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Regular estimates of herbage mass can improve profitability
of pasture-based dairy systems
P. C. Beukes
A,B
, S. McCarthy
A
, C. M. Wims
A
, P. Gregorini
A
and A. J. Romera
A
A
DairyNZ Ltd, Private Bag 3221, Hamilton 3240, New Zealand.
B
Corresponding author. Email: pierre.beukes@dairynz.co.nz
Abstract. Paddock selection is an important component of grazing management and is based on either an estimate of
herbage mass, or the interval since last grazing for each paddock. Obtaining estimates of herbage mass to guide grazing
management can be a time consuming task. A value proposition could therefore assist farmers in deciding whether to
invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of
three levels of knowledge of individual paddock herbage mass on profitability of two typical pasture-based dairy systems
in New Zealand; a medium input system stocked at 3.2 Friesian-Jersey cross bred cows/ha with ~15% imported feed, and
a high input system stocked at 4.5 Friesian cows/ha with ~40% imported feed. The three levels of knowledge were:
(1) ‘perfect knowledge’, where herbage mass per paddock is known with perfect accuracy, (2) ‘imperfect knowledge’,
where herbage mass per paddock is estimated with an average error of 15%, (3) ‘low knowledge’, where herbage mass is
not known, and paddocks are selected based on longest time since last grazing. In both systems, grazing management
based on imperfect knowledge increased farm operating profit by ~NZ$385/ha at a milk price of NZ$6.33/kg milksolids
(fat + protein) compared with low knowledge. Perfect knowledge added a further NZ$155/ha to profit. The main driver of
these results is the level of accuracy in daily feed allocation, which increases with improved knowledge of herbage
availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding,
higher milk production, and more optimal post-grazing residual herbage mass to maximise herbage regrowth.
Additional keywords: grazing management, herbage allowance, modelling, monitoring, paddock selection, value
proposition.
Received 20 March 2017, accepted 15 November 2017, published online 5 January 2018
Introduction
A focus on grazing management is becoming more critical in an
attempt to reduce the dependency on expensive imported
supplements. Other important factors are new environmental
objectives and a need to retain the low-cost advantages of the
pasture-based dairy industry in New Zealand (Clark et al.2007).
This means a focus on growing and utilising herbage as a source
of high quality, low-cost feed and as the foundation of a profitable
farm business. Grazing management can be divided into three
levels: strategic, tactical and operational, with paddock selection
a key interface between tactical and operational management.
Paddock selection is largely undertaken using criteria such as
herbage mass [kg dry matter (DM)/ha], leaf regrowth stage,
or days since last grazing. Current best management practice is
to regularly collect herbage mass data from each paddock, then
rank paddocks highest to lowest herbage mass to determine
the grazing sequence for the near future, for example the next
7 days. Rotation cycle or round length is normally part of this
planning process and determines the proportion of the farm
grazed each day (Macdonald et al.2010). For each grazing
event, the area allocated, the herbage mass in the selected
paddock, and the number of animals in the mob determine
herbage allowance per cow, which has important consequences
for herbage DM intake and utilisation (Dalley et al.1999). This
in turn has consequences for defoliation severity (i.e. the post-
grazing residual herbage mass), herbage regrowth, sward
structure, quality of subsequent available herbage, and for
supplement feeding and silage harvesting decisions (Fulkerson
et al.2005; Lee et al.2007,2008).
Recent analyses in New Zealand indicate that recommended
grazing management targets on dairy farms are not achieved
at ~50% of grazing events (McCarthy et al.2014). Chapman
et al.(2014) list some of the barriers to improving this
situation; failure to recognise the financial impact of good
grazing management decisions on a daily basis, time cost of
collecting paddock herbage mass information, data-intensive
decision tools, low confidence in decision tools, farm
infrastructure, including paddock numbers and size, which
compromises herbage allocation, and poor decision making
with supplement feeding in order to optimise rotation cycle
and post-grazing residuals. In a survey of 18 dairy farms in
Australia on the usefulness of objective herbage mass data
Eastwood et al.(2010) found that important factors for
farmers were frequency of data delivery, the need to build
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trust in the accuracy of the data, and the need for an understanding
of the value proposition for aligning herbage mass data with
grazing decisions.
