Download Performance, Profit and Risk

Document related concepts

Systemic risk wikipedia, lookup

Transcript
Performance, Profit and Risk
in Pasture-based Dairy Feeding Systems
Findings from the TasMilk60 study
Produced by Dairy Australia’s Grains2Milk program
Performance, Profit and Risk in Pasture-based Dairying Systems – Findings from the TasMilk60 study
© Copyright Dairy Australia 2011. All rights reserved.
ISBN: 978-0-9871000-5-4
Publisher: Dairy Australia
Acknowledgements:
Dairy Australia’s Grains2Milk program gratefully acknowledges the many contributors to the TasMilk60 study:
• The participating Tasmanian dairy farmers for the goodwill and support offered to the project team over three very
different and sometimes trying years.
• Gordon Cleary (Agrilink FarmStats), whose meticulous management of the data collection provided the high-quality
dataset essential for the study. He provided much of the source material for this booklet.
• John Morton (Jemora), whose statistical and analytical input was critical to extracting full value from the dataset. He
also provided much of the source material for this booklet.
• Pam Hartin (Agrilink FarmStats) for her diligent efforts in on-farm data collection.
• Mark Fergusson and his team (Tasmanian Institute of Agricultural Research) for their efforts in on-farm data
collection from farmers associated with the DairyTas Benchmarking project, Dairy Business of the Year (DBOY).
• Courtney Gronow (Agrilink FarmStats), whose work with the Monte Carlo simulations contributed significantly to the
risk assessment studies described in Section 4.
• Those whose technical support strengthened the study outcomes: Brendan Cullen (The University of Melbourne),
Karen Christie and Richard Rawnsley (TIAR), Don Thomson (Landscape & Social Research) and Warwick Waters
(Waters Consulting).
• Those who provided data and/or relevant commentary to the project team, including: Andrew Box and staff
(Fonterra), Darren Smart (Cadbury), National Foods, Keith Davis (Impact Fertilisers), Brett McGlone (Incitec Pivot),
Trevor Macleod (Tasmanian Stockfeed Services), Hugh McMullan (Animal Mineral Solutions), Andrew Radford
(John Lawrence), Aaron Robertson (WHK Pinnacle), Peter McBain, Dan Huggins (Maxi Cow Consulting), Andrew
Angelino (Andrew Angelino Consulting ), Peter Harrisson (Farm Mapping Services) and Laurie Hooper (Agritech).
This project was supported by funding from Dairy Australia and the Dairy Service Levy.
Enquiries:
Steve Little
Grains2Milk program leader for Dairy Australia
Locked Bag 104, Flinders Lane, Victoria 8009
e-mail: [email protected]
web: www.dairyaustralia.com.au
Copyright permission:
If you wish to reproduce information contained in this booklet, contact Grains2Milk program leader, Steve Little,
or Dairy Australia’s Corporate Communications team.
This booklet is published for your information only. It is published with due care and attention to accuracy, but Dairy Australia makes no
warranty with regard to the accuracy and reliability of the information provided, and accepts no responsibility for loss arising in any way
from or in connection with errors or omissions in any information or advice or use of the information. The information is a guide only and
independent professional advice should be sought regarding your specific circumstances.
Table of Contents
Foreword .............................................................. 3
How to use this booklet ....................................... 4
Quick Quiz – True or False? ...................... fold out
Executive summary .............................................. 5
4. Risk – Insights from TasMilk60....................... 39
Risk and uncertainty................................................... 39
Risk management ...................................................... 39
Risk measures ........................................................... 40
Monte Carlo simulation study using TasMilk60 data..... 40
Where to from here? .................................................. 45
1. The TasMilk60 study......................................... 7
5. Other uses of TasMilk60 data to date............ 47
How TasMilk60 was done ............................................ 7
DairyMod pasture study.............................................. 47
The TasMilk60 farms................................................... 14
Greenhouse gas emissions study................................ 49
2. Farm performance – Findings from TasMilk60.....17
6. Conclusions ................................................... 51
Pasture utilised per hectare ........................................ 17
Total feed intake per cow............................................ 17
Milk yield and composition.......................................... 18
Feed conversion efficiency.......................................... 19
7. Appendices .................................................... 53
Tips and traps when collecting farm data
(Gordon Cleary) .......................................................... 53
Operating costs.......................................................... 22
Six principles for identifying determinants of
profitability by analysing data from groups of
dairy farms (John Morton) .......................................... 54
Change in physical and financial aspects
year to year................................................................ 24
8. References ..................................................... 57
Income and milk price................................................. 21
3. Farm profitability – Findings from TasMilk60.... 25
Associations between profitability measures................ 25
Farm profitability for each study year........................... 26
Determinants of profit within each study year............... 27
Change in profit due to management changes............ 30
Effect of enterprise size on farm profit ........................ 33
Degree of consistency of profit outcomes achieved
year to year................................................................ 34
Determinants of consistent relative profit performance
over the three study years........................................... 36
Correlation between farm profitability and
‘farming style’............................................................. 37
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
1
2
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Foreword
Soon after Dairy Australia’s Grains2Milk program
commenced in 2007 a need was identified for a
comprehensive, multi-year dataset of farm performance
across a spectrum of farms using different feeding
strategies within, as best was possible, the same
climatic and market context.
The farm dataset was intended mainly for internal
use by Dairy Australia and its research, development
and extension partners to enhance the industry’s
understanding of the interaction between grain/
concentrate input, feeding system performance, profit
and risk in pasture-based dairy feeding systems.
The results of this Grains2Milk farm monitoring study
needed to be relevant to all dairying regions. However
it was necessary to select one region from which to
collect the dataset to ensure the same climatic and
market context.
Tasmania was selected for the study because it offered
two important features. First, its dairy industry is
predominantly a pasture-based one and has been less
affected by drought than mainland dairying regions in
recent years. Second, the Tasmanian dairy industry
has a wide range of feeding approaches. While many
farmers choose to operate a low input/low production
system with little or no grain/concentrates, in recent
years others have adopted the moderate to high grain
/ concentrate feeding levels typically seen in mainland
dairying regions.
This divergence made it possible for Grains2Milk to
collect physical and financial performance data across a
wide range of feeding approaches, extending to the more
extreme ends of the performance spectrum than would
be available in any other Australian dairying region.
This booklet summarises the findings from the
TasMilk60 study. It explains the methods we used to
analyse the dataset, the key findings and what these
mean for industry. While confirming many widely held
views, these findings also dispelled several myths, and
provide new insights into farm performance,
profitability and risk, and how they inter-relate. The
booklet will help farmers and their advisers make
feeding system decisions relevant to their own situations
with less imperfect knowledge.
We hope you find the booklet valuable.
Steve Little
Grains2Milk program leader for Dairy Australia
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
3
How to use this booklet
You will get the most out of this booklet if you firstly
take 10 minutes to complete the Quick Quiz.
To see how many statements you correctly answered
True or False, open the fold-out panel.
Follow the signpost to the specific page in the body
of the booklet that provides the supporting evidence
from the TasMilk60 study for each answer.
See Section 1 for information about how the
TasMilk60 study was done and a summary of the
physical features and financial characteristics of the
farms that were studied.
This booklet can be used as a personal reference.
However, it may also be a useful resource to support
group discussions among farmers and advisers on
aspects of farm performance, profitability and risk.
The Table of Contents will also guide you to particular
aspects of farm performance, profitability and risk you
are interested in which are discussed in Sections 2, 3
and 4.
4
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Quick Quiz – True or False?
Read each statement below and circle whether you think it is true or false.
When you have completed the quiz, open the fold-out panel to see how many statements you correctly answered.
Follow the signposts to the supporting evidence from the TasMilk60 study in Sections 2, 3 and 4 of this booklet.
Statement
True or False?
(Circle below)
1. Farm performance
Answer
Go to:
1. Farm performance
a. Farms that feed higher levels of concentrates per cow per year achieve poorer pasture utilisation
(tonnes / hectare / year).
True
False
False
Despite fears of substitution, there is no simple relationship between the level of concentrates fed per cow and pasture utilised per
hectare. Good and poor pasture utilisation occurs at all levels of concentrate feeding.
b. Farms that feed lower levels of concentrates per cow get their cows to eat more pasture
(tonnes / cow / year).
True
False
True
However, there is a larger variation in pasture intake per cow between farms feeding the same level of concentrate than there is
between farms feeding different levels of concentrate, so herd and grazing management are critical.
c. The milk protein to fat ratio does not vary between farms feeding different levels of concentrates per cow.
True
False
False
Milk protein to fat ratio is higher on farms where higher levels of concentrates are fed. This is largely due to the reduced fat
concentrations found in milk with higher daily milk volumes per cow.
p18
d. Feed conversion efficiency (FCE) is generally better on farms where higher levels of concentrates per
cow are fed.
True
False
True
This is largely due to the higher total feed intakes on high concentrate-feeding farms, and hence, the greater proportion of
nutrients that are used for milk production and the lesser proportion used for maintenance.
pp19-20
e. Most pasture-based dairy farms feeding one to two tonnes of concentrates per cow in the bail achieve
about the same feed conversion efficiency (FCE).
True
False
False
Feed conversion efficiency (FCE) is highly variable at all levels of concentrate feeding.
pp19-20
f. Changing to a higher level of concentrates per cow, if well managed, should guarantee higher milk
production and improved feed conversion efficiency (FCE) within one year.
True
False
False
When a dairy farm changes its production / feeding system, adjustments are needed (cows, people, farm infrastructure) to realise
the full benefits. It may take several years before the benefits are fully realised.
p20
g. Increasing feed conversion efficiency (FCE) within a farm’s chosen feeding system can be a powerful
lever for increasing farm profit.
True
False
True
This is particularly so in higher milk price years, when every 0.1 increase in energy-corrected litres of milk / kilogram feed DM may
be worth $200+ extra milk profit per cow per year.
p20
h. Dairy farmers have little control over the price they are paid for their milk.
True
False
False
Prices paid to farmers (even those supplying the same manufacturer) vary widely due to on-farm factors including milk volumes,
milk fat and protein concentrations and seasonality of milk supply pattern.
p21
i. Herd, shed and overhead costs are the most important operating costs to control in a dairy farm
business.
True
False
False
Feed costs, and labour and management costs are generally the highest proportions of operating costs and vary widely between farms.
Conversely, herd, shed and overhead costs are relatively small components of operating costs and are less variable between farms.
2. Farm profitability
p17
pp17-18
pp22-23
2. Farm profitability
a. The best measure of farm profitability to use when comparing farms with different land and herd
resources is ‘Milk EBITD per hectare’.
True
False
False
In most farm populations, milk EBITD per cow, hectare, litre, kilogram milk solids, and return on capital (based on milk EBITD) are
closely correlated with each other, so are likely to tell the same story about farm profitability.
p25
b. There is no ‘best’ concentrate feeding level or production / feeding system.
True
False
True
Any concentrate feeding level or production / feeding system can be profitable in any year, given an appropriate mix of
management, milk price and input costs.
p26
c. There are large variations in profitability between farms at each level of concentrate feeding (low /
moderate / high), and from year to year.
True
False
True
The differences in average or median profits achieved between farms using low / moderate / high concentrate feeding levels are
small compared with the variability in profit within each concentrate feeding level.
p26
d. With feed being the greatest operating input cost on any dairy farm, low feed costs per cow are
essential to achieve high farm profitability.
True
False
False
Dairy farms are complex systems, and there are many ways to make a profit (or a loss). Determinants of profit should be assessed
collectively, not separately when analysing farm performance. Income from milk sales is an important component of profit as are
costs – higher feed costs may be justified if they generate extra profit by lifting milk income.
pp27-29
e. In high milk price years, most dairy farms actively pursue higher profit by implementing major
management changes.
True
False
False
Most farms take a ‘business as usual’ approach in high milk price years and don’t take opportunities to substantially increase farm
profit above what the milk price alone delivers. Few farms make large systemic management changes year to year.
pp30-32
f. Milk EBITD per cow and per hectare increase markedly with increased enterprise size (more cows,
hectares or total milk production).
True
False
False
Any beneficial effects of ‘dillution’ of costs per cow with more cows are not large. Increases in total farm profitability with increased
enterprise size are likely to be due to scale, not efficiency.
p33
g. The most profitable dairy farms are consistently profitable year after year.
True
False
False
Relative farm profitability (high or low) is not very repeatable from year to year, so results in a single year may not reflect profit
performance over the longer term.
h. Farms that have consistently higher profits usually have relatively higher milk price and milk yield per
cow, lower pasture and fodder costs, and lower labour and management costs.
True
False
True
However, they tend not to be outstanding performers for any of these particular determinants of profit – they tend to be consistent
all-rounders whose efforts for these profit determinants collectively are superior.
p36
i. Farmers with a wide range of management styles and sets of attitudes and beliefs run successful dairy
farm businesses.
True
False
True
Farmers don’t require a certain management style or set of attitudes and beliefs to be highly profitable.
p37
3. Risk
pp34-36
3. Risk
a. Something that is risky is best avoided.
True
False
False
Attempting to avoid all risk is often futile or counter-productive. Instead, risk exposure can be reduced by spreading, selling, or
shifting risk or by risk averaging.
p39
b. For the dairy farmer, there is always a trade-off between risk and reward.
True
False
True
Farmers with a low risk tolerance will seek options where little risk is involved and require a very high reward for the risk involved.
Those with a higher tolerance for risk will be willing to accept risk without such a big potential reward.
p39
c. If you want to minimise risk, aim to maximise the amount of pasture in the diet and feed a low level of
concentrate.
True
False
False
For all production / feeding systems, there is a mix of risk, performance and management principles whose understanding can
improve the chances of successfully balancing risk and reward.
p39
d. A farm’s unit cost of milk production and operating profit margin are important risk measures for a dairy
farm business.
True
False
False
These are economic measures, not risk measures. They do not provide information about the probability of favourable and
unfavourable outcomes.
p40
e. Pasture utilisation, pasture quality and core costs per cow are key profit drivers in all pasture-based
production / feeding systems, regardless of the level of concentrates fed.
True
False
True
These three on-farm profit drivers can have a greater impact on profit variability than the off-farm profit drivers of milk price,
concentrate price and purchased fodder price over which farmers have less control.
pp40-44
Executive summary
The TasMilk60 study was an observational study done
to better understand the interaction between grain/
concentrate input, risk, management skills and profit
in pasture-based dairying systems. It involved the
collection and analysis of a comprehensive dataset of
farm physical and financial performance over three years
(2006/07, 2007/08, and 2008/09) across a spectrum of
farms using different grain/concentrate feeding rates in
the same climatic and market context.
We chose to do the study in Tasmania for two main
reasons. First, it has been less affected by drought
than mainland dairying regions in recent years. Second,
Tasmania enabled Grains2Milk to collect physical and
financial performance data across a wide range of
pasture-based feeding approaches, extending to more
extreme ends of the performance spectrum than would
be available in any other dairying region of Australia.
The study enrolled:
• 21 farms that were low concentrate feeders
(<1t/cow/year)
• 27 farms that were moderate concentrate feeders
(1 to <2t/cow/year)
• 21 farms that were high concentrate feeders
(≥2t/cow/year)
The study did not aim to enrol farms in proportion to
the distributions of Tasmanian or Australian farms by
concentrate feeding category. Similar numbers of farms
in each of the three categories were enrolled to allow
much more precise statistical analyses to be performed.
Data were collected from managers of selected
farms by either Agrilink FarmStats P/L (AGFS) or the
Tasmanian Institute of Agricultural Research (TIAR),
and from relevant milk supply companies, accountants,
stockfeed and fertiliser suppliers with the manager’s
permission. Data collection was managed by Agrilink
FarmStats. All statistical analyses were performed by
epidemiologist John Morton (Jemora P/L).
The three years of the TasMilk60 study – 2006/07,
2007/08 and 2008/09 – were one of the most volatile
periods in terms of trade seen for many, many years,
and in times of less favourable climatic conditions.
As such, the findings of how well farmers using different
feeding approaches responded and reacted to the
challenges thrown at them, particularly over the full
three-year timeframe rather than just a single year,
provide a telling testament to what worked and what
did not, and lessons relevant for dairy farmers and
advisers managing pasture-based feeding systems
across Australia.
The findings from Grains2Milk’s TasMilk60 study support
the following conclusions:
1. Farm performance
Despite fears of substitution, there is no simple
relationship between the amount of concentrates fed
per cow and pasture utilised per hectare. Both good
and poor pasture utilisation is seen at all levels of
concentrate feeding.
Farms that feed lower levels of concentrate per cow
get their cows to eat more pasture. However, there is
a larger variation in pasture intake per cow between
farms feeding the same level of concentrate than there
is between farms feeding different levels of concentrate,
so herd and grazing management are critical.
Milk protein to fat ratio is generally higher on farms
where higher rates of concentrates are fed. This is
largely due to the reduced fat concentrations found in
milk with higher daily milk volumes per cow.
Feed conversion efficiency (FCE) is highly variable at
all concentrate feeding levels. It is generally better on
farms where higher amounts of concentrates per cow
are fed. This is largely due to the higher total intakes on
high concentrate feeding farms and, hence, the greater
proportion of nutrients that are used for milk production
and the lesser proportion used for maintenance.
Total feed intake per cow explains more than half the
variability in FCE. However, there are also several other
important factors known to help optimise FCE, including
maintaining high feed quality and good rumen function,
and minimising feed gaps, feed wastage and energy
losses.
Increasing FCE within a farm’s chosen feeding system
can be a powerful lever for increasing farm profit,
particularly in higher milk price years.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
5
When a dairy farm changes its production / feeding
system, adjustments are needed (cows, people, farm
infrastructure) to realise the full benefits. It may take
several years before the benefits are fully realised.
