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Regular estimates of herbage mass can improve profitability of pasture-based dairy systems


Abstract and Figures

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.
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Regular estimates of herbage mass can improve protability
of pasture-based dairy systems
P. C. Beukes
, S. McCarthy
, C. M. Wims
, P. Gregorini
and A. J. Romera
DairyNZ Ltd, Private Bag 3221, Hamilton 3240, New Zealand.
Corresponding author. Email:
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 protability 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 prot 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 prot. 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
Received 20 March 2017, accepted 15 November 2017, published online 5 January 2018
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 protable
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 nancial impact of good
grazing management decisions on a daily basis, time cost of
collecting paddock herbage mass information, data-intensive
decision tools, low condence 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
Animal Production Science
Journal compilation CSIRO 2018
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 benets
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.
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-specied 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 inuences 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-dened 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-dened 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 prot
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 dened 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 20122013 year (DairyNZ Limited
2014), with a milk price of NZ$6.33/kg MS. The 20122013 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 prot 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 knowledgeof 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 ODonovan et al.(2002) who found
estimation errors varying from 9% to 21% for four methods of
herbage mass estimation. In this imperfect knowledgepolicy
the model applies an errorto the actual herbage mass per
paddock to generate an estimated herbage mass that mimics
the collection of herbage mass data on commercial farms.
These errorswere 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 knowledgepolicy 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 DairyNZs
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 identies each paddock, denes the soil type, denes 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 specic
growth conditions for a particular paddock.
For this modelling exercise the 20122013 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 20122013 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 20122013 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 ll herbage decits, mainly in early spring before
the herbage ush, mid-summer when herbage growth is slow in
JanuaryMarch, 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 efuent
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
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
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 efuent 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 20042005 representing a good
herbage yield of ~20 t DM/ha; 20132014 representing
anormalherbage yield of ~17.5 t DM/ha; 20122013 for
apoorherbage 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
decit 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, identication 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 decit. 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
rst 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
reected in the operating prot. In the high input system
supplements were taken from the PKE feed store throughout
the year but never more than 5 kg DM/ Maize silage
was fed mainly in the early and later parts of the season,
AugustOctober and FebruaryMay, but never more than 6.5
kg DM/ 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 ll the rest of the decit, mostly in spring, August and
September, and again in autumn, MarchMay. Similar to the
medium input system all feed stores were topped up at the end of
the year on 31 May and the costs reected in calculations of farm
operating prot.
In contrast to perfect knowledge, in the imperfect and low
knowledge scenarios herbage allowances, decits, 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
decits 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 decits each day.
Therefore, during a perceived feed decit they will feed a at
rate of supplement without adjusting the rate as the magnitude
of the decit 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-dened 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,
reecting substitution rates of supplements ranging in rumen-ll
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 20122013 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 prot.
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 NovemberFebruary 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 reect
a combination of perfect paddock selection and a timely and
accurate surplus herbage policy, where increasing residuals are
identied 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 proles for feed eaten
and milk production with perfect knowledge was the result of
known herbage allowances and therefore perfect allocation of
supplements to ll herbage decits. In contrast, both imperfect
and low knowledge created periods of under- and over-feeding
resulting in uctuating intakes and milk production. Compared
with perfect knowledge as the reference point, the daily intakes
in imperfect and low knowledge scenarios uctuated between
0.8 and 1.16 kg DM/day on average, whereas milk production
uctuated between 0.54 and 1.12 kg/day on average.