The effort to collect regular estimates of herbage mass will
vary from farm to farm and depends on the method (e.g. visual
assessment, rising plate meter, sensor technology attached to
a farm vehicle) and the size of the farm. For instance, it may take
4 to 5 h to walk a 290-ha farm with a plate meter or only 1 h to
cover a 200-ha farm with a sensor-mounted ATV bike (Greg
McCracken, pers. comm., December 2015). Time for data
preparation and analysis has to be added to this. Nonetheless,
it presents a considerable effort for farmers if herbage mass
data is to be collected regularly (from weekly to fortnightly).
Aligning objective data use with grazing decision making
requires not only accurate and timely data, but also a positive
value proposition before dairy farmers will switch from their
current methods to an objective data-driven system (Eastwood
et al.2010).
The objective of this study was to estimate a value proposition
by evaluating the potential production and economic benefits
of measuring herbage mass, and using the acquired data to
improve grazing management decisions on typical dairy farms
in the Waikato region of New Zealand. For this, we used a farm-
scale model to simulate different levels of accuracy of herbage
mass knowledge.
Methods
The DairyNZ Whole Farm Model
The DairyNZ Whole Farm Model (WFM; Beukes et al.2008;
Gregorini et al.2014) was developed to assist with analysis and
design of dairy farm systems through scenario testing under
various system interactions that occur over multiple years. The
WFM simulates a farm on a daily time step, rotating non-lactating
and lactating cow herds through the paddocks separately
according to a user-specified rotation policy. Post-grazing
residual herbage mass (residual) is determined by the model as
a function of the feed demand of the herd, grazing hours and
herbage allowance on that day. The user can specify minimum
residuals in time blocks of 10 days below which animals are not
allowed to graze. In the WFM, the residual influences herbage
regrowth of the paddock, and the impact on herbage feeding
value (i.e. chemical composition and sward canopy structure) is
partially captured through the accumulation of dead material
in the sward canopy. Herbage chemical composition for green
and dead material in WFM is a user-defined input of feed
composition values per month for green herbage, and one set
of feed composition for dead herbage. Paddocks can be closed
for conservation by removing them from the grazing round and
allowing herbage mass to accumulate before cutting for silage,
which is stored in a feed store accounting for losses in the
ensiling process. Supplements (home-grown or imported) can
be fed to cows according to policies created by the user (e.g.
dates, which cows, type of feed, amounts, where fed, and
fed before or after herbage). Crops can be grown on the farm
taking account of time required for paddock preparation, costs
of crop growing, and the time and costs of re-grassing
following the crop. Other user-defined policies related to cow
management include breeding, grazing off farm, use of
off-paddock infrastructure for duration controlled grazing,
drying off, culling and replacement.
The economics component in the WFM consists of a profit
and loss statement, balance sheet and return on assets. Revenue
is primarily generated through milksolids (MS = fat + protein)
sales, where this is MS production multiplied by the price per kg
MS. Additional revenue is earned from the sale of cull stock.
A large proportion of costs are defined in an activity-based
costing framework with default values generated through the
use of economic survey data. For this study, economic input
data were updated for the 2012–2013 year (DairyNZ Limited
2014), with a milk price of NZ$6.33/kg MS. The 2012–2013 year
was selected because input costs and milk price for this year
are close to the long-term average for New Zealand (DairyNZ
2014). Apart from revenue and costs, calculation of annual
farm operating profit also includes adjustments in terms of the
economic value of changes from the start to the end of the year in
livestock numbers and liveweight, average farm herbage mass,
and feed stores. For instance, if a farm cuts more silage in
a particular year than it feeds out, the value of the change in
the silage feed store will be a credit in the balance sheet.
Model improvements
The default paddock selection policy in WFM selects the next
available paddock, or break in a paddock (portion of a paddock),
that has the highest herbage mass. The paddock selection policy
is based on ‘perfect knowledge’of energy demand of every cow,
and herbage mass of every paddock on a daily basis, which is
only realistic in a modelling context.
For this study, two alternative paddock selection policies
were developed representing more typical on-farm approaches.