Milk prices paid to farmers (even those supplying the same
manufacturer) vary widely due to on-farm factors under
their control, including milk volumes, milk fat and protein
concentrations, and the seasonality of milk supply pattern.
Farmers aiming to increase profitability by reducing
costs should review and monitor feed costs, and labour
and management costs, as these generally constitute
the highest proportions of operating costs and vary
widely between farms. Conversely, herd, shed and
overhead costs are relatively small components of
operating costs and are less variable between farms.
2. Farm profitability
In farm populations such as that examined in the
TasMilk60 study, milk EBITD per cow, hectare, litre and
kilogram milk solids, and return on capital (based on
milk EBITD) are closely correlated with each other, so
are likely to tell the same story about farm profitability.
Most farms maintain a ‘business as usual’ approach in
high milk price years and do not take opportunities to
substantially increase farm profit above what the milk
price alone delivers. Few farms make large systemic
management changes year to year.
Beneficial effects on farm profitability of increasing
enterprise size are generally relatively small and are likely
to be due to scale, not efficiency.
Relative farm profitability is not very repeatable when
milk prices fluctuate widely year to year, so results in a
single year may not reflect profit performance over the
longer term.
Farmers do not require a certain management style or
set of attitudes and beliefs to be highly profitable.
3. Risk
Risk and uncertainty are central to all dairy farming
decisions. Attempting to avoid all risk is often futile
or counter-productive. Instead, risk exposure can be
reduced by spreading, selling or shifting risk, or by risk
averaging.
There is no ‘best’ concentrate feeding level or
production / feeding system. Any concentrate feeding
level or production / feeding system can be profitable in
any year, given an appropriate mix of management, milk
price and input costs.
For the dairy farmer, there is always a trade-off between
risk and reward. Those with a low risk tolerance will
seek options where little risk is involved and require
a very high reward for the risk involved. Those with a
higher tolerance for risk will be willing to accept risk
without such a big potential reward.
The differences in average and median profits
achieved between farms using low, moderate and high
concentrate feeding levels are small compared with
the variability between farms within each concentrate
feeding level.
For all production/feeding systems, there is a mix of
risk, performance and management principles whose
understanding can improve the chances of successfully
balancing risk and reward.
Dairy farms are complex systems and there are many
ways to make a profit (or a loss). Determinants of
profit should be assessed collectively, not separately,
when analysing farm performance. Farms that have
consistently higher profits usually have a relatively higher
milk price, higher milk yield per cow, lower fodder costs,
and lower labour and management costs. However,
they tend not to be outstanding performers for any of
these particular determinants of profit – they tend to be
consistent all-rounders whose efforts for these profit
determinants collectively are superior. Income from milk
sales is as important a component of profit as are costs
– higher feed costs may be justified if they generate
extra profit by lifting milk income.
Risk measures should provide the probability of
unfavourable and favourable outcomes occuring.
Unfortunately, many so-called risk measures currently
used in the dairy industry do not. These erroneous risk
measures include pasture as a percentage of total feed
consumed, unit cost of milk production and operating
profit margin.
Pasture utilisation, pasture quality and core costs per
cow are key profit drivers in all pasture-based dairy
feeding systems, regardless of the level of concentrates
fed. These three on-farm profit drivers can have a
greater impact on profit variability than the off-farm profit
drivers of milk price, concentrate price and purchased
fodder price, over which farmers have less control.
Steve Little
Grains2Milk program leader for Dairy Australia
6
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 1
The TasMilk60 study
This section explains how the TasMilk60 study was done and describes physical features and financial
characteristics of the farms that were studied.
TasMilk60 was purely an observational study. No
attempt was made to influence the physical and
financial performance of the study farms. Findings were
based entirely on natural variations in these, between
farms and farm years.
Three remarkable years to study
farm performance, profit and risk
The three years of the TasMilk60 study – 2006/07,
2007/08, and 2008/09 – were remarkable in many ways.
Seasonal conditions
Seasonal conditions were generally drier than is usual
for most of northern Tasmania during these three years
(Figure 1). Only Smithton, in 2008/09, exceeded the
decade average rainfall, with Deloraine and Scottsdale
receiving generally below average rainfall in all three years.
Milk prices
Market conditions were highly volatile over the threeyear study period, as reflected in the milk price table
below (Table 1). After being paid an average milk price
in 2006/07, farmers enjoyed the highest milk price ever
in 2007/08. However, 2008/09 saw the first milk price
step-down in more than 30 years, due to the flow-on of
the global financial crisis to the export-orientated milk
companies. Announced in December and effective from
February, this created substantial financial pressures
for many farmers, including debt pressures from new
working capital requirements.
04/05 05/06
06/07
07/08
08/09
25.9
27.2
30.9
33.6
36.5
50.2
41.3
$/kg/MS 3.43
3.54
4.05
4.39
4.79
6.63
5.40
Grain prices
As shown in Figure 2, the three years of the TasMilk60
study coincided with an extended period of relatively
high cereal grain prices. Like milk prices, grain prices
peaked in 2007/08 with the global financial crisis.
Cumulative monthly rainfall
TasMilk60 was a retrospective/prospective longitudinal
study. Annualised physical and financial data were
collected from 69 convenience sampled Tasmanian
dairy farms for between one and three consecutive
financial years from 2006/07 to 2008/09.
02/03 03/04
¢/litre
Cumulative monthly rainfall
Study overview
Table 1: Milk price history – Tasmania (Source: DA).
Year
Cumulative monthly rainfall
How TasMilk60 was done
1200
1000
Seasonal rainfall – Smithton
TasMilk60 study
800
600
400
200
0
1400
1200
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Seasonal rainfall – Deloraine
TasMilk60 study
1000
800
600
400
200
0
1200
1000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Seasonal rainfall – Scottsdale
TasMilk60 study
800
600
400
200
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure 1: Cumulative monthly rainfall (Source: Bureau
of Meteorology).
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
7
Other farm inputs
Many other input costs, particularly fertiliser and fuel,
escalated substantially in 2007/08, the year of the
highest milk price seen, as shown in the index of farm
input costs below (Figure 3).
As a result, for many farmers the increased income
arising from the higher milk price was offset by the
increased overall cost of providing feed for their cows.
The milk price downturn in 2008/09 coincided with an
improvement in fertiliser costs, but trading conditions
again clearly put the different feeding approaches in use
among TasMilk60 farms to the test.
Summary
In summary, the TasMilk60 study was conducted
during one of the most volatile trade periods seen for
many, many years, and in times of less-than-favourable
climatic conditions.
As such, the findings of how well farmers using different
feeding approaches responded and reacted to the
challenges thrown at them, particularly over the full threeyear timeframe rather than just a single year, provide a
telling testament to what worked and what did not.
Farm selection
Data were collected from the managers of selected
farms by either Agrilink FarmStats (AGFS) (46 farms) or
personnel from the Tasmanian Institute of Agricultural
Research (TIAR) (23 farms). AGFS farms were selected
from those using consulting nutritionists, Andrew
Angelino (Andrew Angelino Consulting) and Daniel
Huggins (Maxi Cow Consulting), and TIAR farms were
selected from those participating in the Dairy Business
of the Year benchmarking event in Tasmania.
$500
Barley
Triticale
Wheat
This farm selection process was chosen so that
approximately similar numbers of farms where
<1t dry matter of grain/concentrates, 1t to <2t and
≥2t were fed per cow per year would be enrolled. This
was desirable to maximise precision when analysing
data within each of these three concentrate feeding
categories. The study did not aim to enrol farms
in proportion to the distributions of Tasmanian or
Australian farms by concentrate feeding category.
Data collection
Data collection was managed by Agrilink FarmStats
(AGFS). Farm data were collected by AGFS or TIAR
personnel from managers of selected farms using a
joint data collection instrument developed by AGFS
specifically for the project in conjunction with Mark
Fergusson of TIAR. More detailed data were required
than are routinely collected with benchmarking programs
such as AGFS’s MilkMark®, or Red Sky,® as used in the
Dairy Business of the Year (DBOY) event.
For most farm years, data were collected within
6-12 months of the end of each financial year, although
for a small number of farm years the data were collected
more than 12 months after the end of the relevant
financial year.
While a great deal of the data was obtained from the
participating farm managers, some were collected from
the relevant milk supply company, accountant, stockfeed
or fertiliser supplier or other sources with the manager’s
permission.
Physical and financial data were recorded and analysed
separately by financial year for each study farm.
Customised spreadsheets were developed for handling
field data on-farm. Data collectors had the choice of
using a hard-copy version of the survey instrument or
working through the electronic version on-screen.
140%
TasMilk 60
study
120%
$400
Price index vs. 2000
$300
$200
$100
100%
80%
TasMilk 60 study
Electricity
Feed, grazing, cultivation and harvesting
Fertiliser, lime and seeds
Fuel
Wages and salaries
Overall farm expenses
60%
40%
20%
9
Ja 6
n
9
Ja 7
n
9
Ja 8
n
9
Ja 9
n
00
Ja
n
0
Ja 1
n
02
Ja
n
0
Ja 3
n
0
Ja 4
n
0
Ja 5
n
0
Ja 6
n
0
Ja 7
n
0
Ja 8
n
0
Ja 9
n
1
Ja 0
n
11
n
Ja
Ja
n
95
$0
Figure 2: Cereal grain prices, ex-Melbourne
(Source: AWF).
8
0%
2001
2002
2003
2004
2005
2006
2007
2008
Figure 3: Index of farm input prices, base = year 2000
(Source: DA).
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
On-farm sessions inevitably raised data issues that
required further clarification and follow-up. Given that
the TasMilk60 study required a high-quality dataset,
extraordinary efforts were made by AGFS to validate
data with help from participating farmers and many
others (see acknowledgements on the inside front cover
of this booklet).
Farm physical data
Milk outputs are easy to find, as they are recorded by
the receiving milk factory. Various source documents
help record what has happened to physical inputs once
these arrived on-farm. These include grain, hay or silage
invoices of purchase, contractors’ invoices for fodder
conservation and fertiliser delivery dockets. However,
it is tired farmers who are often the only source of hard
data about these.
Given its focus on feeding approaches, the TasMilk60
study needed accurate information about cow feeding,
so that accurate pasture back-calculations could be
carried out. As a result, farmers were asked questions
about their feeding approach from different angles to
help verify what was fed to which livestock.
For example, invoices for grain might show 765 tonnes
purchased. The farmer’s daily feeding rate might
be nominated at 6kg/day/cow for a 400-cow herd
averaging 300 day lactations. Thus, 720 tonnes has
purportedly been fed to the milking herd, leaving
45 tonnes to be accounted for, usually through the
feeding of dairy replacements.
When fertiliser was applied to areas used for potatoes,
fodder crops, poppies or vegetables, both the quantity
and cost was isolated from the dairy enterprise.
Per cow inputs and outputs are central to exercises
such as TasMilk60. The effective herd size number to
use as the denominator is often subject to negotiation,
as issues such as attrition and culling, peak cows and
carried-over cows. These were discussed and resolved
with each farmer in the TasMilk60 study.
The other key denominator, hectares of milking area,
is usually a firmer number to define, although seasonal
conditions can change how some land is used over
the season. That non-milking area on the hillside was
defined as a milking area if a proportion of the milking
herd spent time on the hill.
Farmers were asked to estimate the average body
weight of the herd when in early lactation and average
changes in body condition from the start to the end
of each year. However, these data were not used
in statistical analysis of determinants of profitability
due to concerns about estimation errors and lack of
standardisation between assessors.
Fodder reserves carried over between financial years
represented both an inventory issue in respect of pasture
utilisation in the year if such reserves were fed out and
also a cost issue if the cost was effectively carried over
into the next year. Special-purpose validation tools were
developed by AGFS for fodder inventory.
Stock inventory changes also required careful
consideration. We included stock sales income in
dairy income (i.e. milk plus stock sales plus other dairy
income), but did not include costs of purchased cattle
as operating costs. This methodology could affect
comparisons across farm years where some farms
sell unusually high numbers of cattle, and/or receive
breeding cattle rather than chopper prices, and where
some farms purchase breeding cattle. As explained
in Section 3, in TasMilk60 milk and dairy EBITD were
closely correlated (Figure 25), demonstrating that these
effects were minimal for the study farms. However, to
minimise these effects, we focused on milk EBITD rather
than dairy EBITD throughout.
Fodder wastage was not measured. A default wastage
figure of 20% of quantity feed (offered) was assumed for
both homegrown and purchased hay and silage across
all farms and used in all energy computations.
The physical data collected needed to include those
inputs necessary for pasture consumption to be
estimated by the Department of Primary Industries
Victoria (DPIV) Pasture Consumption & Feed Conversion
Efficiency Calculator (2010) and those inputs necessary
for the estimation of pasture growth using DairyMod (as
described on pages 47-48). Examples of the type of
measurements required were:
• For pasture growth:
– GPS identification of the dairy farm;
– climate data from the Bureau of Meterology
(BOM) and/or SILO database;
– soil types;
– N fertiliser applications;
– pasture types; and
– usual irrigation protocol.
• For pasture consumption:
– amount and estimated ME of supplementary
feeds fed during the period;
– milking area;
– cow details, including estimated body weight and
condition;
– proportion of first calvers;
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
9
– numbers of productive and non-productive
animals on the farm over time; and
– daily distance walked by cows and type of terrain.
Farm financial data
Farm financial data are generally easier to acquire
than physical data, with manual or computerised farm
accounting systems and many source documents
available, including cheque books, bank statements,
invoices, receipts, tax returns, wages book, etc. However,
financial data collection still presents challenges.
Of principal concern in identifying revenue and expenses
within a given financial year is whether accounts have
been prepared on a cash or accrual basis. Cash is often
the visible tip of the iceberg, with accruals the unseen
bulk below. TasMilk60 was conducted on an accrual,
not cash, basis.
• does not include income from stock sales and other
dairy income; and
• includes non-cash costs (imputed labour and
management).
Farm EBITD is a ‘whole of farm’ measure of profitability,
but is inappropriate for comparisons between farms of
different sizes, as it can increase markedly with farm
size, as reflected by number of cows, hectares and
farm milk production. So, to account for farm size, milk
EBITDs per cow and hectare were used extensively in
this study; milk EBITDs per litre and kilogram of milk
solids were also used.
Hard copies of the relevant financial statements for the
most recent financial year were sought by the AGFS or
TIAR personnel charged with data collection. By choice,
the process placed emphasis on the farmer’s own
management accounts, rather than his accountant’s
annual tax summary, because the former was likely to
be more in tune with practical farm management
(and the needs of the study) than the accountant’s taxbased chart of accounts.
Analyses of study data indicates that any bias due to
costs not increasing proportionately with farm size is
probably quite small, and certainly far less than the effect
of farm size on farm EBITD, EBITD per cow, hectare, litre
and kilogram milk solids. Accordingly, these measures
were superior to farm EBITDs when making profitability
comparisons between farms of different sizes.
For example, tax returns frequently show between four
to six separately described Repairs and Maintenance
cost accounts – Repairs (tracks), Repairs (fences),
Repairs (Irrigation), Repairs (Dairy), Repairs (Structures),
etc. It is not unusual to see $60,000 worth of
expenditure spread across these detailed accounts.
However, this flight to detail can be neutralised entirely
if $400,000 worth of grain, $100,000 worth of hay and
$80,000 worth of silage-making are bundled together
into a single ‘Fodder’ cost account. Disaggregation is a
time-consuming activity.
• The approach taken to stock trading was to include stock sales as dairy
income, stock purchases as capital expenditure (rather than as operating
costs), and replacement costs as operating costs, given they are
necessary for ongoing operation of the farm enterprise. These decisions
were irrelevant to most of this study as most analyses are based on milk
EBITD rather than dairy EBITD.
Financial categories and calculations
The income and operating cost categories used in
TasMilk60 are shown in Figure 4, and categories of
non-operating costs, and assets and liabilities in
Figure 5. Methods of calculating various measures of
farm profitability are shown in Figure 6.
Since the principal focus of the TasMilk60 study was to
gather information about the financial performance of
different feeding approaches, it was decided to focus on
Milk EBITD as the key profit parameter, rather than Dairy
EBITD, which includes income from stock sales and
other dairy income.
10
Milk EBITD stands for Earnings Before Interest, Tax
and Depreciation. It is calculated by subtracting dairy
operating costs (herd + shed + feed + labour and
management + overheads) from milk income. As such,
Milk EBITD:
Note:
• Changes in fodder inventory were accounted for by including fodder
costs only for fodder consumed in the particular farm year. Average unit
costs were used.
• Changes in grain inventory were disregarded as most farms had little
scope for storing substantial quantities of grain.
• Similarly, changes in fertiliser inventory were disregarded, as few farms
would have purchased and stored fertiliser on-farm for the following
financial year.
• Advance payments for grain and fertiliser were attributed to the year in
which these inputs were used, rather than to the year of purchase.
Statistical analysis
All statistical analyses described in this report were
performed by epidemiologist John Morton (Jemora
P/L), using data supplied by AGFS for annual periods
(2006/07, 2007/08 and 2008/09). The farm year was
the unit of analysis for almost all analyses. A ‘farm year’
consisted of data from one farm for a single year. A farm
enrolled in all three years of the study contributed three
farm years of observations.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Figure 4: Income and operating cost categories.
