing of milk, whereby a value per kg of fat and protein is calculated, was included in the model. The values per kg of fat and kg of protein were calculated from the NMV of products produced, the fat and protein requirements in the products produced and the weight (kg) of fat and protein available for processing. As explained in Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010), the marginal rate of technical substitution (MRTS) was used to calculate the component values of milk. The MRTS was used to determine the value per kg of fat and protein. The MRTS is the amount by which the quantity of one input, such as protein, can be reduced (–Δx ₂) when one extra unit of another input, such as fat, is used (Δx ₁) in order that the overall outcome, such as milk value, remains constant:

${\rm MRTS}\,(x_1,\,x_2 ) = {\rm \Delta} x_{2/} {\rm \Delta} x_1 = -{\rm MP}_1 /{\rm MP}_2$

where MP₁ and MP₂ are the marginal products of input 1 and input 2, respectively.

For each additional kg of fat or protein, the overall milk revenue will be increased depending on the product portfolio, product values and processing costs. The value of that increase is the marginal value of that component.

Model assumptions

The model assumptions are separated into two categories including raw milk and financial inputs.

Raw milk

The volume of raw milk being processed in this analysis was 5190 million litres/year, which was representative of the volume of domestic milk intake by creameries and pasteurizers in Ireland in 2010 (Central Statistics Office Ireland 2010). While in Ireland there are a number of milk processors, it was assumed in the current analysis that all of the milk flowed through one processor who could decide what products to produce. This is quite a sensitive assumption, as industry returns are highly dependent upon the type and value of products produced, which in reality is variable across processors. However, many processors now work closely together throughout the year with milk flowing between processors to maximize industry returns, minimize processing costs and increase overall profitability. Sensitivity analysis was carried out to demonstrate how sensitive the model outcomes are to product market values. Further detail on this sensitivity analysis is provided.

Financial inputs

Market values

There is some seasonal variation around product market values. The market values assumed in the current analysis were taken from the 2008–10 data of the Dutch official quotation system (Productschap Zuivel 2004–11) to capture this price variation. This is the quotation system referred to by the Irish Dairy Board in financial analyses. The market prices were representative of a 3-year average from 2008 to 2010 for each month of the year. The market values for butter, WMP, SMP and whey powder were representative of the market prices for the Netherlands. Market prices for cheese and casein were not quoted for the Netherlands; therefore, as recommended by dairy industry analysts, the market price for cheese was representative of the UK cheddar cheese market price (Datum UK 2004–11), since the market price per tonne of cheese would be similar between Ireland and the UK. The market price for casein was representative of the US casein market price (CLAL 2004–11) because it was deemed a good proxy for the market price per tonne of casein received in Ireland. As in Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010), the market price for BMP was assumed equivalent to the market price for WMP. The product market values assumed for the 12 months in the current analysis are presented in Table 2.

Table 2.

Product market values assumed per tonne of dairy product*

* Assumed average market prices per month from 2008 to 2010 as per the Dutch official quotation (see Productschap Zuivel 2004–11) for butter, WMP, SMP and whey powder. Cheese market prices were representative of the UK cheddar cheese market price (Datum UK 2004–11). Casein market prices were taken from the US casein market price (CLAL 2004–11).

† WMP, whole milk powder; SMP, skim milk powder; BMP, butter milk powder.

‡ Assumed BMP market values equivalent to WMP market values.

Processing costs

The processing costs and cost components published in Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010) were further validated via additional consultative processes with financial and managerial dairy processing personnel. Processing costs were split into two groups following Geary et al. (Reference Geary, Lopez-Villalobos, Garrick and Shalloo2010): volume-related processing costs (costs/litre) and product-related processing costs (costs/tonne). The volume-related processing costs include milk collection, milk standardization and volume-related processing costs which represent a number of cost components: 0·50 fuel and power, 0·33 direct labour and added ingredients. The product-related processing costs include packaging, storage, distribution and product-related processing costs which represent a number of cost components: 0·50 fuel and power, 0·66 direct labour, other direct expenses and effluent treatment and disposal. Via the validation process, each cost component was reviewed in turn for each of the dairy products being produced. The unit processing costs included in the current analysis were assumed to stay constant per month of year and were applied either to the volume of milk being processed (volume-related processing costs) or the volume of product produced (product-related processing costs).