In the one alternative policy, the model assumed the farmer
would estimate herbage mass with an average error of 15%
(an average error of 450 kg DM/ha on a paddock with an
actual herbage mass of 3000 kg DM/ha). This average error
was derived from O’Donovan et al.(2002) who found
estimation errors varying from 9% to 21% for four methods of
herbage mass estimation. In this ‘imperfect knowledge’policy
the model applies an ‘error’to the actual herbage mass per
paddock to generate an estimated herbage mass that mimics
the collection of herbage mass data on commercial farms.
These ‘errors’were generated by using a normally distributed
scalar of 1 0.15, and applying these scalars to the actual herbage
mass for each paddock before paddock selection for the next
grazing. In a second alternative policy it was assumed that the
farmer does not assess herbage mass, but records the sequence
in which paddocks are grazed and simply continues moving
through the paddocks by always selecting the next paddock
based on the longest time since the last grazing or cutting
event. This ‘low knowledge’policy ignores differential
herbage accumulation rates between paddocks, and the
potential for faster growing paddocks to come up for grazing
sooner than the policy dictates. This policy is used on actual farms
because it does not require any estimate of herbage mass, but
relies on recording the paddock that has just been grazed or cut,
which by implication also provides the information on the
paddock that was grazed or cut the furthest back in the past
i.e. farmers follow a pre-determined order of paddock grazing.
BAnimal Production Science P. C. Beukes et al.
Model calibration
One major factor that farmers have to deal with in pasture
management is the difference between paddocks in herbage
growth rates and consequently annual herbage yield (Clark
et al.2010;Romeraet al.2010). As the focus of this modelling
exercise is to determine the value of using knowledge of herbage
mass to make better management decisions, it was essential to
capture some of these differences.
Weekly calibrated herbage growth rates from DairyNZ’s
Scott farm, Hamilton, New Zealand (–37.78; 175.32) were
used to estimate annual herbage yield for 26 paddocks for the
period 1 June 2011 to 31 May 2012 and paddocks were ranked
according to relative yield (Fig. 1). The relative yield was used
in adjusting the fertility factor (default 1.0) for each paddock in
the initialisation page of WFM. On this input page the model
user identifies each paddock, defines the soil type, defines the
herbage type (e.g. ryegrass-clover), the paddock area, herbage
mass at the start of the simulation, and the fertility factor. The
fertility factor is a scalar that adjusts the daily growth rates as
predicted by the pasture model and can be used to adjust the
annual herbage growth curve up or down depending on specific
growth conditions for a particular paddock.
For this modelling exercise the 2012–2013 farm season was
used in developing the baseline scenarios (see next section).
After the WFM was set up for the farm, it was simulated with
weather data from the Ruakura meteorological station (–37.77;
175.31 situated on the outskirts of the city of Hamilton, NZ) for
the period 1 June 2012 to 31 May 2013. The fertility factor for
individual paddocks was adjusted in an attempt to represent the
observed variability in annual herbage yield. Figure 1shows
the inter-paddock variability in annual herbage yield as predicted
by the WFM for the 2012–2013 season. The result was regarded
as an adequate representation of the observed differences in
paddock performance.
Scenario development and assumptions
The WFM was initialised for two dairy systems typical of the
Waikato region of New Zealand for the 2012–2013 season;
a medium input system and a high input system. The medium
input system imports ~15% of the annual feed requirement from
external sources. The farm is stocked at 3.2 cows/ha (Friesian ·
Jersey cross bred cows with average liveweight per cow of
~500 kg in December), replacement stock are reared off farm,
and non-lactating cows are wintered on the farm. Planned start of
calving is on 1 July, with cows achieving peak milk in late spring,
September and October. Dry off date is in autumn (7 May in this
study) resulting in an average lactation length of ~270 days for
the herd. Milksolids production is ~380 kg/cow.year and 1200 kg/
ha.year. No crops are grown, but surplus herbage is ensiled as
pasture silage. Additional silage and palm kernel expeller (PKE)
are imported to fill herbage deficits, mainly in early spring before
the herbage flush, mid-summer when herbage growth is slow in
January–March, and late autumn to increase cow body condition
score before the next calving. These supplements are fed in the
paddock with wastages of 15% for pasture silage and 25% for
PKE (DairyNZ 2010). Pastures receive ~150 kg nitrogen (N)/ha.
year mainly from N fertiliser, but also from farm dairy effluent
recycled onto some of the paddocks.