OPERATING
INCOME
Milk income
Dairy Income
Stock sales
Gross milk income less levies & volume charge
Other dairy
income
OPERATING
COSTS
Herd costs
Mature stock
costs
Animal health, identification, herd recording, dogs,
sundries
Replacement
costs
Artificial breeding, agistment, animal health, calf
rearing supplies, feeds & fodder, identification
Dairy supplies & rubberware, chemicals & sanitation,
electricity, garbage disposal
Shed costs
Feed costs
Labour &
management
Overhead
costs
Milker
supplements
Concentrates (protein grain/meals, other grains,
minerals, additives), by-products, purchased hay &
silage
Pastures (inc
forage crops)
Fertiliser & spreading, land lease, soil testing,
effluent ground-working, sprays (weeds & pest),
seed, diesel, fuel & oil
Fodder &
irrigation
Milking area fodder, agistment for mature stock,
outpaddock, irrigation power & fuel, water licence &
rates, temporary water purchase, water monitoring
Labour costs
Gross wages (incl PAYG), allowances, Workcover,
superannuation, training courses, protective
clothing, staff amenities
Imputed
management &
labour
Repairs &
maintenance
Dairy, drains & lanes, fences & gates, irrigation,
machinery, stock water, structures, vehicles,
workshop & tools
Other
overheads
General freight & cartage, donations, equipment
hire, farm vehicles, farm insurance, memberships &
subscriptions, shire rates, travel expenses
Administration
Accountant, consultants, legal fees, office supplies,
telephone/fax & Internet
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
11
Figure 5: Categories of non-operating costs, and assets and liabilities.
NON OPERATING
COSTS
Overdraft interest, loan interest, lease charges,
HP charges, bank charges, cow leases
Finance costs
Personal
costs
Drawings
Principal
Co-op shares
Capital costs
Land & water
Livestock
Machinery
ASSETS &
ASSETS &
LIABILITIES
LIABILITIES
Land &
improvements
Dairy assets
improvements
Livestock
Machinery
Dairy
liabilities
12
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Figure 6: Methods of calculating various measures of farm profitability (EBITD = earnings before interest, taxation
and debt repayment; *capital costs are restricted to those for the dairy enterprise).
Milk income
Operating costs
Milk
EBITD
Dairy assets
Milk return
on capital
Dairy assets less
dairy liabilities
Milk return
on equity
Dairy assets
Dairy return
on capital
Dairy assets less
dairy liabilities
Dairy return
on equity
Stock sales and
other dairy
income
Dairy
EBITD
Non-cash items
(Imputed labour
& management)
Dairy
cash
surplus
Finance,
personal
&
capital
costs *
Change in
overdraft
& cash
reserves
Change
in asset
value
Change
in net
dairy
worth
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
13
Physical features
The TasMilk60 farms
Predominant breed
Number of farms
Sixty-nine farms were studied for between one and
three years. Fifty-six farms were studied for all three
years.
The predominant breed was Holstein-Friesian on almost
60% of the study farms and Holstein-Friesian X Jersey
cross on a further 26% of farms. Herds where cows
were fed more concentrates were more likely to be
Holstein-Friesian and less likely to be cross-bred.
Cow bodyweight
Grain / concentrate feeding
categories
Farms were categorised by the amount of grain/
concentrate (‘concentrates’) fed to the cows in each
year. The categories used were less than 1t DM fed
per cow per year, 1t to less than 2t, and 2t or more
(throughout this report, concentrate feed amounts are
described as tonne DM, rather than on an ‘as fed’ basis,
unless otherwise stated). Study farms were distributed
approximately evenly across all three categories
(Table 2) and most remained in the same category for all
years when studied.
The estimated weights of cows were higher on farms
where ≥2t of concentrates were fed across all farms and
among farms with predominantly Holstein-Friesian herds.
Calving system
Over three-quarters of the herds on the study farms
were seasonal calving, with 68% (47/69) calving in
spring and a further 9% (6/69) calving in autumn. Herds
on the remaining farms were split calving or yearround calving. Calving systems were similar across
concentrate feedings categories.
Herd size
Table 2: Distribution of study farm years by concentrate
feeding category and year.
Category
Year
Pooled
2006/07
2007/08
2008/09
<1t
21
18
17
56
1t to <2t
27
26
21
73
≥2t
21
22
19
62
Pooled
69
66
57
191
Based on Grains2Milk’s feeding system classification
(Types 1 to 5) (Little, 2010), about two-thirds of farms
used System 2 (moderate-high bail), with most of the
remaining farms using System 1 (low bail). Only nine of
the 191 farm years studied were in years when farms
were using System 3 (PMR).
Concentrate feeding experience
The concentrate feeding experience of the managers
of the study farms at the start of 2006/07 varied widely.
The AGFS-recruited participants generally had more
experience than the TIAR-recruited participants. While
some managers with no experience fed more than
1t concentrates in 2006/07, all managers of farms
where ≥2t were fed had at least one previous year of
experience feeding concentrates.
14
Across the 191 farm years, the median herd size was
381 cows. Herd sizes ranged from 106 to 1,400 cows
and most herds were between 200 and 800 cows.
Herd sizes were similar across concentrate feeding
categories, but increased as the study progressed.
Milking area
Across the 191 farm years, the median milking area was
153 hectares. The median milking area was 177ha on
farms where cows were fed less than 1t concentrates,
compared to 140ha and 148ha for farms where cows
were fed 1t to <2t and ≥2t concentrates, respectively.
Stocking rate
Stocking rates were mostly between 1.5 and four cows
per milking area hectare. Across the 191 farm years,
the median stocking rate was 2.5 cows per hectare.
The median stocking rates were similar in all three
concentrate feeding categories. Stocking rates were
higher in 2007/08 and 2008/09.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Use of irrigation
Equity and debt
Irrigation was commonly used. Part or all of the milking
area was irrigated in 84% (160/191) of farm years
(86%, 79% and 87% in years where cows were fed less
than 1t, 1t to <2t and ≥2t concentrates, respectively).
Across farms using any irrigation, the median
percentage of the milking area that was irrigated was
53%; the median was 41% on farms where cows were
fed less than 1t concentrates, compared to 64% and
56% on farms where cows were fed 1t to <2t and ≥2t
concentrates, respectively.
Median equity in dairy assets in each farm’s first year
in the study was 70% (range 12% to 100%; equity on
most farms was initially between 40% and 100%).
Financial characteristics
Assets
The median value of dairy assets in each herd’s first year
in the study was $3.7m (range $1.1m to $9.3m; most
farms were between $2m and $7m). This equated to
medians of $9,329 per cow (range $5,355 to $20,657)
and $22,348 per hectare (range $7,629 to $61,970;
most farms were between $15,000 and $45,000).
Assets per cow and hectare did not vary significantly
with concentrate feeding category.
On all the farms, the highest proportion of assets by
value were land and improvements; this proportion
was a little lower on farms where more concentrates
were fed due to the greater contribution of livestock on
those farms. Machinery constituted a relatively small
proportion of dairy asset value on all farms.
Median debt in each farm’s first year in the study was
$1m. This equated to medians of $2,811 per cow
(range $0 to $8,195) and $6,049 per hectare (range $0
to $22,483).
Income
Milk income contributed between 80% and 96%
(averaged within farm over all study years) of total
dairy income (median 94%). Income from stock sales
contributed between 2% and 14% (median 5%). Other
income contributed between 0% and 6% (median 1%).
Most farms supplied Fonterra, but the milk supply
company was National Foods and Cadbury in about
20% and 10% of farm years, respectively. Relative to
farms supplying Fonterra, those supplying National
Foods were more likely to feed ≥2t of concentrates, and
to either calve seasonally in autumn or use split-calving.
Breed distributions did not differ markedly between
farms supplying National Foods and those supplying
Fonterra.
For details of milk income received per kilogram milk
solids, per cow and per hectare on farms across the
three concentrate feeding categories, see page 21.
Note: The next two sections of this booklet represent data from the TasMilk60 study in a series of box plots,
otherwise known as ‘box and whiskers’ graphs. These are based on percentiles.
1,500
How to read a ‘box and whiskers’ graph
▼
▼
▼
• Each value outside of the whiskers is considered an ‘outlier’ and shown as a dot
▼
−500
• The shaded box includes the middle half of the farms. The lower border of the
shaded box is the 25th percentile, the upper border is the 75th percentile and the
horizontal line within the box is the middle farm, i.e. the 50th percentile.
• The lower limit of the whiskers is the value for the lowest farm that is on or within
1.5 interquartile ranges* below the 25th percentile
0
500
1,000
• The upper limit of the whiskers is the value for the highest farm that is on or within
1.5 interquartile ranges* above the 75th percentile
* Interquartile range is the difference between the 25th and 75th percentiles.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
15
16
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 2
Farm performance
– Findings from TasMilk60
This section describes findings from the TasMilk60 study about key physical and financial aspects of farm
performance, and explains what these mean for industry.
Pasture utilised per hectare
Total feed intake per cow
What we did
What we did
Pasture utilised per hectare of milking area was
estimated for each farm year using DPI Victoria’s
Pasture Consumption & Feed Conversion Efficiency
Calculator (2010).
Total feed intake per cow was calculated by adding
the estimated quantity of pasture consumed per cow
(pasture utilised per milking hectare divided by stocking
rate) to the quantities of homegrown and purchased
fodder, grain/concentrates and any other feeds fed to
the milking herd throughout the year.
What we found
What we found
Total feed intake per cow was not always higher on
farms where more concentrates were fed (Figure 8).
Pasture intake per cow in situ was generally between
2.5t and 4t DM per cow per year (Figure 9). Mean
intakes were 0.2t and 0.3t DM less on farms where
1t to <2t and ≥2t of concentrates were fed, compared
to where <1t concentrates were fed. These differences
were small compared to the large variation in pasture
intake per cow in situ between farms.
08/09
Figure 7: Distribution of pasture utilised per hectare
from the milking area by concentrate feeding
category and year.
What this means for industry
the amount
There is no simple relationship between
ure utilised
of concentrates fed per cow and the past
agement
per hectare. Good and poor pasture man
ing.
feed
te
entra
occurs at all levels of conc
6
4
<1 1 to <2 >=2
07/08
2
<1 1 to <2 >=2
06/07
0
<1 1 to <2 >=2
Total feed intake per cow (t DM)
8
5
10
Pasture intake per cow in situ as a percentage of diet was
generally highest on farms where <1t concentrates were
fed (typically 60-90%) and much lower (typically 40-55%)
0
Pasture utilised per hectare (t DM)
15
The study farms generally utilised between 5t and 15t
DM of pasture from the milking area (both grazed and
removed as fodder; Figure 7). The median amounts
of pasture used were similar on farms in all three
concentrate feeding categories across all study years,
showing that there was no simple relationship between
the amount of concentrate fed per cow and pasture
utilised per hectare. However, the amount of pasture
utilised varied markedly between herds.
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure8: Distribution of total feed intake per cow
(t DM) by concentrate feeding category and year.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
17
5
4
What we did
2
3
A farm’s average milk solids yield per cow was
calculated as the sum of farm fat and protein yields
divided by the peak number of cows milked for at least
60 days.
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 9: Distribution of pasture intake per cow in situ
(t DM) by concentrate feeding category and year.
on farms where ≥2t of concentrates were fed. However,
there was considerable overlap in this variable between
concentrate feeding categories in each study year.
Farms feeding moderate-high levels of concentrates
generally also fed cows more conserved fodder. However,
percentages of diets that were fodder overlapped
considerably between these groups of farms (Figure 10).
Concentrates generally constituted <20%, 20-30% and
28-45% of total feed intake on farms feeding <1t, 1t to
<2t, and ≥2t concentrates, respectively.
What this means fo
r industry
There was an important variation in fat and protein
concentrations in milk between farms in the same
concentrate feeding category.
• Fat concentration was lower on farms where more
concentrates were fed, but protein concentrations did
not differ significantly between concentrate feeding
categories.
• Protein to fat ratios were higher on farms where more
concentrates were fed, largely due to the reduced fat
concentrations in milk from those farms. Among the
farms with predominantly Holstein-Friesian herds, the
fat percentage was generally lower, while the protein
percentage was slightly higher on farms where more
concentrates were fed (Figure 12). Higher protein to
fat ratios on these farms were largely due to lower fat
percentages.
700
600
500
400
300
Milk solids per cow (kg)
100
0
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
06/07
07/08
08/09
Figure 10: Distribution of fodder intake per cow
(t DM) by concentrate feeding category and year.
18
200
2
1
0
Fodder (home−grown and purchased)
intake per cow (t DM)
3
Total feed intakes pe
r cow are not alway
s higher on
farms where more
concentrates are fed
.
Farms feeding mode
rate-high levels of co
ncentrates
generally also feed
more conserved fod
der.
It is difficult to get a
cow to consume mu
ch more than
3.5t DM of pasture
per year, regardless
of
the amount of
concentrates and fod
der fed.
Milk yield per cow varied substantially between herds
within the concentrate feeding category (Figure 11).
Generally, higher milk yields per cow on farms where
more concentrates were fed translated into higher milk
yields per hectare in this group of farms.
800
1
What we found
0
Pasture intake per cow in situ
ie while grazing (t DM)
Milk yield and composition
Figure 11: Distribution of milk solids per cow by
concentrate feeding category and year.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
1.1
1
Determinants of feed conversion efficiency were
assessed across all concentrate feeding categories.
.9
Annual milker feed conversion efficiency (FCE) was
calculated using the DPIV Pasture Consumption & Feed
Conversion Efficiency Calculator as litres of energycorrected milk (corrected to 3.14 MJ NE per litre) per cow
divided by total annual feed intake (expressed as kilogram
of DM) (Beever and Doyle, 2007).
.8
Protein to fat ratio
.7
What we did
.6
Feed conversion efficiency (FCE)
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 12: Distribution of protein to fat ratio by
concentrate feeding category and year for farms with
predominantly Holstein-Friesian cows.
What this means for industry
litre can vary
The average kilogram milk solids and
from farm to farm.
significantly in their protein to fat ratio
er on farms where
Protein to fat ratio is generally high
more concentrates are fed.
Marginal versus average feed
conversion efficiencies
Annual feed conversion efficiencies can be either
marginal or average.
Marginal annual feed conversion efficiency describes
the expected increase in the farm’s annual energycorrected litres per cow following feeding of an
extra kilogram DM of annual feed intake per cow.
Marginal annual feed conversion efficiencies were not
estimated in this study.
The feed conversion efficiencies reported above are
average annual feed conversion efficiencies. They
should not be interpreted as the expected annual
milk yield response to extra annual feed intake for
the farm. For any particular farm year, they may be
either less or more than the marginal annual feed
conversion efficiency.
These annual feed conversion efficiencies should also
not be confused with short-term milk yield responses
to additional feed intakes.
The feed conversion efficiency achieved by the
TasMilk60 farms feeding 1t to <2t concentrate per cow
over all three study years were examined more closely
using data on years of experience feeding at this level of
concentrate to see if there was any correlation.
A modeling exercise was done with the TasMilk60
dataset to estimate the financial value of an incremental
change in feed conversion efficiency.
What we found
The FCE was generally higher on farms where higher
amounts of concentrates were fed, but there were large
variations between farms in all concentrate feeding
categories (Figure 13).
Across all farm years, 41% of farms feeding
<1t concentrate per cow achieved Grains2Milk’s
recommended target of 1.0L/kg DM for System 1 farms,
while 16% of farms feeding 1t to <2t and 31% of farms
feeding ≥2t concentrate per cow achieved Grains2Milk’s
recommended target of 1.2L/kg DM for System 2 farms.
Key drivers of FCE
Total feed intake per cow was positively associated with
and explained 55% of the variance in feed conversion
efficiency (Figure 14). This occured because, with
increasing feed intake, a larger proportion of the diet
was available for milk production, i.e. maintenance
requirements were ‘diluted’ by increasing total feed
intakes.
In addition to optimising total daily feed intake and
providing a nutritionally balanced diet that meets all
the cows’ requirements, other things known to help
optimise FCE are to:
• maintain high feed quality;
• maintain good rumen function;
• minimise feed gaps throughout the year;
• minimise feed wastage; and
• minimise energy losses.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
19
Table 3: FCE by number of years concentrate feeding
(farms feeding 1t to <2t conc. / cow over all three years).
1.4
1.2
<1 1 to <2 >=2
2007/08
2008/09
Figure 13: Distribution of feed conversion efficiency
by concentrate feeding category and year for farms
with predominantly Holstein-Friesian cows.
1.4
50%
56%
14%
11%
11%
Increasing feed conversion efficiency within your chosen
feeding system (Types 1 to 5) can be a powerful lever
for increasing profit, particularly in higher milk price
years such as 2007/08 (Figure 15). For that year, milk
EBITD increased by between $200 and $500 per cow
for every 0.1 improvement in FCE, depending on the
grain feeding category. This contrasted with negligible
to modest benefits of $40 to $120 / cow in the low milk
price year of 2006/07.
3
4
5
6
7
8
Total feed intake per cow (t DM)
Figure 14: Association between total feed intake per
cow and feed conversion efficiency.