Milksolids production and cost of supplements were the
main factors that inuenced operating prot and the differences
predicted for the three paddock selection policies across the three
herbage growth years. On average, prot 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 20122013,
normal 20132014, good 20042005. Operating prot is based on a milk price of NZ$6.33/kg milksolids
Medium input High input
Variable Year Perfect
Annual herbage yield (t DM/ha) Poor 16.7 15.9 15.5 16.1 16.1 15.8
Normal 18.1 17.1 16.9 17.3 17.4 17.2
Good 21.2 20.4 19.5 20.1 20.2 19.8
Average 18.7 17.8 17.3 17.8 17.9 17.6
Supplements fed (t DM/ha) Poor 7.5 7.2 7.2 14.1 14.1 14.0
Normal 6.3 6.4 6.4 14.3 14.3 14.3
Good 3.2 3.3 3.3 10.6 10.6 10.6
Average 5.7 5.6 5.6 13.0 13.0 13.0
Milksolids production (kg/ha) Poor 1287 1267 1211 2087 2054 1944
Normal 1280 1291 1230 2110 2104 2033
Good 1281 1284 1190 2094 2041 1992
Average 1283 1281 1210 2097 2066 1990
Cost of supplements (NZ$/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 prot (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 20042005. Both input systems had the same
higher average prot 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).
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,
~15001700 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 decits 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)
kg DM/ha
Perfect knowledge
Imperfect knowledge
Low knowledge
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
efciency, 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 signicantly increase production potential of the
pasture, with the major benet 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 prot gains are of the same
magnitude as the NZ$360600/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 15001700 kg DM/ha. Chapman (2016) further
estimates that NZ$260480/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 prot 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 benet 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
kg DM/
kg milk/
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 20132014 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., 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 prot.
In the imperfect and low knowledge scenarios, we simulated
feeding policies that would feed at rates of supplements on
a fortnightly basis during perceived feed decits. For example,
these managers are not guided by daily post-grazing residuals
to support their supplement feeding decisions. This is an
acceptable simplication 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 intuitivemanagement 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 artof good
pasture management.
The results suggest that herbage mass monitoring and decision
making based on this knowledge could increase farm protby
1115% 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
Conicts of interest
The authors declare no conicts of interest.
This work was funded by New Zealand dairy farmers through DairyNZ
Inc.: Project System Optimisation FP1419.
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Value of herbage mass estimates Animal Production Science I
... The foundation of a profitable pasture-based dairy farm is the maximum utilisation of the on-farm grown herbage as a source of high quality and low-cost feed (García et al., 2013;Beukes et al., 2019). Evidence from practical farm situations indicates that even under relatively uniform pastures, feed availability for cows can vary by up to 50% from daily intake requirements (Fulkerson et al., 2005;Insua et al., 2019b). ...
... The latter exposes pasture fields to a high grazing pressure leading to suboptimal post-grazing pasture residues reducing pasture regrowth (Fulkerson et al., 2005;Chapman et al., 2012). Accurate daily estimates of pasture feed allocation are therefore critical to maximise the profitability of pasture-based dairy system (Romera et al., 2010;Beukes et al., 2019). Accordingly, Beukes et al. (2019) found that regular estimation of herbage mass can increase farm profit by up to 15%. ...
... Accurate daily estimates of pasture feed allocation are therefore critical to maximise the profitability of pasture-based dairy system (Romera et al., 2010;Beukes et al., 2019). Accordingly, Beukes et al. (2019) found that regular estimation of herbage mass can increase farm profit by up to 15%. However, obtaining an accurate estimate of available grass grown in the paddock with traditional methods such as traversing each paddock with an electronic rising plate meter is a time-intensive exercise in a time-poor industry (García et al., 2013). ...
Accurate daily estimates of pasture biomass can improve the profitability of pasture-based dairy system by optimising input of feed supplements and pasture utilisation. However, obtaining accurate pasture mass estimates is a laborious and time-consuming task. The aim of this study was to test the performance of an integrated method combining remote sensing imagery acquired with a multispectral camera mounted on an unmanned aerial vehicle (UAV), statistical models (generalised additive model, GAM) and machine learning algorithms (random forest, RF) implemented with publicly available data to predict future pasture biomass loads. This study showed that using observations of pasture growth along with environmental and pasture management variables enabled both models, GAM and RF to predict the pre-grazing pasture biomass production at field scale with an average error below 20%. If predictive variables (i.e. post-grazing pasture biomass) were excluded, model performance was reduced, generating errors up to 40%. The post-grazing biomass information at high spatial resolution (<1 m) acquired with the UAV-multispectral camera system was used as predictive variable for future pasture biomass. With the inclusion of the spatially explicit post-grazing biomass variable both models accurately predicted the pre-grazing pasture biomass with an error of 27.7% and 22.9% for RF and GAM, respectively. However, the GAM model performed better than RF in reproducing the spatial variability of pre-grazing pasture biomass. This study demonstrates the capability of statistical and machine learning models implemented with UAV or manually obtained pasture information along with publicly available data to accurately predict future pasture biomass at field and farm scale.