The high input system is stocked at 4.5 cows/ha and imports
~40% of the annual feed requirements from external sources.
Cows are larger Friesians with an average liveweight of 560 kg
in December. Start of calving is the same as the medium input
system, 1 July, but dry-off date is later, 29 May in this study,
resulting in a lactation length of up to 300 days. Replacement
stock is reared off the farm, and non-lactating cows are also sent
off-farm from late May to return just before calving. Milksolids
production is ~500 kg/cow.year and 2000 kg/ha.year. A forage
crop is grown on a portion of the farm producing higher yields
than pasture, for example, maize for silage that can yield from
1.4
1.2
1.0
0.8
13 11 9 20 12 14 26 17 16 3 22 19 1 25 7 21 24 23 8 15 18 5 2 4 10 6
Relative yield
Paddock number
0.6
0.4
0.2
0
Fig. 1. Observed (black) and predicted (grey) relative annual herbage yield for 26 paddocks at Scott farm,
Hamilton, New Zealand. Average yield = 1.0.
Value of herbage mass estimates Animal Production Science C
19 t DM/ha in a relatively poor year to 27 t DM/ha in a good year.
In this study, maize silage was grown on 6% of the high input
farm. Due to the high stocking rate there is seldom surplus
herbage, so much less pasture silage is made in this system
compared with the medium input. Imported feeds are pasture
and maize silage, and PKE, and are fed throughout the year.
These supplements are either fed in the milking parlour, or on
a feed pad, or in mobile trough feeders in the paddock. Because
of these facilities, supplement wastage is reduced to ~10% of
the offered feed (DairyNZ 2010). Fertiliser and effluent N onto
paddocks is the same as in the medium input system.
For every farm system the three paddock selection policies
were simulated; using perfect knowledge, imperfect knowledge,
or low knowledge of herbage mass for paddock selection. The
rotation policy was the same for the two farm systems and used
the default in the model, which varies the rotation cycle from as
long as 100 days in winter to 70 days at start of calving, which is
then reduced stepwise to the shortest rotation at balance date
(when herbage growth rate equals feed demand) of ~20 days
(Macdonald et al.2010). The residual policy was set to 1500 kg
DM/ha as the minimum residual allowed throughout the year
for all scenarios. In WFM the annual herbage yield (t DM/ha) for
a farm system is largely driven by the climate year (data from
Ruakura meteorological station supplied by the National Institute
of Water and Atmospheric Research). The medium input system
was simulated over many climate years in an attempt to select
three climate years with herbage yield predictions spanning
the range of known values. In the end three climate years were
selected and were simulated for each farm system and each
paddock selection policy –2004–2005 representing a ‘good’
herbage yield of ~20 t DM/ha; 2013–2014 representing
a‘normal’herbage yield of ~17.5 t DM/ha; 2012–2013 for
a‘poor’herbage yield of ~16 t DM/ha. All scenarios started
on 1 June with paddock herbage masses distributed to give
a feed wedge with an average farm herbage mass of 2500 kg
DM/ha on that date. For this modelling it was assumed that
more supplements will be imported during periods of pasture
deficit due to low summer and autumn rainfall to keep cows
lactating until target dry-off dates of 7 May in the medium and
29 May in the high input system. The dry-off date was kept
constant between scenarios allowing supplement costs to vary
between climate years and paddock selection policies.
Two management policies differed between the three
paddock selection options: surplus herbage, and supplement
feeding. In the perfect knowledge scenario the surplus herbage
policy looked for an average residual >1700 kg DM/ha; when
this was triggered all paddocks >3500 kg DM/ha were cut and
the surplus ensiled as pasture silage, with 15% wastage. The
assumption was that this manager has the knowledge to identify
a rising residual early, and will also accurately identify the
correct paddocks with surplus herbage for cutting. In the case
of imperfect knowledge the manager monitors herbage mass
per paddock, albeit with a margin of error, therefore he will
also be relatively early in identifying a rising residual >1700 kg
DM/ha, but there will be some error in identifying the paddocks
with the highest herbage mass, so only paddocks >4000 kg DM/
ha would be cut. As herbage mass is not assessed by the low
knowledge manager, identification of a rising residual and of the
correct paddocks for cutting will be problematic, so the rule was
set to cut all paddocks >4000 kg DM/ha when the average residual
is >2000 kg DM/ha, capturing an obvious surplus even without
any measurements.