In the Irish dairy industry storage and finance costs are incurred as a result of the seasonal supply profile. In the peak months, when the volume of milk being processed is greatest (May in the current analysis) the volume of product being produced exceeds demand. Therefore, excess product needs to be stored. As well as incurring storage costs there is also the cost of having capital invested in product which has not been sold: this cost is captured via the financing costs. In the current analysis, the demand for each product was calculated by summing the volume of product produced throughout the year and dividing this by 12 to give the constant monthly demand for each product. This assumption of a constant demand for dairy products is in line with The World Dairy Situation (International Dairy Federation 2010), which found demand for dairy produce to be steady. When the volume of product produced per month exceeds this demand, storage and financing costs are incurred but only on the volumes that exceed demand.

The processing costs included in the current analysis are presented in Table 3.

Table 3.

Volume- and product-related processing costs

* WMP, whole milk powder; SMP, skim milk powder; BMP, butter milk powder.

† BMP processing costs assumed equivalent to WMP.

‡ Quinlan et al. (Reference Quinlan, Keane, O'Connor, Shalloo, Gosh and Murray2010).

Fixed costs

Fixed costs were included in the current analysis at a rate of €0·015 per litre, which was validated in the consultation process. This was applied to the total volume of milk being processed in the year (5190 million litres) and the cost was spread evenly over the 12 months of the year. This cost incorporates rents and rates, depreciation, quality control, management, central research and development, marketing, administration and IT.

Scenario analysis

Supply profiles

Two milk supply profiles were examined, to demonstrate the impact a change within the seasonal supply profile has on industry returns in terms of the returns to the processor and the price paid to producers. Supply profile 1 was representative of the current Irish national milk supply profile with a mean calving date of 14 March, while supply profile 2 was representative of a supply profile with a mean calving date of 14 February, which is assumed to be optimum at farm level (Shalloo et al. Reference Shalloo, O'Donnell and Horan2007). The fat, protein and lactose compositions for both supply profiles were generated using SLAC lactation records (Olori & Galesloot Reference Olori and Galesloot1999).

Supply profile 1: MARCH. The national supply profile was taken from an Irish dairy processor and was assumed to be representative of the national milk supply profile with a mean calving date of 14 March. With this supply profile, peak milk production is in May, with 0·141 of the annual milk pool being produced in this month. This supply profile was representative of having 0·42 of cows calved by 1 March and 0·72 of cows calved by 1 April.

Supply profile 2: FEBRUARY. The February milk supply profile was representative of a mean calving date of 14 February, with 0·33 of cows calved by the end of January, 0·78 calved by the end of February and 0·93 calved by the end of March. The February milk supply profile has a lower peak in May of 0·136 relative to the March supply profile, with proportionately more milk supplied in the shoulders. The two supply profiles utilized in the current analysis are in Fig. 2 and result in different volumes of milk being processed each month of the year, as well as varying milk compositions.

Fig. 2.

March* and February* milk supply profiles. *Mean calving date 14 March, mean calving date 14 February.

Capacity constraints

In addition, three cheese and casein processing capacity constraints were examined, to demonstrate the impact on processor and producer returns when processing capacity for the higher returning products was altered. The first capacity constraint (Cap 1), similar to the current processing capacity in Ireland as per industry consultation, was 0·06 per month of the national milk pool (311 million litres). The second capacity constraint (Cap 2) was 0·09 per month of the national milk pool (467 million litres) and the third (Cap 3) was 0·12 per month of the national milk pool (623 million litres), partitioned into two-thirds cheese and one-third casein.

Product mix

In the current analysis, cheese and casein were not produced in the months of January and December. This is due to the poor milk quality in these months from spring calving herds, and is based on industry practice in Ireland (Guinee et al. Reference Guinee, Mulholland, Kelly and O'Callaghan2007). The product mix in the current analysis changes per month. For each of the analyses, the casein capacity was filled first, then the cheese capacity was filled and the remainder of the milk pool was apportioned to butter, WMP and SMP at a ratio of 53:24:23, respectively. This ratio was calculated based on the Irish dairy product mix of 2008 (FAOSTAT 2009) in which 0·30 of the milk pool was used to produce butter, 0·43 to produce cheese, 0·14 to produce WMP and 0·13 to produce SMP. Removing the 0·43 to cheese and recalculating the WMP, SMP and butter proportions to total 1·00 results in the ratios presented above. In the months of January and December, when cheese and casein were not produced, the milk pool was apportioned 0·43, 0·43 and 0·14 to butter, SMP and WMP, respectively.

In total, six scenarios representing the three capacity constraints for each of the two milk supply profiles were analysed.