−1000
Milk EBITD per cow ($ )
1500
Beneficial effects of FCE were due to combined effects
of increased total feed intakes, and/or increased FCE at
a given feed intake.
ributor to high
Total feed intake per cow is a major cont
r management
feed conversion efficiency. However, othe
factors are also important.
ding system,
If a dairy farm changes its production/fee
le, infrastructure)
adjustments are needed (cows, feed, peop
some years.
to realise the full benefits. This may take
feed costs for
There is significant potential to reduce
feed conversion
the same milk yield through managing
feed nutrient costs).
efficiency (and also through controlling
in your chosen
Increasing feed conversion efficiency with
erful lever for
pow
a
be
feeding system (Types 1 to 5) can
price years.
milk
er
increasing farm profit, particularly in high
1.2
41%
13%
1
19%
≥4 years
.8
2-3 yrs
.6
33%
.4
39%
.2
45%
0
69%
1000
0-1 yrs
500
Top
quartile
(top 25%)
0
3rd
quartile
−500
2nd
quartile
$ value of an incremental change in FCE
20
<1 1 to <2 >=2
2006/07
Feed conversion efficiency
(energy−corrected lites per kg DM)
Bottom
quartile
(bottom 25%)
What this means for industry
1
<1 1 to <2 >=2
Feed conversion
efficiency quartiles
No. of years
concentrate
feeding
.8
Two-thirds of farms in the top quartile for feed
conversion efficiency had at least 2-3 years concentrate
feeding under their belt, whereas more than two-thirds
of farms in the bottom quartile for feed conversion
efficiency had 0-1 years feeding concentrates (Table 3).
The major reason for this was because farmers with
more prior concentrate feeding experience fed higher
total feed intakes per cow, a strong determinant of feed
conversion efficiency.
.6
Years of experience feeding concentrates versus FCE
Feed conversion efficiency
(l of energy−corrected milk per kg DM)
Visit www.dairyaustralia.com.au for more detailed
Grains2Milk information about strategies for optimising
feed conversion efficiency in each of the five main
feeding systems.
.6
.8
1
1.2
1.4
Feed conversion efficiency
(energy−corrected litres per kg DM)
Figure 15: Relationship between FCE and milk EBITD
per cow in year two of the study (2007/08). Green
represents <1t concentrate per cow, blue 1 to <2t and
gold ≥2t.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Income and milk price
Milk income per cow and hectare
What we did
Milk income per cow (Figure 17) and per hectare
(Figure 18) were generally higher on farms where more
concentrates were fed, but varied substantially within
each concentrate feeding category.
Milk prices received were analysed by year and
manufacturer supplied, and then compared across
concentrate feeding categories.
dustry
What this means for in
5,000
4,000
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
15,000
10,000
5,000
Milk income per hectare ($ )
7
6
5
0
4
Milk income per kg milk solids ($ )
<1 1 to <2 >=2
Figure 17: Distribution of milk income per cow by
concentrate feeding category and year.
8
Milk price was lower on farms with seasonal spring
calving (the most common calving system among
the study farms) in two of the three study years, but
was higher in 2007/08. After adjusting for milk solids
concentration and calving system, milk protein to
fat ratio was positively associated with milk price in
2007/08, but not in the other two years. This occurred
despite the milk protein to fat ratio being higher in all
three study years on farms where more concentrates
were fed (Figure 12).
3,000
However, there was marked variability in milk prices
between herds within years, even among farms that
supplied the same manufacturer, Fonterra (Figure 16).
This was potentially due, in part, to farm-level factors
under management control, including milk volumes,
milk fat and protein concentrations and the seasonality
of the supply pattern.
2,000
The median milk prices received by the study farms
varied markedly between years, from 33¢ per litre in
2006/07 to 50¢ in 2007/08 to 39¢ in 2008/09; median
milk incomes per kilogram milk solids were $4.35, $6.57
and $5.11 per kilogram, respectively.
ce than
re control over milk pri
Dairy farmers have mo
luding
gh on-farm factors inc
they may realise throu
tions and
and protein concentra
milk volumes, milk fat
ly pattern.
seasonality of milk supp
1,000
Milk income per kilogram milk solids
Milk income per cow ($ )
What we found
<1 1 to <2 >=2
06/07
<1 1 to <2 >=2
07/08
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
08/09
06/07
07/08
08/09
Figure 16: Distribution of milk income per kilogram
milk solids by concentrate feeding category and year
for farms supplying Fonterra.
Figure 18: Distribution of milk income per hectare by
concentrate feeding category and year.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
21
Operating costs
Table 4: Percentages of operating costs by category for 191
farm years.
What we did
Cost category:
The cost categories described on page 11 were
analysed by year and then compared across
concentrate feeding categories.
What we found
Operating costs per cow, hectare and
kilogram milk solids
Operating costs per cow (and per hectare) were
generally higher on farms where more concentrates
were fed, but varied substantially within each
concentrate feeding category (Figure 19). Median
operating costs per cow were approximately $1,400
and $3,000 on farms feeding less than 1t and 2t or
more of concentrates, respectively.
Operating costs per litre and per kilogram milk
solids were generally higher on farms where more
concentrates were fed, but differences between these
groups of farms were much less marked (Figure 20).
All of these operating cost measures varied substantially
between farms in the same concentrate feeding
category, indicating that there is considerable potential
for reducing costs at similar levels of concentrate
feeding.
Operating cost components
Range
Feed
56%
19-73%
Labour and management
24%
7-54%
Herd
8%
2-20%
Shed
3%
1-11%
Overhead
9%
4-24%
Feed costs generally constituted higher proportions
of operating costs on farms where more concentrates
were fed (Figure 21), and labour and management costs
generally constituted lower proportions (Figure 22).
Herd, shed and overhead costs were generally relatively
small components of operating costs and proportions
of operating costs that were herd, shed and overhead
costs varied less between farm years.
As would be expected, feed costs were predominantly
supplement costs (concentrates, purchased and
non-milker area fodder, and other purchased feeds)
on farms where 1t or more of concentrates were fed,
whereas pasture costs were generally major feed costs
on farms where less than 1t of concentrates were fed.
The cost for milking area fodder and irrigation generally
constituted less than 15% of feed costs.
Costs of concentrates (including protein and other
grains and meals, and minerals) were mostly between
$300 and $600 per tonne as fed; they were lower for
feeds fed in 2006/07 (median price per tonne $433)
than 2007/08 ($550) and 2008/09 ($545). They were
generally higher on farms where more concentrates
were fed as these farms were more likely to include
protein supplements, minerals and other additives
with grain to provide a more nutritionally balanced diet
8
6
4
0
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
06/07
07/08
08/09
Figure 19: Distribution of operating costs per cow by
concentrate feeding category and year.
22
2
Operating costs per kg milk solids ($ )
4,000
3,000
2,000
1,000
0
Operating costs per cow ($ )
5,000
Feed costs generally constituted the highest proportion
of operating costs, followed by labour and management
costs. Herd, shed and overhead costs were generally
much smaller proportions of operating costs (Table 4).
Median
Figure 20: Distribution of operating costs per kg milk
solids by concentrate feeding category and year.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
400
300
200
0
100
Pasture, fodder and irrigation cost s
per tonne DM utilised from milking area ($ )
70
60
50
40
30
20
10
Percentage of operating costs that were feed cost s
0
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 21: Distribution of percentages of operating
costs that were feed costs by concentrate feeding
category and year.
to their cows. However costs of concentrates varied
substantially within concentrate feeding categories in
each year. These differences are likely to be due to
variation in both types of feeds and unit prices.
Interestingly, farms where more concentrates were
fed also generally spent more on pasture, fodder and
irrigation costs per milking hectare. This was due, in
part, to higher fertiliser expenditure on these farms. It
was not explained by greater use of pasture renovation.
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 23: Distribution of pasture, fodder and
irrigation costs expressed per tonne DM pasture
utilised from the milking area by concentrate feeding
category and year.
What this means for industry
operating
There is considerable potential for reducing
ng.
costs at any given level of concentrate feedi
ing
Farmers aiming to increase profitability by reduc
and
,
costs
costs should review and monitor feed
labour and management costs. Large increases
gh
in profitability are unlikely to be achieved throu
they
as
costs
ead
overh
reductions in herd, shed and
costs
ting
are relatively small components of opera
and are less variable between farms.
rtunities
Further research is required to identify oppo
income.
for reducing feed costs without reducing milk
Marginal versus average costs
per tonne of pasture
20
30
40
50
The costs per tonne of pasture calculated in this
study can be considered as average annual costs of
pasture. They should not be interpreted as marginal
costs. (Marginal annual costs are calculated as the
extra pasture utilised divided by the extra annual input
costs for a given strategy. The marginal cost per tonne
of pasture can be either more or less than the average
cost of pasture at the same input costs).
10
Note, too, that only pasture, fodder and irrigation costs
were included in these calculations; other operating and
non-operating costs were not included.
0
Percentage of operating costs that wer e
labour & management cost s
Average pasture, fodder and irrigation costs per tonne
of pasture were calculated as pasture, fodder and
irrigation costs divided by tonnes DM of pasture utilised
from the milking area. These costs were higher on farms
where more concentrates were fed (Figure 23) because
pasture, fodder and irrigation costs per hectare were
generally higher, but pasture utilised per hectare was no
higher on these farms.
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 22: Distribution of percentages of operating
costs that were labour and management costs by
concentrate feeding category and year.
These annual costs of pasture should also not
be confused with the costs of short-term pasture
responses to additional inputs to grow more pasture.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
23
Change in physical and
financial aspects year to year
What we did
Changes in physical features and financial
characteristics over successive years were described
for 65 Tasmanian dairy farms from 2006/07 to 2007/08,
and for 57 farms from 2007/08 to 2008/09.
These changes were of particular interest as milk prices
varied substantially between these years, with the
majority of farms receiving typical long-term prices in
2006/07, very high prices in 2007/08, and high opening
prices that were reduced markedly mid-year in 2008/09.
Two types of change were considered:
• Systematic changes across the group of study
farms in which the group generally (i.e. collectively)
shifted (up or down).
• Changes within farms between years.
It is possible to have no important systematic
change but substantial changes within farms. This
would occur if changes within farms were evenly
balanced with increases on some farms and decreases
on others.
Changes within farms between years were assessed
using absolute differences, and measures of agreement
and correlation.
What we found
Small systematic changes occurred for some variables.
Herd size generally increased across years, consistent
with a long-term trend in the Australian dairy industry.
Pasture utilised per hectare generally increased during
the study, as did stocking rates. Milk yields also generally
increased from 2006/07 to 2007/08, but increases in
medians were relatively small. None of the pasture,
fodder or concentrate intakes changed systematically
by important amounts over this period, despite the large
differences in milk price between these years.
24
Despite large differences in milk price from 2006/07
to 2007/08, there were no large systematic changes
to milk yield, pasture, fodder or concentrate intakes
between those years, even though there were large
changes in some variables in some herds. Most farms
took a ‘business as usual’ approach; only a few farms
took a ‘make hay while the sun shines’ approach and
made significant changes to optimise their profit in the
high milk price year (2007/08). This may have been
because farmers were uncertain as to the optimal
changes for their circumstances and farm goals.
Alternatively, rapid changes in response to the high milk
price may have been impractical or difficult. It is possible
that these farmers generally aimed to satisfy rather
than maximise income, and so made few management
changes to maximise income in the high milk price year.
In an increasingly volatile operating environment (climatic
and markets), dairy farmers do need to ‘make hay while
the sun shines’ in the good years to increase equity
and/or farm performance so they cope better during
more difficult years. We need to better understand
farmer decision making in the face of sudden,
substantial increases in milk price, and to identify
practical management strategies that can be rapidly
implemented to capitalise on this situation.
What this mean
s for industry
For many reason
s, most dairy farm
s do not make
large systemic m
anagement chan
ge
s from year to
year in response
to changing mar
ket conditions.
It is critical that fa
rms have a stable
and confident
platform from wh
ich to respond or
ch
ange. Change
due to instability
is often eroded or
unprofitable.
Farmers need m
ore support in id
entifying practical
management stra
tegies that can be
rapidly
implemented to ta
ke greater advant
age of high milk
price years when
they occur.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 3
Farm profitability
– Findings from TasMilk60
This section describes findings from the TasMilk60 study about farm profitability, and explains what these
mean for industry.
Milk EBITD per cow, hectare, litre, kilogram milk
solids and return on capital (milk EBITD) were closely
correlated with each other (Figure 24). Least close
correlations were with return on equity. This was
presumably because return on equity is determined
not only by farm management decisions and external
factors, but is also sensitive to equity, and equity was
not closely correlated with management decisions and
external factors. The close correlation between milk
EBITD per cow and per hectare occurred because, in
the study population, neither milk EBITD per cow nor per
hectare were closely related to stocking rate.
Milk
EBITD
per cow
Milk
EBITD
per ha
1500
1000
−500
0
What we found
500
Twelve measures of farm profitability that were comparable
across farms of different sizes were assessed.
Dairy EBITD per cow ($ )
What we did
2000
Associations between profitability
measures
−500
0
500
1000
1500
2000
Milk EBITD per cow ($ )
Figure 25: Associations between milk and dairy EBITD
per cow for all farm years combined.
The same relationships were observed for the
corresponding dairy EBITD measures. In addition,
the corresponding milk and dairy EBITD measures
(e.g. milk EBITD per cow and dairy EBITD per cow) were
all closely correlated (Figure 25). That was because dairy
EBITD differed from milk EBITD only in that it included
stock sales and other dairy income, both of which were
relatively minor compared to income from milk sales.
What this means for in
dustry
Milk
EBITD
per
litre
Milk
EBITD per
kg milk
solids
Return on
capital
(milk
EBITD)
In farm populations su
ch as that examined in
the
TasMilk60 study, milk
EBITD per cow, hecta
re,
litre,
kilogram milk solids an
d return on capital (milk
EBITD)
are closely correlated
with each other, so are
like
ly to
tell the same story abou
t farm profitability.
Return on
equity
(milk
EBITD)
Figure 24: Associations between profitability measures based on milk EBITD for all farm years combined.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
25
Farm profitability for each study year
What we did
Given the close correlations between most measures of
profitability, we focused on two measures: milk EBITD
per cow and milk EBITD per hectare.
What we found
With these two measures, there were large variations
in profitability between farms in various concentrate
feeding categories and years (Figures 26 and 27).
In the lowest milk price year (2006/07), milk EBITD
per cow was most variable on farms where 1t to <2t
concentrates were fed. The reasons for this are unclear,
but this result may reflect greater management difficulty
in optimising this system in low milk price years or less
experience feeding concentrates among managers of
these farms at the time of the study.
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 26: Distribution of milk EBITD per cow by
concentrate feeding category and year.
4,000
2,000
0
−2,000
Milk EBITD per hectare ($ )
1,000
500
0
−500
Milk EBITD per cow ($ )
1,500
6,000
In the year with highest milk price (2007/08), the median
milk EBITD per cow was higher on farms where 1t or
more of concentrates were fed. In the other two years,
milk prices were lower and the median milk EBITD per
cow was higher on farms where <1t of concentrates
were fed relative to farms where 1t to <2t concentrates
were fed. However, these differences in medians
between concentrate feeding categories were small
compared with the variability within each category, and
the highest values were similar for all three categories.
<1 1 to <2 >=2
<1 1 to <2 >=2
<1 1 to <2 >=2
06/07
07/08
08/09
Figure 27: Distribution of milk EBITD per hectare by
concentrate feeding category and year.
s for industry
What this mean
ing level for
concentrate feed
There is no ‘best’
entrate feeding
system. Any conc
ing
ed
fe
n/
tio
uc
prod
n be profitable
eding system ca
fe
/
n
tio
uc
od
pr
ement,
level or
riate mix of manag
op
pr
ap
an
n
ve
gi
in any year
ut costs.
milk price and inp
profits
average or median
The differences in
/ moderate / high
n farms using low
achieved betwee
all compared with
ing levels are sm
ed
fe
te
tra
en
nc
co
ncentrate
ofit within each co
the variability in pr
feeding level.
26
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Determinants of profit within each
study year
It is unlikely that highly profitable farms have every
indirect and direct determinant at values for maximum
(indirect or direct) effects on profit. To explore how
farmers managed selected profit determinants
collectively on individual commercial farms, the five
most and five least profitable farm years were identified
within each concentrate feeding category and values
for selected indirect and direct profit determinants
assessed. These profit determinants were identified
from the final path models as those most likely to have
important indirect or direct effects on milk EBITD per cow.
What we did
Direct and indirect determinants of profitability were
modelled using path analysis. Indirect determinants
were those whose effects were assumed to be
mediated through an intervening variable. For example,
milk protein percentage was assumed to influence
milk EBITD per cow through its effect on milk price per
litre. Direct determinants of profitability were assumed
to directly determine profitability, rather than via an
intervening variable.
What we found
Some of the results of these analyses are shown in
Table 5 below. These show the values for selected profit
determinants of milk EBITD per cow for the five most
profitable farm years where 1t to <2t of concentrates
were fed per cow.
Figure 28 (see following page) shows the null path model
of milk EBITD per cow. Separate final path models were
developed for each of the three concentrate feeding
categories. Importance of direct determinants was
assessed based on the estimated changes following
increases in each determinant from the 25th to 75th
percentile values observed on the study farms. Thus,
importance was determined by the combination of the
strength of association of the relationship and the amount
of variation in the determinant in the study farms.
The best way to summarise all these data is using
Table 6. This shows, for each profit determinant,
the proportions of farm years with values more than
10% better and more than 10% worse than the median.
Within each concentrate feeding category, for all profit
determinants other than milk price, some of the five
highest profitability farm years had values worse than
the median and/or some of the five lowest profitability
farm years had values better than median.
Many of the same determinants were retained in the
final models for all three concentrate feeding categories,
indicating that some determinants affect profitability
regardless of concentrate feeding category.
Table 5: Values for selected indirect and direct profit determinants of milk EBITD per cow for the five most profitable and five least
profitable farm years where 1t to <2t of concentrates were fed per cow.