In the perfect knowledge scenario the supplement policy
resulted in cows being fed supplement to meet their energy
requirements whenever there was a herbage deficit. In this
scenario the exact energy requirement of each cow and the
exact herbage mass of the next paddock in the rotation is
known, allowing supplement to be offered at exactly the right
amounts i.e. no under- or over-feeding on any particular day. To
achieve this in the medium input system supplements were taken
first from the pasture silage feed store until it ran out, and then
purchased PKE was fed. At the end of the simulated year the feed
stores were topped up to the starting amounts with the costs
reflected in the operating profit. In the high input system
supplements were taken from the PKE feed store throughout
the year but never more than 5 kg DM/cow.day. Maize silage
was fed mainly in the early and later parts of the season,
August–October and February–May, but never more than 6.5
kg DM/cow.day. The maize silage feed store was replenished
by the maize crop on the farm when it was harvested in late
March. However, in some years more maize silage was used
than was grown and this had to be imported at a cost. Pasture
silage was the third feed store in the high input system and was
used to fill the rest of the deficit, mostly in spring, August and
September, and again in autumn, March–May. Similar to the
medium input system all feed stores were topped up at the end of
the year on 31 May and the costs reflected in calculations of farm
operating profit.
In contrast to perfect knowledge, in the imperfect and low
knowledge scenarios herbage allowances, deficits, and exact
amounts of supplement to feed are not known with complete
accuracy. This real-world situation was mimicked by running
the model for the perfect knowledge scenarios and extracting
the average fortnightly amounts of supplement fed in each of
the three climate years. It was assumed in the imperfect and low
knowledge scenarios that farmers will perceive the same feed
deficits as in the perfect knowledge scenarios, but only at the
fortnight level. Because they do not have perfect knowledge,
they do not know the exact magnitude of these deficits each day.
Therefore, during a perceived feed deficit they will feed a flat
rate of supplement without adjusting the rate as the magnitude
of the deficit changes. The average fortnightly supplement
amounts extracted from the perfect knowledge scenarios were
entered into the imperfect and low knowledge scenarios, but in
the case of the latter two, supplements were fed before cows
were put to pasture so that paddock selection and, therefore,
herbage allowance dictated the overall level of feeding. This
resulted in less accurate feed allocation in the imperfect and low
knowledge scenarios, but similar levels of supplement use over
the season. The WFM is coded to simulate a user-defined rate
of substitution of herbage for supplements when supplements
are fed before herbage on any particular day. In the case of PKE,
pasture silage, and maize silage, substitution rates of 0.5, 0.7, and
1.0 kg DM herbage/kg DM supplement, respectively, were used,
reflecting substitution rates of supplements ranging in rumen-fill
effect, when animals are already well fed (Holmes et al.2002).
All scenarios were run with economic input for the 2012–
2013 year (DairyNZ Limited 2014). Prices were NZ$290/t DM
DAnimal Production Science P. C. Beukes et al.
for bought-in pasture silage, NZ$340/t DM for maize silage,
NZ$270/t DM for PKE, NZ$140/t DM for cutting silage, and
NZ$40/t DM for feeding supplements. The actual milk price
for 2012–2013 was used (NZ$6.33/kg MS), but sensitivity
was tested for NZ$5 and NZ$7. Simulations were run for the
two farm systems, three paddock selection policies and three
climate years and outputs collected were monthly farm average
herbage mass, annual herbage yield, milk production, supplement
costs and operating profit.
Results
In the medium input system average annual herbage yield
increased by 8% from low to perfect knowledge scenarios
(Table 1). The higher herbage yield in the perfect knowledge
scenario is explained by average farm herbage mass and post-
grazing residuals in November–February being more in the target
range for optimum net growth (i.e. gross growth –senescence)
compared with the imperfect and low knowledge scenarios
(Fig. 2a,b). Lower average herbage mass and residuals reflect
a combination of perfect paddock selection and a timely and
accurate surplus herbage policy, where increasing residuals are
identified early and the correct paddocks cut for silage.
Compared with the medium input system, the high input system
showed small differences between post-grazing residuals and
average farm herbage mass for the three paddock selection
policies (Fig. 2c,d). This translated into very little difference
in herbage yield between the three policies (Table 1).