Price sensitivity analysis

Sensitivity analysis was carried out to demonstrate how sensitive the model is to price changes in the market value of products. Using the March supply profile with the Cap 1 constraint, the product market values were varied. The product market values from 2004 to 2011 were utilized in the analysis. The market values from 2004 were chosen, because the lowest market value for cheese over the last 8 years (2004–11) was recorded in 2004 (cheese market prices were sourced from Datum UK 2004–11, casein market prices were sourced from US database CLAL (2004–11), all other market prices were sourced from Productschap Zuivel 2004–11). The 2011 product market values for January were utilized as the highest market values for cheese over this time period were recorded. No other model inputs were modified in the current analysis. The product market values used for cheese, butter, WMP, SMP, casein, whey and BMP for the 2004 analysis, based on an annual average, were €2375, €3005, €2558, €2044, €3990, €419 and €2558, respectively. For the 2011 analysis, the product market values used for cheese, butter, WMP, SMP, casein, whey and BMP were €3050, €3820, €3250, €2600, €6974, €900 and €3250, respectively.

RESULTS

The results for the two milk supply profiles utilizing three cheese and casein processing capacities are presented. The results are based on the national milk supply volume of 5190 million litres for 2010 (Central Statistics Office Ireland 2010) and are split into three sections, based on the three processing capacities examined. Within each of the processing capacity scenarios the results for both the March and February supply profiles are presented.

Monthly milk intake

Prior to presenting the processing model results, the monthly milk intakes for both supply profiles are presented. The proportion of total milk supplied, the volume being supplied and the fat, protein and lactose compositions per month of both milk supply profiles are presented in Table 4. Utilizing the MARCH supply profile the milk intake in January was 50 million litres, increasing to a peak milk supply in May and June of 734 and 699 million litres, respectively (Table 4). The FEBRUARY supply profile had a January milk intake of 16 million litres, increasing to a peak supply of 640 and 708 million litres in April and May. The FEBRUARY supply profile produced proportionately more milk than the MARCH supply profile in the months of February, March and April, with 0·269 of the annual milk pool produced in these 3 months for the FEBRUARY supply profile, relative to 0·213 for the MARCH supply profile. The monthly milk intake was the same across the three processing capacity scenarios examined.

Table 4.

Volume of milk processed and the associated fat, protein and lactose content of milk for the March* and February† supply profiles, Jan–Dec

* Mean calving date of 14 March. Fat, protein and lactose composition sourced from Central Statistics Office Ireland (2010).

† Mean calving date of 14 February. Fat, protein and lactose composition sourced from the Moorepark Dairy Systems Model (Shalloo et al. Reference Shalloo, Dillon, Rath and Wallace2004).

Capacity 1

The model results for both the MARCH and FEBRUARY milk supply profiles utilizing the Cap 1 constraints (208 million litres/month for cheese and 104 million litres/month for casein) are discussed here. The physical outputs of the model, including the volume of milk utilized in the production of each product and the quantity of product produced are presented in Table 5. The financial outputs of the model, including NMV, milk price and the component values of milk, are presented in Table 6.

Table 5

Monthly milk volumes used and volumes of products produced for the March* and February* supply profiles utilizing capacity constraint 1†

* Mean calving date 14 March, Mean calving date 14 February.

† Capacity constraint 1=cheese processing capacity of 207 596 000 litres/month; casein processing capacity of 103 798 000 litres/month.

‡ WMP, whole milk powder; SMP, skim milk powder.

Table 6.

Monthly net milk value, milk price and component values of milk for the March* and February* supply profiles utilizing capacity constraint 1†

* Mean calving date 14 March, Mean calving date 14 February.

† Capacity constraint 1=cheese processing capacity of 207 596 000 litres/month; casein processing capacity of 103 798 000 litres/month.

‡ NMV, net milk value (TR – TC).

§ The effect of seasonality on base price, variation in milk composition removed.

Milk utilization

With the FEBRUARY supply profile, 1% more milk (101 million litres) was used in the production of cheese relative to the MARCH supply profile (Table 5). As a result, 1% less milk (55 million litres) was used in the production of butter. In both supply profiles from March to October, the cheese processing capacity was filled.

Quantity of products produced

The casein capacity was filled for 10 months of the year (February–November) across the two supply profiles. The difference in the casein yield was due to the different fat, protein and lactose compositions of the supply profiles (Table 5). With the FEBRUARY milk supply profile, an additional 13 840 tonnes of cheese and 2400 less tonnes of butter were produced relative to the MARCH supply profile.