Median*
Farm number
Year
Milk EBITD per
cow
Milk price (¢/litre)
Milk yield (litres
per cow)
Feed costs per
cow
Labour and
management
costs per cow
Herd costs per
cow
Shed costs per
cow
Overhead costs
per cow
Total costs per
cow
Highest milk EBITD per cow**
58
32
18
2007/08 2007/08 2007/08
1,243
1,141
1,091
33
2007/08
1,082
56
2008/09
-534
Lowest milk EBITD per cow**
64
65
66
2006/07 2006/07 2008/09
-555
-588
-631
247
1
2007/08
1,657
68
2006/07
-661
38
6,870
48
9,362
48
7,858
45
8,423
51
7,262
58
5,836
38
7,363
30
7,789
31
7,158
37
6,366
28
5,567
1,365
1,647
1,475
1,559
1,834
1,301
1,825
1,443
1,132
1,704
1,072
565
516
565
626
300
446
806
906
999
739
791
177
194
159
214
158
153
255
237
413
196
154
59
135
87
72
100
82
72
28
76
79
51
224
302
259
138
221
301
346
287
162
291
154
2,454
2,794
2,545
2,609
2,614
2,282
3,304
2,902
2,782
3,009
2,222
*Median for all farm-years where 1t to <2t of concentrates were fed per cow.
**Values above median that result in indirect or direct increases in milk EBITD per cow if other paths to milk EBITD per cow are unchanged are in blue font;
others are in black font.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
27
28
Pasture, milking area fodder
and irrig. costs per ha
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Pasture and milking
area fodder cost per t
DM
% diet as pasture and milking area fodder
Purchased fodder costs per t DM
% diet as purchased fodder
Other feed costs per t DM
Fat%
Milking area fodder
intake per cow
% diet as other feed
% diet as fodder
Pasture
intake in situ
per hectare
Calving
system
Year
Protein%
Stocking rate
(cows per ha)
Pasture utilised
(t per ha)
% diet as pasture
% milking area irrigated
% milking area regrassed
Fertiliser costs per ha
Irrigation volume per irrig. ha
Winter grazing rotation
Autumn grazing rotation
Summer grazing rotation
Spring grazing rotation
Key approach to pasture
management
Land and impr. value per ha
Manager’s years of
experience feeding
concentrates
Concentrate
costs per t DM
Feed costs
per cow
Cows per
FTE38
% of milker
area irrigated
Annual salary/wages
plus on-costs per
FTE38
Labour and mgt. costs per cow
Herd costs per cow
Shed costs per cow
Overhead costs
per cow
Milk EBITD
per cow
Amount fodder conserved
Mature cow animal
on farm per cow
health costs per cow Cows milked per Total non-pasture
operator-hour
feed intake per cow
Replacement feed costs per cow
Proportion of FTE38s unpaid
Feed costs
per t DM
% diet as concentrates
Diet ME
content
Feed
conversion
efficiency
Milk yield
(l/cow)
Concentrate intake per cow
Total feed
intake per
cow
Predominant
breed
Pasture
intake in
situ per
cow
Other feed intake per cow
Purch. fodder intake per cow
Milk price
(c/l)
Manufacturer
Figure 28: Null path model of milk EBITD per cow.
Table 6: Proportions of 15 farm years with highest and 15 farm years with lowest milk EBITD per cow* that had values for each
profit determinant more than 10% better and more than 10% worse than median.
Profit determinant
Highest milk EBITD per
cow farm years (n=15)
Milk price (¢/litre)
Lowest milk EBITD per
cow farm years (n=15)
>10% better
than median
>10% worse
than median
>10% better
than median
>10% worse
than median
100%
0%
0%
60%
Milk yield (litres per cow)
60%
13%
20%
40%
Feed costs per cow
13%
53%
47%
27%
Total feed intake per cow
13%
7%
7%
33%
Feed conversion efficiency
33%
0%
7%
20%
Feed conversion efficiency above maintenance
33%
7%
13%
13%
Dietary ME
0%
0%
0%
0%
Feed costs per t DM
7%
27%
33%
27%
Concentrate costs per t DM
7%
40%
40%
7%
Pasture and milking area fodder cost per t DM
20%
47%
20%
67%
Pasture, milking area fodder and irrig. costs per hectare
20%
53%
47%
47%
Pasture utilised per hectare
40%
13%
27%
53%
Stocking rate
27%
13%
40%
53%
Purchased fodder costs per t DM
60%
13%
20%
73%
Labour and management costs per cow
33%
20%
7%
80%
Cows per FTE38
40%
20%
7%
80%
Herd costs per cow
47%
40%
27%
53%
Shed costs per cow
13%
67%
40%
60%
Overhead costs per cow
33%
40%
27%
53%
Total costs per cow
7%
40%
7%
60%
*The five farm years with highest and five farm years with lowest milk EBITD per cow were selected within each concentrate feeding category.
Across all 30 farm years, the following profit
determinants generally deviated from median by 10%
or more in expected directions for both high and low
profitability farm years: milk price, milk yield (litres per
cow), pasture utilised per hectare, purchased fodder
costs per tonne DM, labour and management costs per
cow, and cows per 38-hour full-time equivalent (FTE38).
However, exceptions to these patterns were common.
In addition, pasture and milking area fodder cost per
tonne DM deviated from median by 10% or more in
expected directions for low profitability farm years but
not for high profitability farm years.
These findings emphasise the need to assess all direct
determinants of profitability collectively when analysing
farm performance, rather than assessing performance
against achievable or target values separately for
individual indices.
What this means for indus
try
Dairy farms are complex sys
tems and there are many
ways to make a profit (or a
loss). Determinants of
profit should be assessed
collectively, not separately.
Farms that have consistent
ly higher profits usually
have a relatively higher milk
price, higher milk yield
per cow, lower fodder cos
ts, and lower labour and
management costs. Howe
ver, they tend not to be
outstanding performers for
any of these particular
determinants of profit – the
y tend to be consistent allrounders whose efforts for
these profit determinants
collectively are superior.
See Appendix on page 54 for John Morton’s six
principles for identifying the determinants of profitability
by analysing data from groups of dairy farms.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
29
Change in profit due to
management changes
What we found
Changes in milk EBITD per cow
What we did
Changes in milk EBITD per cow from 2006/07 to
2007/08 were explored using 65 farms enrolled in both
2006/07 and 2007/08.
What we found
The changes are shown in Figure 29. As milk prices
increased substantially, milk EBITD per cow increases
were expected and this occurred on most farms. On 61
of these 65 farms (94%), milk EBITD per cow increased
by $100 or more. Farms feeding moderate-high levels
of concentrates tended to achieve higher increases in
profit than the low concentrate feeding category.
Causes of changes in milk EBITD per cow
What we did
1,500
1,000
In summary, these results indicate that the increases
in milk EBITD per cow in 2007/08 were largely due to
increased milk price on most farms, partly negated by
substantially increased operating costs (most commonly
due to increased feed costs per cow) and on some
farms, by reduced litres per cow.
51
12
2
20
39
24
31
19
45
29
42
22
30
10
28
43
21
14
67
9
500
0
1,000
2,000
0
Change in milk EBITD per cow ($ )
<1
1 to <2
>=2
Figure 29: Distribution of changes in milk EBITD per
cow from 2006/07 to 2007/08 by concentrate feeding
category.
30
Feed costs per cow increased substantially on most
farms, typically by larger amounts on farms where 1t or
more of concentrates wee fed. Costs per cow for other
cost categories were typically small, although these
decreased substantially on some farms and increased
substantially on others.
−1,000
−500
Change in milk EBITD per cow ($ )
2,000
Changes in milk EBITD per cow from 2006/07 to
2007/08 were explored using novel but related
modelling methods based on a concept developed
by Agrilink FarmStats. This allowed components of
changes in milk EBITD per cow to be identified. These
were analysed separately for farms in each of three
concentrate feeding categories. See Figures 30, 31 and
32, and Table 7.
On most farms, increases in milk price made large
contributions to the changes in milk EBITD per cow.
These were typically larger in farms where 1t or more
of concentrates were fed. Changes in litres per cow
generally made only small contributions to changes
in milk EBITD per cow. Total operating costs per cow
increased on most farms, commonly by substantial
amounts. These increases were typically larger in farms
where 1t or more of concentrates were fed.
Figure 30: Changes in milk EBITD per cow from
2006/07 to 2007/08 due to increased milk price (dark
blue), changed litres per cow at 2007/08 milk price
(light blue) and changed total operating costs per cow
(orange) in farms where <1 t concentrates were fed
in 2006/07. Farms are listed in descending order of
actual increase in milk EBITD per cow. Farm number
is shown.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
58
64
59
52
68
53
33
5
1
36
65
41
18
25
55
56
23
8
6
61
16
54
34
15
62
40
35
47
38
63
32
7
66
49
48
46
44
27
17
3
4
37
57
50
13
−1,000
0
1,000
2,000
−1,000
Change in milk EBITD per cow ($ )
0
1,000
2,000
Change in milk EBITD per cow ($ )
Figure 31: Changes in milk EBITD per cow from
2006/07 to 2007/08 due to increased milk price
(dark blue), changed litres per cow at 2007/08
milk price (light blue) and changed total operating
costs per cow (orange) in farms where 1t to <2t
concentrates were fed in 2006/07. Farms are listed in
descending order of actual increase in milk EBITD per
cow. Farm number is shown.
Figure 32: Changes in milk EBITD per cow from
2006/07 to 2007/08 due to increased milk price
(dark blue), changed litres per cow at 2007/08 milk
price (light blue) and changed total operating costs
per cow (orange) in farms where ≥2t concentrates
were fed in 2006/07. Farms are listed in descending
order of actual increase in milk EBITD per cow. Farm
number is shown.
Expected changes in milk EBITD per cow
had no management changes been made
To explore impacts of any management changes,
expected changes in milk EBITD per cow from 2006/07
2007/08 were calculated had there been no changes
in feed or labour inputs, or in yields per cow. Expected
changes were based on observed changes in milk price,
pasture and milking area fodder costs per tonne DM,
concentrate costs per tonne DM, purchased fodder
costs per tonne DM and costs per FTE38. For example,
in farm 1, concentrate costs increased from 2006/07
What we found
Given the large increase in milk price from 2006/07 to
2007/08, milk EBITD per cow would be expected to have
increased without management changes if unit prices
of inputs did not increase. But it was likely that some
managers also made management changes over this time.
Table 7: Distributions of contributions to change in milk EBITD per cow ($) from 2006/07 to 2007/08.
Variable
All farms (n=65)
Concentrate feeding
category in 2006/07
<1t
(n=20)
1t to <2t
(n=24)
≥2t
(n=21)
Min
25 p’ile
Median
75 p’ile
Max
Median
Median
Median
Change due to increased milk price
173
681
1,011
1,310
1,946
641
1,196
1,265
Change due to changed litres per cow at 2007/08 milk price
-573
-106
76
183
1,627
11
133
102
Change due to changed total operating costs per cow
-1,147
-576
-330
163
191
-206
-451
-423
Change due to changed feed costs per cow
-1,094
-505
-334
-185
147
-183
-372
-442
Change due to changed labour and management costs per cow
-341
-21
15
89
540
9
46
10
Change due to changed herd costs per cow
-250
-48
-11
18
80
-5
-2
-21
Change due to changed shed costs per cow
-68
-17
-6
7
68
-6
-12
1
Change due to changed overhead costs per cow
-220
-44
6
22
137
-12
-9
15
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
31
1000
1500
0
500
1,000
500
Expected change in milk EBITD per cow ($)
−500
Actual minus expected change in
milk EBITD per cow ($ )
1500
1000
500
Actual change in milk EBITD per cow ($)
0
0
<1
>=2
Figure 33: Expected and actual changes in milk EBITD
per cow from 2006/07 to 2007/08 in farms where
<1t concentrates per cow (green squares),
1t to <2t (blue triangles), and ≥2t (gold diamonds)
were fed; expected changes were those expected had
there been no changes in feed or labour inputs, or in
yields per cow.
Figure 34: Distribution of differences between actual
and expected changes in milk EBITD per cow from
2006/07 to 2007/08 by concentrate feeding category;
expected changes were those expected had there
been no changes in feed or labour inputs, or in yields
per cow.
to 2007/08 by $129 per tonne DM. Had the amount
of concentrates fed in 2006/07 (1.33t DM) not been
changed in 2007/08, milk EBITD per cow would have
fallen by $172 (1.33*129) due to increased unit price of
concentrates. This method assumes that changes in
these unit costs did not result in altered milk yields.
on the majority of farms, the increase in milk EBITD per
cow in 2007/08 was similar to that expected had no
major management changes been made. Given the high
milk prices in 2007/08, it was likely that opportunities to
substantially increase farm profitability were not taken
on many farms.
What we found
The relationship between expected and actual change
is shown in Figure 33. For farms close to the yellow line,
the actual changes were similar to that expected given
changes in milk price, pasture and milking area fodder
costs per tonne DM, concentrate costs per tonne DM,
purchased fodder costs per tonne DM and costs per
FTE38. Management changes did not contribute to the
increased milk EBITD per cow in these farms. For farms
below the line, actual increases were less than expected
in the absence of management change, while for farms
above the line, actual increases were greater than
expected.
Distributions of differences between actual and
expected are shown in Figure 34. In about half of the
farms, milk EBITD per cow increased by more than
expected; actual increases were more than $200 per
cow above that expected in more than 20% of farms.
Distributions were similar across 2006/07 concentrate
feeding categories.
These findings indicate that, on some farms, milk EBITD
per cow increased by substantially more than expected
had no management changes been made. However,
32
1 to <2
As discussed on page 24, there is a need to better
understand farmer decision making in the face of
sudden substantial increases in milk price, and to
identify practical management strategies that can be
rapidly implemented to capitalise on this situation.
With an increasingly volatile operating environment
(climate and markets), dairy farmers do need to ‘make
hay while the sun shines’ in the good years to increase
equity and/or farm performance so they cope better
during more difficult years.
What this means fo
r industry
Most dairy farms do
not make large syste
mic
management change
s from year to year in
response
to changing market co
nditions.
It is critical that farms
have a stable and co
nfident
platform from which
to respond or change
. Change
from instability is often
eroded or unprofitable
.
Farmers need more
support in identifying
practical
management strategie
s that can be rapidly
implemented to take
greater advantage of
high milk
price years when the
y occur.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Effect of enterprise size on farm profit
What we did
Within each study year, we assessed milk EBITD per
cow for enterprises of different size. Enterprise size was
described as number of cows, number of hectares, and
litres of milk and kilogram milk solids produced for the
year. We assessed milk EBITD per cow in the same way.
Operating costs per cow were assessed for various
herd sizes.
Milk EBITD per cow and per hectare increased a little
with number of hectares in 2006/07 but not in 2007/08
or 2008/09.
Operating costs per cow and per hectare did decrease
with increased herd size but, again, these decreases
were small. The TasMilk60 study did not allow
conclusions to be drawn about whether larger herds
had lower operating costs through greater "buying
power" due to increased enterprise size nor whether
they had higher operating costs associated with their
greater size and scale involved.
What we found
Milk EBITD per cow and per hectare increased with herd
size, litres of milk and kilogram milk solids in 2006/07
and 2007/08. However these increases were relatively
small and no such increases occurred in 2008/09. For
example, in 2006/07 milk EBITD per cow increased by
$72 for every extra 100 cows. However, milk EBITD per
cow in that year varied from -$660 to $757, a range of
$1,417, so an increase of $72 for every extra 100 cows
was quite minor.
What this means fo
r industry
Beneficial effects on
farm profitability of inc
reasing
enterprise size are ge
nerally relatively small.
Any beneficial effects
of 'dilution' of costs
per cow
with more cows are
not large, due to the
rel
atively
low levels of fixed co
sts available for diluti
on.
Increases in total farm
profitability with increa
sed
enterprise size are like
ly to be due to scale
(m
ore
cows / hectares / mi
lk), not efficiency (op
era
ting cost
per cow / hectare).
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
33
Degree of consistency of profit
outcomes achieved year to year
What we did
Consistency of milk EBITD per cow was assessed within
study farms between years. Fifty-six farms studied in all
three years were included in these analyses.
Consistency of relative profit outcome was assessed by:
• looking at each farm’s absolute profitability across the
three years; and
What we found
Both absolute and relative farm profitability were
markedly inconsistent between years, as illustrated in
the two ‘fiddle stick’ charts (Figures 35 and 36).
Correlations between milk EBITD per cow remained
weak within Fonterra suppliers. This indicates that
differences in milk prices between manufacturers did not
explain these weak correlations. Correlations between
consecutive years were closer on farms where less than
1t concentrates were fed, but even on these farms,
correlations were only moderately close.
• using deviation from within year medians and tertile
within year
Because milk EBITD per cow was so inconsistent within
farms between years, it was also important to assess
milk EBITD per cow cumulatively over the three study
years for each farm. Of particular interest were the
medians and upper values achieved within concentrate
feeding categories, and the extent of variability within
each category. Three-year cumulative milk EBITD per
cow values were calculated as the unweighted sum of
milk EBITD per cow in each of the three study years.
$
2000
2000
1500
1500
1000
1000
500
$ 500
0
0
-500
-500
-1000
06/07
07/08
-1000
07/08
08/09
Figure 35: Line graphs of EBITD per cow in 2006/07, 2007/08 and 2008/09 for 56 farms studied in all three years;
each farm is depicted by the same line colour between both pairs of years.
34
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Table 8: Distribution of 56 farms by number of study years in
each of upper, mid and lower tertile of milk EBITD per cow
within year.
No. years in
No. farms
Upper
Mid
Lower
Expected*
Observed
3
0
0
2.1
7
2
1
0
6.2
4
2
0
1
6.2
4
6
1
2
0
6.2
1
1
1
12.4
10
1
0
2
6.2
4
0
3
0
2.1
1
0
2
1
6.2
11
0
1
2
6.2
3
0
0
3
2.1
6
These findings indicate that, across years with widely
varying milk prices, farm profitability, in both absolute
terms and relative to other farms in the same region,
is highly inconsistent and demonstrates that high (or
low) relative farm profitability should not be viewed
as a repeatable attribute when milk price fluctuates
substantially.