The average amounts of supplements fed remained the
same across the three knowledge systems, explained by the
assumption that imperfect and low knowledge managers
would feed approximately the same time of the year and the
same amounts of supplements than perfect knowledge managers
would do. Pasture growth year resulted in annual herbage
yield increasing from poor to good years by 26% on average,
resulting in supplement feeding decreasing in the same years by
55% and 25% in medium and high input systems, respectively
(Table 1).
In both medium and high input systems there was little
difference in milk production per hectare between the perfect
and imperfect knowledge scenarios, but, compared with the
perfect knowledge scenario, the low knowledge scenario was
lower by 6% in the medium input, and 5% in the high input
systems (Table 1). Differences in MS production per hectare
can be explained by differences in milk production per cow as
shown in Fig. 3. The relatively smooth profiles for feed eaten
and milk production with perfect knowledge was the result of
known herbage allowances and therefore perfect allocation of
supplements to fill herbage deficits. In contrast, both imperfect
and low knowledge created periods of under- and over-feeding
resulting in fluctuating intakes and milk production. Compared
with perfect knowledge as the reference point, the daily intakes
in imperfect and low knowledge scenarios fluctuated between
0.8 and 1.16 kg DM/day on average, whereas milk production
fluctuated between 0.54 and 1.12 kg/day on average.
Milksolids production and cost of supplements were the
main factors that influenced operating profit and the differences
predicted for the three paddock selection policies across the three
herbage growth years. On average, profit was approximately
NZ$140 to 170/ha higher in perfect compared with imperfect
knowledge scenarios (Table 1). A substantial part of this
difference in the medium input system came from a feed store
adjustment of NZ$418/ha in the good growth year for perfect
knowledge compared with NZ$206/ha for imperfect knowledge
Table 1. Model output for a two typical Waikato farm systems with three paddock selection policies for three herbage growth years; poor –2012–2013,
normal –2013–2014, good –2004–2005. Operating profit is based on a milk price of NZ$6.33/kg milksolids
Medium input High input
Variable Year Perfect
knowledge
Imperfect
knowledge
Low
knowledge
Perfect
knowledge
Imperfect
knowledge
Low
knowledge
Annual herbage yield (t DM/ha) Poor 16.7 15.9 15.5 16.1 16.1 15.8
Normal 18.1 17.1 16.9 17.3 17.4 17.2
Good 21.2 20.4 19.5 20.1 20.2 19.8
Average 18.7 17.8 17.3 17.8 17.9 17.6
Supplements fed (t DM/ha) Poor 7.5 7.2 7.2 14.1 14.1 14.0
Normal 6.3 6.4 6.4 14.3 14.3 14.3
Good 3.2 3.3 3.3 10.6 10.6 10.6
Average 5.7 5.6 5.6 13.0 13.0 13.0
Milksolids production (kg/ha) Poor 1287 1267 1211 2087 2054 1944
Normal 1280 1291 1230 2110 2104 2033
Good 1281 1284 1190 2094 2041 1992
Average 1283 1281 1210 2097 2066 1990
Cost of supplements (NZ$/ha) Poor 1994 2013 1985 4660 4659 4507
Normal 1622 1759 1729 4563 4568 4534
Good 1007 907 925 3310 3261 3242
Average 1541 1560 1546 4178 4163 4094
Operating profit (NZ$/ha) Poor 2557 2377 2082 3518 3340 2820
Normal 3005 2902 2585 3883 3835 3500
Good 3970 3833 3289 4956 4671 4374
Average 3177 3037 2652 4119 3949 3565
Value of herbage mass estimates Animal Production Science E
in the same year. The good year in the medium input system was
the only situation where there was more pasture silage cut than
fed in the same year, resulting in a positive feed store adjustment
across all knowledge scenarios. The high feed store adjustment
for perfect knowledge means that the value of accurately
identifying and cutting surplus pasture was realised in the
good growth year. In the high input system the difference
between perfect and imperfect knowledge was driven by lower
MS production in imperfect knowledge mainly in the good
growth year of 2004–2005. Both input systems had the same
higher average profit of approximately NZ$385/ha in imperfect
compared with low knowledge scenarios. This difference was
mainly the consequence of an average 3.7% lower MS
production in the low knowledge scenario, the result of more
frequent and more severe under-feeding of lactating cows because
of no knowledge of herbage mass and allowance (Fig. 3).
Discussion
Results from the medium input system with perfect knowledge
demonstrated that more herbage is grown by keeping average
herbage mass in the rapidly growing phase of the grass growth
curve (Parsons et al.1988). This is achieved by grazing
perennial ryegrass based pastures between 2600 and 3200 kg
DM/ha during the main grazing period, and ensuring that post-
grazing residuals are managed well and with consistency,
~1500–1700 kg DM/ha or between 3.5 and 4.5 cm
compressed height (Macdonald et al.2010; McCarthy et al.
2014). The results further demonstrate that in high input
systems, with a high grazing pressure coming from high
stocking rate and some land taken for cropping, good residuals
and average farm herbage mass can be achieved with any level
of knowledge for paddock selection. This is often one of the
reasons why farmers opt for a high input system –the high
stocking rate and associated grazing pressure reduces the
chances of poor herbage utilisation as a result of errors in
pasture management. The strategy of these farmers is to
increase total herbage eaten per hectare through the use of
relatively high stocking rates and maintaining moderate levels
of herbage intake per cow (Romera and Doole 2015). The
low average post-grazing residuals in the high input system
(Fig. 2c) are conducive for higher annual herbage yield
(Macdonald et al.2008), but this was not realised in our study
(Table 1), probably because rotation cycles were not different
between medium and high input systems, whereas in the
Macdonald et al.(2008) study rotation cycles in winter,
summer and autumn increased linearly with increasing
stocking rate. The longer rotation cycles was an attempt to
partially alleviate increasing feed deficits as a result of
increasing stocking rates, and created longer inter-grazing
intervals that allowed more time for pasture regrowth
(Macdonald et al.2008).
2200 (a)
(b)
(c)
(d)
2000
1800
1600
1400
1200
3000
3500
2000
Month
kg DM/ha
2500
1500
Perfect knowledge
Imperfect knowledge
Low knowledge
1000
June
July
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
June
July
Aug.
Sep.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
500
0
Fig. 2. (a,c) Monthly average post-grazing residual herbage mass and (b,d) farm herbage mass for
three paddock selection policies for (a,b) medium input, and (c,d) high input systems.
FAnimal Production Science P. C. Beukes et al.
Periods of under-feeding were more frequent and more
severe in low knowledge compared with imperfect knowledge,
indicating that paddock selection and, therefore, herbage
allowance was more often wrong in the low knowledge scenario.
The occasional over-feeding in both of these scenarios, and the
consequent increase in milk production during these periods, was
the result of substitution of herbage for supplements. Because
herbage allowances were not known with perfect accuracy, and
because the timing and amounts of supplements fed were based
on information from the perfect knowledge scenario, more
supplements were fed than was required. Substitution resulted
in some herbage being left ungrazed, but also increased total dry
matter intake during these periods. In essence, both under- and
over-feeding stems from poor paddock selection and supplement
feeding based on unknown herbage allowance. It can be argued
that these imperfect and low knowledge managers would use the
daily milk production information from their animals to inform
decisions about supplement feeding level. However, as long as
herbage allowance remains uncertain, adjustments to supplement
feeding level will be arbitrary. Fulkerson et al.(2005) found
that accurate daily herbage allocation to dairy cows resulted in
a relatively constant daily intake, improved feed conversion
efficiency, and consistent post-grazing residuals effected by
maintaining a constant grazing pressure. They conclude that
accurately allocating feed to dairy cows on a daily basis
should significantly increase production potential of the
pasture, with the major benefit of improving the use of pasture
previously wasted by over-allocation (Fulkerson et al.2005).
Our results support the contention that good herbage mass
data, and paddock selection based on this data, has a substantial
absolute value in any system where herbage forms the bulk of
the animal diet. The value of NZ$385/ha was sensitive to milk
price and changed to NZ$300/ha at NZ$5, and NZ$450/ha
at NZ$7/kg MS. These estimated profit gains are of the same
magnitude as the NZ$360–600/ha, at an economic value of
NZ$0.30/kg DM, estimated by Chapman (2016) for the extra
herbage grown as a result of consistently achieving the target
residual of 1500–1700 kg DM/ha. Chapman (2016) further
estimates that NZ$260–480/ha is on offer for the correct timing
of re-grazing a paddock when it reaches the point of maximum
average growth rate, between 2- and 3-leaf growth stage for
ryegrass. These estimates point towards large gains in profit being
available in many pasture-based dairy farms by application
of well proven management practices, and by collecting the
relevant data necessary for executing these practices.