1,500
1,500
1,000
1,000
500
500
$ diffference from median
$ diffference from median
*Expected numbers under random variation, i.e. if each farm’s
tertile in each year was determined randomly.
As shown in Table 8, deviations from within year
medians changed substantially between years. Of
the 19 farms initially in the highest tertile for that year,
only seven (37%) were in the highest tertile in both
subsequent years. Of the 19 farms initially in the lowest
tertile, only six (32%) remained in the lowest tertile in
both subsequent years. Almost two-thirds of the 56
farms were in the top tertile in at least one study year,
but only seven (13%) were consistently high (in the
upper tertile in all three years) and only six (11%) were
consistently low. Eighteen per cent of farms (10/56) had
one year in each of the lower, mid and upper tertiles.
0
-500
0
-500
-1,000
-1,000
-1,500
-1,500
06/07
07/08
07/08
08/09
Figure 36: Line graphs of milk EBITD per cow deviation from median within year in 2006/07, 2007/08 and 2008/09
for 56 farms studied in all three years; each farm is depicted by the same line colour between pairs of years.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
35
2,000
1,000
What we did
0
Consistency of milk EBITD per cow between years was
assessed by concentrate feeding category. Selected
indirect and direct profit determinants of milk EBITD
per cow were assessed in the seven consistently high
relative profitability farms and the six farms that had
consistently low relative profitability.
−1,000
3−year cumulative milk EBITD per cow ($ )
3,000
Determinants of consistent relative
profit performance over the three
study years
<1t
1t to <2t
>=2t
Figure 37: Distribution of three-year cumulative milk
EBITD per cow by amount of concentrates fed for all
three study years.
Medians were similar in each concentrate feeding
category (Figure 37), indicating that, among farms that
did not change concentrate feeding categories during
the study, no category had generally higher three-year
cumulative milk EBITD per cow. Variances were also
similar in each concentrate feeding category.
s for industry
What this mean
) is not very
tability (high or low
ar.
Relative farm profi
te widely year to ye
milk prices fluctua
repeatable when
ing level or
concentrate feed
or
There is no ‘best’
concentrate level
ing system. Any
y
an
in
production / feed
e
profitabl
ing system can be
production / feed
agement, milk
an
m
te mix of
ria
op
pr
ap
an
n
ve
year gi
sts.
price and input co
What we found
Among these consistent relative profitability farms, high
profitability was due in part to consistent relatively high
milk price, milk yield per cow, and consistent relatively
low pasture and milking area fodder cost per tonne DM
and purchased fodder costs per tonne DM. Lower labour
and management costs per cow also contributed to high
profitability among consistent relative profitability farms,
due in part, to more cows per FTE38. Total costs per
cow were consistently low more often among the high
profitability farms.
Importantly, no consistently high relative profitability
farm was more than 10% better than median in all
three years for all determinants of profitability. This
emphasises the need to assess all key direct and
indirect determinants of profitability collectively when
analysing farm performance, rather than assessing
performance against achievable or target values
separately for individual indices.
Note that because these findings are based on
small numbers of farms, they should be regarded as
preliminary, and require confirmation in larger studies.
Given the infrequency of consistent relative profitability,
a case-control design may be an efficient study design
to identify determinants of consistent relative profitability
across years.
What this me
ans for indus
try
Farmers that ha
ve consistently
higher profits
not to be outs
tend
tanding perform
er
s for any partic
determinants
ular
of profit, but ar
e consistent al
l-rounders.
Determinants
of profit should
be assessed co
not separately,
llectively,
when analysin
g farm perform
ance.
36
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Correlation between farm
profitability and ‘farming style’
What we did
To explore the question ‘Do decision makers in more
profitable dairy farm businesses tend to have a particular
management style or set of attitudes and beliefs?’ the
Derived Attitudinal Farmer Segmentation (DAFS) method
was used. This method was developed by Thomson
(2001), and selected by Waters, Thomson and Nettle
(2006) as the preferred segmentation method for dairy
farmers. It segments farmers on the basis of their
perceptions of a wide range of situational and individual
characteristics and has been successful in explaining
patterns in a wide range of behaviours across industries
and geographic locations.
Dairy Australia has used the method to characterise
‘farming styles’ to help the industry develop more
effective and targeted extension and communication
strategies within the different segments of the dairy farm
sector. The Client Stocktake project, completed in 2009,
involved a telephone survey of 450 dairy farmers from
around Australia using a survey form of 35 attitudinal
statements, each of which respondents were asked to
indicate the extent to which they agreed or disagreed.
Six DAFS groups were derived from a K-Means
clustering process using the farmers’ responses to
these attitudinal statements. Eight indices were used
to interpret the attitudinal profile of these groups, these
indices being ‘Business orientation’, ‘Aversion to risk’,
‘Sustainable improvement’, ‘Knowledge & self-reliance’,
‘Inter-generational orientation’, ‘The dairy way of life’,
‘Financial pressure’ and ‘Farming tradition’.
Though useful in its own right, the Client Stocktake
survey collected no hard data about the physical and
financial performance of each respondent’s dairy farm
business, so the ‘farm styles’ could not be aligned to
a farm’s commercial standing or farming practices.
The TasMilk60 study offered the opportunity to conduct
a DAFS study about a group of farmers for whom their
farm business’s physical and financial performance
was known.
During the data collection phase, it was a 10-minute
addition to have participating farmers record their
opinions about the extent to which they agreed or
disagreed with the 40 statements.
Sixty people from 33 TasMilk60 farm businesses
completed a valid survey form. These data were passed
to Warwick Waters (Waters Consulting) and Don
Thomson (Landscape and Social Research) for analysis.
What we found
Primary decision maker respondents on the TasMilk60
farms were most commonly in one of three DAFS
groups (‘Family first’, ‘Open to change’ and ‘Growing for
the kids’). The distribution of respondents was similar to
that for the sample of 450 Australian dairy farmers who
participated in the Client Stocktake project, except the
percentage in Group 4 (‘Established and stable’), which
contained fewer respondents.
Neither DAFS group nor attitudinal indices varied
markedly by concentrate feeding category, indicating that
high profitability occurred on farms with primary decision
makers with diverse attitudes and farming styles.
Table 9: Distribution of Derived Attitudinal Farmer
Segmentation group of primary decision maker by
concentrate feeding category in 2006/07 on 32 farms.
DAFS group
<1t
1t to <2t
≥2t
Family first (Group 1)
43% (3)
17% (2)
8% (1)
Love farming (Group 3)
8% (1)
8% (1)
Established and stable
(Group 4)
17% (2)
Winding down (Group 2)
15% (2)
Open to change (Group 5)
14% (1)
42% (5)
31% (4)
Growing for the kids
(Group 6)
43% (3)
17% (2)
38% (5)
Total
100% (7)
100% (12)
100% (13)
dustry
What this means for in
style or
a certain management
Farmers don’t require
ble.
liefs to be highly profita
set of attitudes and be
The original DAFS survey form of 35 statements was
expanded to 40, with the added statements geared
around cow nutrition, feeding, risk and wealth creation.
Note: During the analysis of the DAFS survey data, a weakness in method was identified – the necessity of nominating the “primary decision maker” from within
a farming family. By default, since “there can be only one”, the process tends to assign this role to the senior male of the family, even when it was a fact known
to the survey team that husband and wife were joint decision makers with different knowledge and expertise.
If the DAFS method is to become more reliable and accurate, means must be found to integrate the views of the members of a farming family’s decision-making
team. Sons, daughters, brothers and sisters may all be part of this team as well, each taking primary responsibility for making decisions related to different
aspects of the farm business, e.g. pasture management, finances, herd husbandry.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
37
38
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 4
Risk
– Insights from TasMilk60
This section describes findings from the TasMilk60 study about risk, and explains what these mean for
industry. The three year spread of data across a wide range of feeding approaches within the TasMilk60
group of farms lent itself to a long-overdue objective examination of risk, risk management and risk measures.
Risk and uncertainty
Risk managment
Risk and uncertainty are central to all dairy farming
decisions. Malcolm (2009) offers this view, “Imagining
the future with rigour means adopting the structured
approaches to decisions developed in modern decision
theory that emphasises probability analyses and
alternative futures, with sound appreciation of the
pervasiveness of uncertainty”.
Attempting to avoid risks is often futile or counterproductive. Instead, risk exposure can be reduced by
spreading, selling or shifting risk, or by risk averaging.
While there is broad agreement about the nature of the
risks facing dairy farmers (production, price or market,
financial, institutional and human), farmers themselves
define risk in several different ways:
1. The probability of an adverse outcome or financial
loss.
2. The size of the loss if it were to occur.
3. The expected value of a potential loss
(i.e. 1 multiplied by 2).
Many of the risks in farming are inter-dependent.
Individual risks may correlate with other risks positively
or negatively, e.g. weather and fodder prices. Farming
systems are most severely tested by the relatively rare
but most severe circumstances that may occur when
drought (low yields) coincides with high feed costs, low
milk prices and high interest rates.
Statistical approaches have been used to define risk as
equal to the variance of the probability distribution of all
possible consequences of a risky course of action.
In a farming systems context, risk can be defined as
the variability of the profit outcomes attributed to that
system given a range of values of influential parameters
over time. Uncertainty is characterised by lack of
complete information about an event, such as the
probabilities of frequency, size, severity and duration.
For the dairy farmer, there is always a trade-off between
risk and reward. Those with a low risk tolerance will
seek options where little risk is involved and will require
a very high reward for the risk involved. Those with a
higher tolerance for risk will be willing to accept risk
without such a big potential reward.
What constitutes an acceptable risk-reward trade-off
is also coloured by the farmer’s perception of several
factors – familiarity, control, catastrophic potential (‘dread
factor’), equity and level of knowledge (Slovic 2000).
For all production/feeding systems, there is a mix of
risk, performance and management principles whose
understanding can improve the chances of successfully
balancing risk and reward.
Since the risk environment faced by farmers is not static
or fixed, risk management is, “… a continuous adaptive
process that needs to be integrated into all relevant
aspects of the decision making procedures of the farm”
(Hardaker et al. 1997).
Topp and Shafron (2006), in their comprehensive review
of risk, note that, “Where a farmer can gain control or
at least strongly influence his risk-reward trade-off, he
may take the opportunity to spread risk by diversifying,
selling or shifting risk or risk-averaging. Where a higher
than average risk in one aspect can be offset by a lower
than average risk, the resulting combined risk may mean
less overall risk exposure”.
Against the background of a decade of major market
and climatic volatility, the dairy farm sector has not yet
rigorously accommodated risk and uncertainty into its
decision-making and profit-planning process, despite
sound warnings of the logical consequences being
delivered at the start of that decade.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
39
Malcolm (2001) noted that, “Another misplaced
emphasis in farm management economics has been the
traditional emphasis on risk/volatility as something to
be minimized. This has been at the expense of a more
realistic emphasis that would have been on risk/volatility
not only as something farmers manage live with, but
equally important, risk/volatility as a major source of the
opportunities for management and of the opportunities to
prosper and grow”.
Risk measures
To summarise, uncertainty characterizes the decisionmaking environment that exists when a farmer decision
maker does not have perfect information; and risk is
the economic consequence on the welfare of the farm
business as a result of this uncertainty.
Risk management is the process by which the
probability of unfavourable outcomes is reduced and
favourable outcomes increased through guiding the
farmer decision maker to make timely choices among
alternative options.
Risk measures should provide the probability of
unfavourable and favourable outcomes occuring.
Unfortunately, many so-called risk measures currently
used in the dairy industry do not provide information
about risk and its consequences.
These erroneous risk measures include:
• Pasture as a % of total feed consumed
• Unit cost of milk production (¢ per litre or $/kg MS)
• Operating profit margin
Pasture as a % of total feed consumed is a technical
measure of the herd feedbase, not a risk measure,
and is biased towards feeding system 1 (low bail). It
incorrectly assumes that a higher EBITD and return on
assets (ROA) is positively correlated to higher % pasture
in the cow’s diet, and that increasing pasture’s share
of the diet must therefore be a means of lowering risk.
These assumptions are unsound and are not supported
by the TasMilk60 dataset.
Unit cost of milk production is an economic measure,
not a risk measure, and provides no information about
the probability of adverse cost events or enterprise
profitability. The TasMilk60 data show that achieving
a low unit cost has no effect on the risk exposure of
the farm business at all. Since operating at the point
of lowest unit cost does not maximise profits, farmers
making risk-reducing decisions based upon the unit
cost “risk” measure may expose their farm business to
increased risks through lost opportunity.
40
Operating profit margin is also an economic measure,
not a risk measure, and provides no information about
the probability and repeatability of particular enterprise
profit outcomes, as TasMilk60 has demonstrated.
Commercial reality has higher rewards associated with
higher risk, so it is illogical to assume that achieving
higher dairy profits automatically results in lower risk
exposure. Furthermore, profitable farmers who choose
to apply surplus profit to debt reduction may reduce
their exposure to financial risk but profitable farmers
who choose instead to buy a boat do not. Therefore,
risk reduction hinges on the spending decision, not on
the level of profitability achieved.
Many technical, financial and economic measures
provide information about financial efficiency, liquidity,
solvency or repayment capacity but are not risk
measures. For well-established definitions of these
terms, refer to those provided on the USA Farm
Financial Standards Council website http://www.ffsc.
org/Files/FarmFinancialGuidelinesRatios.pdf
The TasMilk60 study has shown that the dairy industry
needs to re-examine its use of risk measures as part of
a new approach to risk and risk management in dairy
farm decision making.
Monte Carlo simulation study
using TasMilk60 data
What we did
As part of the TasMilk60 study, Courtney Gronow (AGFS),
Gordon Cleary (AGFS) and Steve Little (Dairy Australia)
considered the different qualitative, semi-quantitative
and quantitative methods of assessing risk that could
be applied to the three years of physical and financial
performance data collected from the TasMilk60 farms.
Gronow, Cleary and Little chose to pursue a quantitative
analysis because it assigns specific values and
probabilities to risks. However, they were also mindful
of three limitations which apply even to quantitative risk
analysis, as highlighted by Hardaker and Lien (2007):
1. There is a common failure to deal with the decision
maker’s risk aversion.
2. There is too strong a focus on downside risk.
3. There is too little attention to the place of individual
risks in the total risk portfolio.
To address these three limitations, a quantitative method
is required which considers both the downside and
upside consequences of risks, calculates probability
distributions for operating profits and accounts for risk
aversion through use of certainty probabilities.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
A probabilistic budgeting approach was therefore
applied in the TasMilk60 study. Agrilink FarmStats
used Crystal Ball® (Oracle Corporation, UK) to carry
out Monte Carlo simulations on an integrated dairy
biophysical and financial model (CUD,® Cowdata Pty
Ltd, Australia) and generate profit distributions for three
model farms (A, B and C), respectively feeding a low,
moderate or high level of grain-based concentrate.
The fixed inputs and the variable inputs (each with
minimum, maximum and most likely values) used in these
simulations (Table 10 and Figures 38a, b and c) were
drawn from the physical and financial data from the
TasMilk60 farms in each of the three feeding categories
for the three-year period 2006/07 to 2008/09.
The Monte Carlo simulations generated profit
distribution curves for the three model farms, which
enabled comparison of the variability of farm EBITD
and showed the probability of achieving with certainty
a forecast EBITD amount. A target EBITD value of
$200,000, nominally capable of supporting personal
drawings and debt servicing, was selected to test the
probability that each feeding system could achieve the
target sum with certainty in each of the three years.
The simulation also generated Tornado charts that
enabled the influence of the key profit drivers on farm
EBITD to be compared in each case and across the
three concentrate feeding levels.
What we found
Milk price was found to have a greater impact on profit
variability at higher levels of concentrate feeding.
Based on general discussions with farmers during Year
2 of the study (2007/08), when the grain price peaked, it
is also likely that many farmers, especially those feeding
low levels of concentrates, perceive grain/concentrate
price to be a more important profit driver than it really
is for their farm business, and therefore are inclined to
over-react to changes in grain/concentrate price relative
to other profit drivers.
The full results of this simulation work have been reported
in ‘Managing production system risk: An application of
Monte Carlo simulation to Tasmanian dairy farm data’,
by C.S. Gronow, S.B. Little and G.V. Cleary, in the
proceedings of the 2010 Australasian Dairy Science
Symposium held at Lincoln University in New Zealand.
Table 10: Fixed inputs used in Monte Carlo simulations for
each model farm.
Farm A
Farm B
Farm C
Concentrate feeding rate
Low
<1t
Moderate
1 to <2t
High
≥2t
Effective area (ha)
180
165
177
Figures 38a, b and c show the profit distribution curves
and Tornado charts generated for the three model farms
(A, B and C), respectively feeding a low, moderate or
high level of concentrate in 2006/07.
Herd size
393
402
420
Stocking rate (cows/ ha)
2.18
2.44
2.37
Base case milk yield (L/cow)
4,271
6,400
8,421
Base case (milk solids /cow)
333
478
608
The profit distribution curves showed that feeding low
levels of concentrates tended to result in less profit
variability but the lowest mean profit.
Fat percentage
4.4%
4.1%
3.9%
Protein percentage
3.4%
3.4%
3.4%
Milk solids percentage
7.8%
7.5%
7.2%
Based on the Tornado charts for each model farm,
on-farm profit drivers of pasture utilisation, pasture
quality and core cost had a greater impact on profit
variability than did the off-farm profit drivers of milk
price, concentrate price and purchased fodder price.