The potential benefit of good feed management should be
weighed against the cost of regular paddock assessments and
herbage mass data analysis to improve grazing management
decisions. One estimate of this cost was done by Crawford
et al.(2015) where they found a value of NZ$23/ha.year
20
18
16
30
25
20
15
10
5
14
12
10
8
6
(a)
(b)
(c)
(d)
Month
kg DM/cow.day
kg milk/cow.day
July Sep. Nov. Jan. Mar. May July Sep. Nov. Jan. Mar. May
Perfect knowledge Imperfect knowledge Low knowledge
Fig. 3. (a,c) Weekly average feed eaten and (b,d) milk production for three paddock selection
policies for (a,b) medium input, and (c,d) high input systems for the 2013–2014 climate year.
Value of herbage mass estimates Animal Production Science G
for weekly paddock assessments over 46 weeks. In another
estimate from a New Zealand dairy farm the cost came to
NZ$41/ha. year for a contractor doing a farm drive with an
ATV bike towing a C-dax pasture meter (King et al.2010)
every 10 days over a farm area of 290 ha. This cost includes
a monthly report identifying grazing days per paddock,
recommended rotation cycle based on herbage growth rates,
and paddocks to be closed for silage or hay (Greg McCracken,
pers. comm., December 2015). The same farmer commented
that an employee used to undertake the farm walk with a rising-
plate meter taking ~4.5 h at NZ$30/h, which equates to NZ$17/ha.
year. However, he found data to be inconsistent between
different employees, and the method still required some of his
time for data processing and analysis. A further estimate of costs
was obtained from a business specialising in herbage mass
measurements in the Southland region of New Zealand. Costs
came to approximately NZ$40/ha.year for a visit once a week,
or NZ$30/ha.year for a visit once a fortnight. In this case the
cost includes travel charges and data processing, but the data
report comes with minimal farm management advice (Donald
Martin, pers. comm., December 2015, GrassCo, www.grassco.
co.nz, accessed 22 November 2017). In reality, most farmers
will likely be between the two categories of low and imperfect
knowledge. Also, practical realities dictate that intensity of
paddock monitoring activities will vary depending on seasonal
workload, and the quality of the estimates will also vary
depending on how these are conducted and/or the skill of the
assessor. However, this study indicates that the costs associated
with collecting herbage mass data for informed grazing
management decisions are small in comparison with potential
gains in operating profit.
In the imperfect and low knowledge scenarios, we simulated
feeding policies that would feed flat rates of supplements on
a fortnightly basis during perceived feed deficits. For example,
these managers are not guided by daily post-grazing residuals
to support their supplement feeding decisions. This is an
acceptable simplification given the focus of this study, which
was assessing the value proposition of measuring pre-grazing
herbage mass. Pasture allocation and supplement feeding
based on heuristics using post-grazing residual data is a different
question, and much harder to model, because it is less rule-
based, more ‘intuitive’management styles. Nevertheless, any
knowledge of herbage mass is only one step in the chain
towards assuring constant daily intakes and good pasture
utilisation. It is one of the foundations of the ‘art’of good
pasture management.
Conclusions
The results suggest that herbage mass monitoring and decision
making based on this knowledge could increase farm profitby
11–15% compared with a low knowledge of herbage mass. This
could be achieved notwithstanding an average error of 15% in
herbage mass estimates. The reasons relate to feeding the herd
more uniformly, savings on imported supplements and allowing
better management of surplus herbage. This estimated value
proposition should be considered by farmers when evaluating
the cost of improved herbage mass monitoring and grazing
management and when seeking opportunities to improve farm
profitability.
Conflicts of interest
The authors declare no conflicts of interest.
Acknowledgements
This work was funded by New Zealand dairy farmers through DairyNZ
Inc.: Project System Optimisation FP1419.
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