Bodyweight (kg)
479
529
579
Feed Conversion Efficiency
0.92
1.12
1.26
Protein to fat ratio
0.76
0.83
0.87
ECM
4484
6452
8247
ECM FCE
0.96
1.13
1.23
Total feed
4.66
5.71
6.68
Concentrates
0.47
1.47
2.31
Body condition change
0.25
0.25
0.25
Weather effect
10%
10%
10%
Rumen fill
100%
100%
100%
Min. fodder
0.2
0.2
0.2
Home grown fodder
0.28
0.42
0.52
Other feeds
0.12
0.12
0.12
Fodder efficiency
80%
80%
80%
Pasture management was confirmed as a key profit
driver in all pasture-based feeding systems, regardless
of the level of concentrates fed (low, moderate or high).
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
41
Farm A (low concentrate feeding level) 2006/07
Profit distribution
curve
➔
Mean EBITD
= $110,948
12% chance of $200,000 EBITD
Variable inputs
used in simulations
for model farm A:
Variable
Pasture MJ ME/ kgDM
Pasture utilised (t DM /Ha)
Core Cost Per Cow
Milk Price ($/kgMS)
Pasture NDF Quality
Concentrates $ / t DM
Purchased Fodder $/t DM
Input
Downside Upside Base Case
11.2
10.5
12.2
8.90
6.41
11.56
$1,055
$889
$1,221
$4.46
$4.07
$4.85
45%
43%
48%
$425
$339
$511
$147
$93
$222
Tornado chart
$134,362 $121,679
$12,683
Farm EBITD
-$100,000
$0
Pasture MJ ME/
kgDM
Pasture utilised (t
DM /Ha)
Core Cost Per Cow
$100,000
$200,000
10.5
12.2
6.41
11.56
$1,221
Milk Price ($/kgMS)
$4.07
Pasture NDF Quality
48%
Concentrates $ / t
DM
Purchased Fodder
$/t DM
$889
$4.85
43%
$511
$222
Figure 38a: Monte Carlo simulation for low concentrate farm in 2006/07.
42
Farm EBITD
Downside Upside
Range
$64,402 $221,751 $157,348
-$11,967 $128,984 $140,952
$194,388
$63,581 $130,806
$77,086 $180,882 $103,796
$174,980
$78,402 $96,579
$144,843 $113,126 $31,718
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
$339
$93
$300,000
Farm B (moderate concentrate feeding level) 2006/07
Profit distribution
curve
➔
Mean EBITD
= $145,326
30% chance of $200,000 EBITD
Variable inputs
used in simulations
for model farm B:
Tornado chart
Input
Variable
Pasture utilised (t DM /Ha)
Core Cost Per Cow
Milk Price ($/kgMS)
Pasture MJ ME/ kgDM
Concentrates $ / t DM
Pasture NDF Quality
Purchased Fodder $/t DM
Farm EBITD
Downside Upside Base Case Downside Upside
Range
11.56
6.41
8.90
-$12,624 $205,085 $217,709
$1,524
$1,108
$1,316
$234,672 $67,768 $166,903
$4.85
$4.07
$4.46
$73,450 $228,990 $155,541
12.2
10.5
11.2
$89,644 $239,668 $150,025
$511
$339
$425
$201,957 $100,483 $101,474
48%
43%
45%
$170,927 $127,398 $43,529
$222
$93
$147
$163,938 $133,944 $29,994
Farm EBITD
-$100,000
Pasture utilised (t
DM /Ha)
Core Cost Per Cow
Milk Price ($/kgMS)
Pasture MJ ME/
kgDM
Concentrates $ / t
DM
Pasture NDF Quality
Purchased Fodder
$/t DM
$0
$100,000
$200,000
6.41
$300,000
$400,000
11.56
$1,524
$1,108
$4.07
$4.85
10.5
12.2
$339
$511
48%
$222
43%
$93
Figure 38b: Monte Carlo simulation for moderate concentrate farm in 2006/07.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
43
Farm C (high concentrate feeding level) 2006/07
Profit distribution
curve
➔
Mean EBITD
= $160,210
36% chance of $200,000 EBITD
Variable inputs
used in simulations
for model farm C:
Variable
Pasture utilised (t DM /Ha)
Milk Price ($/kgMS)
Core Cost Per Cow
Concentrates $ / t DM
Pasture MJ ME/ kgDM
Pasture NDF Quality
Purchased Fodder $/t DM
Downside
6.41
$4.07
$1,321
$339
10.5
43%
$93
Input
Upside Base Case
8.90
11.56
$4.46
$4.85
$1,569
$1,817
$425
$511
11.2
12.2
45%
48%
$147
$222
Farm EBITD
Downside Upside
Range
-$13,225 $226,530 $239,755
$58,883 $267,048 $208,164
$266,915 $59,015 $207,900
$246,265 $79,666 $166,599
$98,430 $255,664 $157,234
$183,652 $137,960 $45,692
$177,208 $143,617 $33,591
Farm EBITD
Tornado chart
-$100,000
Pasture utilised (t
DM /Ha)
$0
$100,000
$200,000
6.41
11.56
Milk Price ($/kgMS)
$4.07
$4.85
Core Cost Per Cow
$1,817
$1,321
Concentrates $ / t
DM
Pasture MJ ME/
kgDM
Pasture NDF Quality
Purchased Fodder
$/t DM
$511
$339
10.5
12.2
48%
$222
43%
$93
Figure 38c: Monte Carlo simulation for high concentrate farm in 2006/07.
44
$300,000
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
$400,000
Where to from here?
The dairy sector must accept that for all feeding
systems there is a mix of risk, performance and
management principles whose understanding can
improve the chances of success.
The traditional and outdated mindset is that risk is to be
avoided. (For example, if you’re worried about the rising
cost of grain / concentrates, not using that feed type
completely avoids exposure to price risk). This needs
to be replaced by the concept of risk-swapping as a
far more practical solution to risk mitigation than a futile
attempt to avoid risk.
In conjunction with other stakeholders, particularly the
banks, the dairy sector needs to agree on standard risk
measures and to explore new ways (or old ways newly
used) to quantitatively assess risk so that coping with
risk can be better handled and appreciated.
Extension messages to farmers about risk need to be
sound, consistent and unambiguous. Risk should not be
used inappropriately by champions of one production /
feeding approach to argue that theirs is better or lessrisky than another.
The TasMilk60 study has demonstrated how a
probabilistic budgeting approach to risk analysis can
be useful at an industry level in comparing different
farm production / feeding systems on the basis of their
probability distributions of operating profit, rather than
on single values of operating profit for each system
(as discussed by Chapman et al. 2007).
However, through the TasMilk60 study, Dairy Australia’s
Grains2Milk program and Agrilink FarmStats have also
recognised the value a probabilistic budgeting approach
may also provide at the individual farm level in the
annual forward planning process used by dairy farmers.
The conventional annual forward planning process
usually begins with an annual budget. Single, “mostlikely” unit values for key financial drivers such as
milk price and supplementary feed costs and for key
biophysical drivers such as milk yield and concentrate
and fodder feeding levels are selected and used to
derive key income and cost values. Prior year spending
on non-feed operating costs and debt-servicing costs
(if any) is used as a guide to plan spending in the
upcoming year(s).
For many, if the budget result is a surplus, that is the
end of the planning process. Others extend the process
to include a sensitivity analysis, often on milk price
alone, but sometimes including selected costs such as
grain or fertiliser. However, sensitivity analyses that place
equal weight on the outcomes generated by pairing
variable input measures (e.g. milk price and concentrate
price) deny reality, which is that certain combinations
of input values have a higher or lower likelihood of
occurring in the farmer’s real world. Unfortunately,
despite this methodological weakness, many dairy farm
businesses rely on this approach to prepare for the
shock of the unknown and unpredicted.
Such an annual forward planning process can lead
farmers to make decisions that do not use all the best
information available (Malcolm et. al. 2005). If increased
market and climatic volatility are now a constant in
the dairy farmer’s world, such sub-optimal planning
procedures could have high real costs and avoidable
negative impacts in rural communities.
As an extension of the TasMilk60 study, Dairy Australia’s
Grains2Milk program has commissioned Agrilink
FarmStats to undertake a pilot study to assess the
benefits of an alpha version of a new probablistic risk
assessment tool for farmers dubbed ‘Risk Tamer’.
At the time of writing this booklet, the pilot study was
still in progress.
What this means for industry
Risk and uncertainty rule all dairy farming
decisions. Attempting to avoid risks is often futile
or counterproductive. Instead, risk exposure can be
reduced by spreading, selling or shifting risk, or by risk
averaging.
For all feeding systems there is a mix of risk,
performance and management principles whose
understanding can improve the chances of
successfully balancing risk and reward.
Pasture utilisation, pasture quality and core costs per
cow are key profit drivers in all pasture-based feeding
systems, regardless of the level of concentrates fed.
Milk price has a greater impact on profit variability at
higher levels of concentrate feeding.
Farmers are inclined to over-react to changes in
grain/concentrate price.
A farmer has more control over risk than first meets
the eye.
One of the Australian dairy industry’s strengths is
the diverse range of farmers and production/feeding
systems used – we should celebrate this rather than
promote or defend particular systems!
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
45
46
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 5
Other uses of TasMilk60 data to date
As previously described, Grains2Milk’s TasMilk60 dataset has been used to:
• estimate the financial value of incremental change in feed conversion efficiency (see page 20) ;
• explore whether more profitable farmers tend to have a particular farming style (see page 37); and
• assess risk using a probabilistic budgeting approach (page 40).
In addition to these, the TasMilk60 farm dataset has also been used in two other ways:
DairyMod pasture study
(The University of Melbourne)
What was done
• animal physiology and production; and
Brendan Cullen, Institute of Land and Food Resources,
The University of Melbourne, carried out a range of
simulations using DairyMod, with a view to establishing
the degree of difference between actual pasture growth
and non-limited potential pasture growth across the
TasMilk60 farms. These simulations used data from 35
TasMilk60 farms for 2006/07 only.
• pasture management, irrigation and fertiliser
application.
DairyMod is a simulation model used for predicting
and understanding the consequences of weather and
management decisions on dairy grazing systems.
The model predicts outcomes according to:
• pasture growth and utilisation by dairy cows;
• water and nutrient dynamics;
Daily climate data is required for each model and details
about soil types, pasture species, fertiliser use, irrigation,
animal type and grazing systems must be specified.
The model produces annual summaries and daily data of
pasture and animal production, water balance and soil
nutrients. Figure 39 is an example of DairyMod output.
The method and main assumptions used in the
DairyMod simulations of the TasMilk60 farms were:
1. Climate data was obtained from Bureau of
Meterology DataDrill using latitude and longitude of
nearest town.
Pasture:
• biomass
• species composition
• quality
Livestock:
• animal intake
• liveweight and milk production
• stocking rate
Climate:
• rainfall
• temperature
Soil characteristics:
• moisture
• nitrogen
All paddocks
Farm summary
Figure 39: Sample DairyMod output.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
47
2. Soil type was specified by the farmer, with clay loam
as the default.
3. Herd size and calving pattern were specified by the
farmer.
4. All pastures were assumed to be perennial ryegrass/
white clover.
5. Farm area was as specified by the farmer, but
all were given 50 paddocks with an allocation to
dryland and irrigated on a pro rata basis.
6. Grazing management was the same on all farms, i.e.
graze at three-leaf stage to a residual of 1.4t/DM/ha.
7. The supplementary feeding rules used were the
same for all farms.
8. Lactation curves were the same for all farms.
9. N application – total N applied was split between
irrigated and dryland areas at a 3:1 ratio of kg/N/ha.
Timing and rate were defined as:
Dryland – generally one application in autumn and
2-3 in late winter/spring with individual application
rates up to 45kg/N/ha; and
Irrigated – generally 4-6 weeks between
applications, except none applied in June-July and
individual application rates of 30-60kg/N/ha.
Pasture growth rate (kg DM/ha.day)
140
Pasture growth rate (kg DM/ha.day)
140
120
100
◆
■
■
◆
40
20
Figure 40 shows the simulated outcomes for pasture
growth rates where water and N fertiliser were limited
(farm actual) and unlimited (optimal).
A geographical representation of the regional differences
in pasture growth and consumption across northern
Tasmania is given in Figure 41.
These data show that the predominantly irrigated
‘Easterners’ have a greater potential to increase
pasture growth rates than the pre-dominantly rain-fed
‘Westerners’, even though both groups have similar
single-digit pasture utilisation rates.
■
◆
◆
■
Mean +/- SE (t DM/ha)
Eaten: 9.72 +/- 0.86
Potential: 11.53 +/- 0.87
growth
■
■
◆
■
◆
Mean +/- SE (t DM/ha)
Eaten: 8.28 +/- 0.53
Potential: 15.66 +/- 0.84
growth
◆
■
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
▼
▼
◆ Farm N and water
120
100
◆
■
80
◆
■
60
■
◆
◆
■
■ Optimal N and water
■
◆
■
◆
40
■
■
◆
◆
■
■
◆
◆
■
◆
◆
■
0
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Figure 41: Geographical differences in pasture growth
and consumption across northern Tasmania.
Figure 40: Modelled pasture growth rates for farm
(actual) and optimal water and nitrogen fertiliser
applications.
48
Easterners (59% irrigated)
■
◆
◆
0
20
What was found
Westerners (17% irrigated)
■
◆
◆
■
60
If the farm applied less irrigation than the model
applied, pasture production was scaled back
according to the rule 1/ML/ha = 1t/DM/ha.
◆ Farm N and water
■ Optimal N and water
◆
■
80
10. Irrigation was applied in response to soil water
deficit of 40mm (calculated over top 50cm soil), with
water volume applied to fill to field capacity:
Irrigation season – 1 October to 30 April;
minimum of three days between irrigation events.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Greenhouse gas emissions study
What was found
(TIAR)
The study found that GHG production increases linearly
as farm milk production increases (Figure 43). However,
there is much to be discovered about the link between
GHG and milk production, particularly at the higher end
of the scale.
What was done
Karen Christie and Richard Rawnsley, TIAR climate
change researchers, examined the greenhouse gas
(GHG) emissions of the TasMilk60 farms using the Dairy
Greenhouse Gas Abatement Strategies calculator (DGAS)
and demonstrated differences in GHG emissions between
farms with different feeding approaches (Figure 42).
A paper about this study, A whole farm systems analysis
of the greenhouse gas emissions of 60 Tasmanian
dairy farms, co-authored by Karen Christie, Richard
Rawnsley and Richard Eckard, was presented at the 4th
International Greenhouse Gases and Animal Agriculture
Conference in Banff, Canada, in October 2010, and
will be published in a special issue of the Animal Feed
Science and Technology Journal in mid-2011.
10.0
4,000
8.0
3,000
6.0
2,000
4.0
1,000
0
Total farm intensity emissions (t CO2-e/t MS)
Total farm GHG emissions (t CO2-e)
Figure 42: GHG emissions and GHG per tonne milk
solids for TasMilk60 farms.
2.0
0.0
3,000
2,000
1,000
0
450,000
5,000
4,000
400,000
12.0
350,000
6,000
300,000
14.0
250,000
7,000
5,000
200,000
16.0
6,000
150,000
8,000
7,000
100,000
18.0
The TasMilk60 dataset has provided a useful insight into
how different feeding and stocking rate strategies can
affect GHG emissions at the herd level.
50,000
9,000
There is no strong relationship between pasture
utilisation (t DM/ha) and GHG emissions (Figure 47).
0
20.0
It is conjectured that these effects are a direct
consequence of some TasMilk60 herds choosing to rely
more on higher starch diets to lift production through
the use of grain/concentrates.
Total farm GHG emissions (t CO2-e)
10,000
GHG production per unit of MS produced declines as
feed conversion efficiency improves (Figure 44), as milk
yield per cow rises (Figure 45) and as the proportion of
concentrates in the cow’s diet increases (Figure 46).
Milksolids per farm
Figure 43: GHG emissions in relation to farm milk
production for TasMilk60 farms.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
49
20.0
18.0
19.0
16.0
18.0
GHG intensity (t CO2-e/t MS)
GHG emissions intensity (t CO2-e/t MS)
20.0
14.0
y = -0.1676x + 28.084
12.0
R2 = 0.5516
10.0
8.0
6.0
4.0
2.0
0.0
17.0
16.0
15.0
14.0
13.0
y = -0.0091x + 18.801
12.0
R2= 0.3894
11.0
50
55
60
65
70
75
80
85
90
95
10.0
200
100
250
300
350
400
FCE (kg MS/t DM intake)
20.0
20.0
19.0
19.0
18.0
18.0
17.0
16.0
15.0
14.0
13.0
12.0
5
10
650
700
y = -0.0609x + 14.931
R2= 0.0073
17.0
16.0
15.0
14.0
13.0
12.0
0
15
20
25
30
35
40
% concentrate in diet
Figure 46: GHG emissions in relation to percentage
concentrate in the diet for TasMilk60 farms.
50
600
10.0
R = 0.3545
2
0
550
11.0
y = -9.9911x + 16.691
10.0
500
Figure 45: GHG emissions in relation to milk yield per
cow, expressed as kg MS/cow for TasMilk60 farms.
GHG intensity (t CO2-e/t MS)
GHG intensity (t CO2e/t MS)
Figure 44: GHG emissions in relation to feed
conversion efficiency expressed as kg MS/t DM for
TasMilk60 farms.
11.0
450
kg MS/cow
45
2
4
6
8
10
12
14
16
18
Pasture consumption (t DM/ha)
Figure 47: GHG emissions in relation to pasture
utilisation, expressed as t DM/ha for TasMilk 60 farms.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 6
Conclusions
The findings from Grains2Milk’s TasMilk60 study support
the following conclusions:
1. Farm performance
Despite fears of substitution, there is no simple
relationship between amount of concentrate fed per
cow and pasture utilised per hectare. Both good
and poor pasture utilisation is seen at all levels of
concentrate feeding.
Farms that feed lower levels of concentrate per cow
get their cows to eat more pasture. However, there is
a larger variation in pasture intake per cow between
farms feeding the same level of concentrate than there
is between farms feeding different levels of concentrate,
so herd and grazing management are critical.
Milk protein to fat ratio is generally higher on farms
where higher rates of concentrate are fed. This is largely
due to reduced fat concentrations found in milk with
higher daily milk volumes per cow.
Feed conversion efficiency (FCE) is highly variable at
all concentrate feeding levels. It is generally better on
farms where higher amounts of concentrate per cow
are fed. This is largely due to the higher total intakes in
high concentrate-feeding farms and, hence, the greater
proportion of nutrients that are used for milk production
and the lesser proportion used for maintenance.
Total feed intake per cow explains more than half the
variability in FCE. However, there are also several other
important factors known to help optimise FCE, including
maintaining high feed quality and good rumen function,
and minimising feed gaps, feed wastage and energy
losses.
protein concentrations, and the seasonality of milk supply
patterns.
Farmers aiming to increase profitability by reducing
costs should review and monitor feed costs, but labour
and management costs, as these generally constitute
the highest proportions of operating costs and vary
widely between farms. Conversely, herd, shed and
overhead costs are relatively small components of
operating costs and are less variable between farms.
2. Farm profitability
In farm populations such as that examined by the
TasMilk60 study, milk EBITD per cow, hectare, litre, and
kilogram milk solids, and return on capital (based on
milk EBITD) are closely correlated with each other, so
are likely to tell the same story about farm profitability.
There is no ‘best’ concentrate feeding level or
production / feeding system. Any concentrate feeding
level or production / feeding system can be profitable in
any year, given an appropriate mix of management, milk
price and input costs.
The differences in average and median profits
achieved between farms using low, moderate and high
concentrate feeding levels are small compared with
the variability between farms within each concentrate
feeding level.
When a dairy farm changes its production / feeding
system, adjustments are needed (cows, people, farm
infrastructure) to realise the full benefits. It may take
several years before the benefits are fully realised.
Dairy farms are complex systems and there are many
ways to make a profit (or a loss). Determinants of
profit should be assessed collectively, not separately,
when analysing farm performance. Farms that have
consistently higher profits usually have a relatively higher
milk price, higher milk yield per cow, lower fodder costs
and lower labour and management costs. However,
they tend not to be outstanding performers for any of
these particular determinants of profit – they tend to be
consistent all-rounders whose efforts for these profit
determinants collectively are superior. Income from milk
sales is as important a component of profit as are costs
– higher feed costs may be justified if they generate
extra profit by lifting milk income.
Milk prices paid to farmers (even those supplying the
same manufacturer) vary widely due to on-farm factors
under their control, including milk volumes, milk fat and
Most farms maintain a ‘business as usual’ approach in
high milk price years and do not take opportunities to
substantially increase farm profit above what the milk
Increasing FCE within a farm’s chosen feeding system
can be a powerful lever for increasing farm profit,
particularly in higher milk price years.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
51
price alone delivers. Few farms make large systemic
management changes year to year.
Beneficial effects on farm profitability of increasing
enterprise size are generally relatively small and are likely
to be due to scale, not efficiency.
High or low relative farm profitability is not very
repeatable when milk prices fluctuate widely year to
year, so results in a single year may not reflect profit
performance over the longer term.
Farmers do not require a certain management style or
set of attitudes and beliefs to be highly profitable.
3. Risk
Risk and uncertainty are central to all dairy farming
decisions. Attempting to avoid all risk is often futile
or counter-productive. Instead, risk exposure can be
reduced by spreading, selling or shifting risk, or by
risk-averaging.
For the dairy farmer, there is always a trade-off between
risk and reward. Those with a low risk tolerance will
seek options where little risk is involved and require
a very high reward for the risk involved. Those with a
higher tolerance for risk will be willing to accept risk
without such a big potential reward.
52
For all production/feeding systems, there is a mix of
risk, performance and management principles whose
understanding can improve the chances of successfully
balancing risk and reward.
Risk measures should provide the probability of
unfavourable and favourable outcomes occuring.
Unfortunately, many so-called risk measures currently
used in the dairy industry do not. These erroneous risk
measures include pasture as a percentage of total
feed consumed, unit cost of milk production and
operating profit margin.
Pasture utilisation, pasture quality and core costs per
cow are key profit drivers in all pasture-based dairy
feeding systems, regardless of the level of concentrates
fed. These three on-farm profit drivers can have a
greater impact on profit variability than the off-farm profit
drivers of milk price, concentrate price and purchased
fodder price, over which farmers have less control.
Steve Little
Grains2Milk program leader for Dairy Australia
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 7
Appendices
– Further findings from TasMilk60
Tips and traps when collecting farm data
By Gordon Cleary (Agrilink FarmStats P/L)
The TasMilk60 project team had to deal with several
common account allocation errors as part of its quality
assurance process. The points below may be helpful for
anyone collecting farm data for analysis.
Labour costs
• Costs of off-farm work by farm employees,
e.g. sons as off-farm contractors.
Passive ‘pigeon-hole’ errors
• Internal cost transfers between related entities/family
members, e.g. kids ‘wages’.
These include:
• Construction labour costs that should be capital works.
• Value of imputed labour and management excluded.
Herd costs
• Non-dairy animal health costs included.
• Leased land or agistment for replacements missing.
• Concentrates and fodder fed to young stock missing
(buried in milker feed costs).
• A livestock breeding enterprise with costs above and
beyond that needed for normal milker replacements
(e.g. for Asian heifers).
Shed costs
• Electricity uses not related to the dairy facility,
e.g. houses, irrigation pumping, workshop, all running
off one meter.
• Hibitane/teat dip/teat spray buried in ‘chemicals’.
• Livestock requisites (herd costs) buried in ‘dairy
supplies’.
Feed costs
• Cost of feedstuffs for replacement animals included.
• Feed costs for other enterprises, e.g. beef, alpaca,
sheep, emu, ostrich, pigs.
• Use of hay or silage in one year that was made and
paid for in the previous year, that is, an inventory issue.
Overhead costs
• One-off repairs that will not recur in the coming year.
• Overhead costs that do not relate to the dairy
enterprise.
Active ‘pigeon-hole’ errors
Farmers may be inclined to expensing capital spending
within their farm accounting systems, leaving it to
their tax accountants to remove the more outrageous
spending items from operating costs.
It is a trap for the unwary that farm development works
do not always show up as lumpy big outlays. For
instance, the wages for a labourer constructing a new
calf shed should be capitalised as it is not a recurrent
operating cost incurred in producing milk.
In the TasMilk60 study, engaging the farmers in a
discussion about farm capital works almost always
allowed necessary adjustments to be identified.
Unless capital works spending is removed, those farm
businesses that are sufficiently profitable to support
capital spending out of cash flow appear to have a high
unit cost of production and low calculated profitability.
• Feed dollar spend in the year is different from the
dollar value of the feed used in the year, usually
through end-of-year pre-purchases.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
53
Six principles for identifying determinants of profitability
by analysing data from groups of dairy farms
By John Morton, Epidemiologist (Jemora P/L)
1. Don’t observe only high (or low)
profitability farms
When identifying determinants of profitability,
observations of just high profitability farms can be
misleading. For example, suppose that TasMilk60 had
studied just the 15 farms with milk EBITD per cow
above $300 in 2006/07. In assessing whether milk
yield per cow is a determinant of profitability, we could
describe the milk yields in these 15 high profitability
farms; the median litres per cow was 6,058. This is
high compared to Tasmanian industry means of just
over 4,000 litres per cow around the time that the study
commenced, inviting the conclusion that higher milk
yield per cow results in higher farm profitability.
This conclusion would be erroneous. On the remaining
53 farms (those with milk EBITD per cow of $300 or
less), the median litres per cow was 6,674, higher than
for the high profitability farms. In fact, as discussed in
Section 3, there was no simple relationship between
milk yield per cow and farm profitability, and both farms
with low and high milk yields per cow could be highly
profitable. The same limitation applies with observations
of just low profitability farms.
(shown on page 28), milk price (¢/L) was proposed
to be determined by milk protein concentration, milk
fat concentration, calving system (as a surrogate for
seasonality of milk supply), manufacturer and year.
Accordingly, all five variables were accounted for when
assessing determinants of milk price.
3. Incorporate unexplained variability
into interpretation
The human mind tends to place insufficient emphasis
on unexplained variability (or ‘scatter’); this can result in
us attributing importance to numeric patterns that may
well be due simply to random variability. Even the most
apparently convincing relationships on visual analysis
should be assessed statistically. A strong or large effect
is meaningful only if it is strong or large relative to the
scatter; an apparently strong or large effect among
study subjects is quite compatible with truly no effect
if the scatter is extreme. For example, consider the
relationship between stocking rate and pasture utilised
per hectare in 2006/07 (Figure 48).
This relationship is quite consistent with important
indirect effects of stocking rate on pasture utilised per
hectare. But we would be much less certain about these
Multivariable analytical methods and restriction of
analyses to sub-groups were used extensively in the
TasMilk60 study, primarily to remove confounding. Path
modelling allowed potential confounders to be defined
(in the null path models), and multivariable and restricted
analyses were performed to account for these potential
confounders. For example, in the null path model
54
10
5
Pasture utilised per hectare (t DM)
Results of univariable associations should be viewed
only as preliminary reflections of reality, and to identify
relationships worthy of more detailed investigation. A
major reason for this caution is confounding.
0
Determinants of profitability on dairy farms are sometimes
studied using univariable (or crude) associations.
Indeed, such associations were used extensively in this
report. For example, in the TasMilk60 study, potential
determinants of changes in key physical and financial
changes between years were assessed using univariable
associations, as were associations between attitudinal
indices and measures of farm profitability.
15
2. Consider confounding
0
1
2
3
4
Stocking rate (cows per hectare)
Figure 48: Associations between pasture utilised
per hectare and stocking rate in 2006/07 in farms
where irrigation was used that were feeding <1t
concentrates per cow (green squares), 1t to <2t (blue
triangles), and ≥2t (gold diamonds).
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
effects if, instead, the relationship appeared as in
Figure 49. This relationship is identical to that in
Figure 48, except that additional random ‘scatter’ or
unexplained variability has been added. Statistical
methods such as regression analysis and analysis of
variance explicitly assess possible effects in relation to
this ‘scatter’ or unexplained variability. P-values and
confidence intervals were used throughout this study to
incorporate unexplained variability into interpretation.
Note that this is an example of a univariable association
and, as discussed above, potentially confounded.
However, a positive association was also present on
TasMilk60 farms where <2t of concentrates were fed
after adjusting for several potential confounders.
4. Account for similarity of observations
from the same farms between years in
statistical analyses
In the TasMilk60 study, management and profitability on
dairy farms were studied using physical and financial
data collected from the same farms over multiple years.
The unit of analysis for most of this work was the farm
year. Each farm contributed one farm year of data for
each year in which it was enrolled. This raises a possible
complexity as observations in one year may well be
similar to those from another year on the same farm.
15
Three strategies were used in the TasMilk60 study to
account for the similarity of observations from the same
farms between years. Some analyses were conducted
separately within each study year, so avoiding including
repeated observations from the same farm in the same
analysis. Second, each farm was fitted as a ‘random
effect’ to account for any such similarity of observations
from the same farms between years. Third, when
analysing proportions (or percentages), robust standard
errors adjusted for clustering were used.
5. Do not aggregate data from individual farms
to produce means etc for groups of farms
When studying management and profitability on dairy
farms, it is possible to aggregate data from individual
farms to produce means etc for groups of farms. For
example, to assess the relationship between milk yield
per cow and milk EBITD per cow, it would have been
possible to calculate means of milk EBITD per cow for
farms grouped into various milk yield per cow categories
and to compare these means using linear regression.
This approach would be valid if the research question
was to describe this relationship for groups of farms
(i.e. do groups of farms with higher mean milk yield per
cow have higher mean milk EBITD per cow). However,
when studying as management and profitability on
dairy farms, inference is usually desired at the individual
farm level. For example, if the study results are to
be used to help farm managers understand what
may happen on their farm with a particular series of
management changes, inference is clearly being made
to the individual farm or farm year level. Given this, such
studies should be conducted with the farm (or farm
year) as the unit of analysis.
5
10
6. Consider the stories behind the
regression models
0
Pasture utilised per hectare (t DM)
If repeated observations within a farm are more similar
to each other than would be expected based on the
overall variability in the data, we would say that the data
are ‘clustered’ within farms. Such repeated observations
from the same farm should not be considered as
statistically independent observations.
0
1
2
3
4
Stocking rate (cows per hectare)
Figure 49: Associations between pasture utilised
per hectare and stocking rate in 2006/07 on farms
where irrigation was used that were feeding <1t
concentrates per cow (green squares), 1t to <2t
(blue triangles), and ≥2t (gold diamonds) with added
random ‘scatter’ or unexplained variability.
Given the physical and financial complexity of dairy
farm businesses, most practically achievable analyses
are unlikely to fully explain all differences in profitability
between farms or farm years. Regression models
essentially describe general relationships across
the study population, and individual farms will not fit
these general patterns. This was demonstrated in the
TasMilk60 study where some high profitability farms
were shown to have quite high feed costs per cow, even
though, all else being equal, profitability would have
been higher when feed costs per cow were lower.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
55
A similar approach was used to explore differences
between consistently high profitability farms and
consistently low profitability farms. There are many ways
to ‘skin a cat’ and numerous ways to run a profitable
dairy farm. These approaches can add to the results
of regression models to build a richer picture of why
profitability varies between dairy farms.
56
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
Section 8
References
Beever, D.F. and Doyle, P.T. (2007), Feed conversion efficiency as a key
determinant of dairy herd performance: a review. Australian Journal of
Experimental Agriculture. 47: 645–657.
Malcolm, L.R. (2001), Farm Management Economic Analysis:
A Few Disciplines, a Few Perspectives, a Few Futures. Australian
Agribusiness Perspectives, Paper 42.
Chapman, D.F., Malcolm, L.R., Neal, M. and Cullen, B.R. (2007), Risk
and uncertainty in dairy production systems: Research concepts,
tools and prospects. In: D.F. Chapman, D.A. Clark, K.L. Macmillan,
D.P. Nation, eds. Meeting the Challenges for Pasture-Based Dairying,
Proceedings of the Australasian Dairy Science Symposium, Melbourne,
Australia, National Dairy Alliance. pp 476-491.
Malcolm, L.R., Makeham, J. and Wright, V. (2005), The farming game:
Agricultural management and marketing. Cambridge University Press,
Cambridge, UK.
Christie, K., Rawnsley, R. and Eckard, R. (2011), A whole farm systems
analysis of the greenhouse gas emissions of 60 Tasmanian dairy farms,
Proceedings of the 4th International Greenhouse Gases and Animal
Agriculture Conference, Banff, Canada, October 2010.
Gronow, C.S., Little S.B. and Cleary G.V. (2010), Managing production
system risk: An application of Monte Carlo simulation to Tasmanian
dairy farm data, Proceedings of the 2010 Australasian Dairy Science
Symposium, Lincoln University, New Zealand.
Malcolm, L.R. (2009), Managing Uncertainty Pragmatically in Private
and Public Decision-making about Investment, Australian Agribusiness
Perspectives, Paper 81.
Pasture Consumption and Feed Conversion Efficiency Calculator
Instruction Manual (2010). Dept. of Primary Industries (DPI) Victoria.
http://new.dpi.vic.gov.au/agriculture/dairy/pastures-hay-silage/calculator.
Slovic, P. (2000) ed., The Perception of Risk. Earthscan, Virginia. 2000.
Thomson, D. (2001), As if the landscape matters: the social space of
‘farming styles’ in the Loddon Catchment of Victoria. Unpublished PhD
thesis, The University of Melbourne.
Hardaker, J.B., Huirne, R.B.M. and Anderson, J.R. (1997), Coping with
risk in agriculture, CAB International.
Topp, V. and Shafron, W. (2006), Managing Farm Risk, ABARE eReport
06.6.
Hardaker, J.B. and Lien, G. (2007), Rationalising Risk Assessment:
Applications to Agricultural Business. Australasian Agribusiness Review
15:75-93.
USA Farm Financial Standards Council website http://www.ffsc.org/Files/
FarmFinancialGuidelinesRatios.pdf
Little, S.B. (2010) Feed Conversion Efficiency – A key measure of feeding
system performance on your farm. Dairy Australia Grains2Milk program.
http://www.dairyaustralia.com.au/Animals-feed-and-environment/
Feeding-and-nutrition
Waters, W., Thomson, D., Nettle, R. (2006) Derived attitudinal farmer
segments: A method for understanding and working with the diversity of
Australian dairy farmers, Extension Farming Systems Journal, Volume 5,
Number 2.
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
57
58
Performance, Profit and Risk in Pasture-based Dairy Feeding Systems – Findings from the TasMilk60 study
4
Performance, Profit and Risk in Pasture-based Dairying Systems – Findings from the TasMilk60 study
ISSN/ISBN/REF 0000
Dairy Australia Ltd ABN 601106227
Level 5, IBM Centre
60 City Road Southbank VIC 3006
T + 61 3 9694 3777 F + 61 3 9694 3888
www.dairyaustralia.com.au