... The impact of variable herbage allocation on milk production and profit has been previously demonstrated, with a more consistent herbage allocation shown to increase milk yield by 9% for cows grazing ryegrass pasture [30]. In a modelling study that investigated different levels of knowledge about pasture mass, Beukes et al. [31] found that annual farm operating profit could be increased by 11-15% if pasture mass could be estimated with an error of 15% of less. The main factor identified as leading to higher farm profit was more accurate herbage allocation which reduced the likelihood of under or over-grazing. ...
... The main factor identified as leading to higher farm profit was more accurate herbage allocation which reduced the likelihood of under or over-grazing. Over 70% of Australian dairy farmers currently make decisions about grazing pastures based on past experience or intuition [32], but as the availability, timeliness and accuracy of pasture measurement tools and devices improve, the uptake of these technologies could assist with more evenly allocating pasture to grazing dairy cows, particularly for farms with larger herd sizes [31,[33][34][35]. ...
Full-text available
The economics of grazing dairy cows offered a range of herbage allowances and fed supplements as a partial mixed ration (PMR) were examined where profit was defined as the margin between total milk income and the cost of pasture plus PMR supplement. The analysis made use of milk production and feed intake data from two dairy cow nutrition experiments, one in early lactation and the other in late lactation. In early lactation and at a PMR intake of 6 kg DM/cow per day, the profit from the cows with access to a medium herbage allowance (25 kg DM/cow per day) was AUD 1.40/cow per day higher than that for cows on a low allowance (15 kg DM/cow per day). At a higher PMR intake of 14 kg DM/cow per day, the profit from the cows on a medium herbage allowance was AUD 0.45/cow per day higher than the cows on a low allowance; there was no additional profit from increasing the herbage allowance from medium to high (40 kg DM/cow per day). In late lactation, the profit from the cows fed a PMR with a medium herbage allowance (20 kg DM/cow per day) was only higher than the cows on a low allowance (12 kg DM/cow per day) when the PMR intake was between 6 and 12 kg DM/cow per day. There was also a difference of AUD +0.50/cow per day between the PMR with medium and high herbage allowance (32 kg DM/cow per day). It was concluded that farmers who feed a PMR to dairy cows should offer at least a medium herbage allowance to optimize profit. While feeding additional PMR increases milk production and profit, further gains would be available by offering a higher herbage allowance. These findings provide an estimate of the net benefits of different herbage allowances when feeding a PMR and will enable farmers to manage their feeding systems more profitably.
... A universal issue affecting grazing livestock farmers is how to efficiently and accurately measure and allocate their pasture to optimise utilisation on a day-to-day basis. By frequently monitoring of pasture availability, the optimum time point for grazing can be identified, decisions made about paddocks to use for forage conservation, and timings set for applications of agri-chemicals further in advance: all leading to increased pasture utilisation and economic benefit (Beukes et al., 2019). Since destructively measuring vegetative mass is laborious and time consuming, measurement using non-destructive techniques has gathered much attention since the 1980 ′ s, initially in arable industries (Mulla, 2013) but increasingly for pasture (Ali et al., 2016). ...
Full-text available
Ultrasonic sensors are a tool for pasture biomass measurement but there has been a lack of on-farm validation to prove their practicality and applicability. As a result, uptake of such sensors is still low. This study sought to perform a multi-site and multi-season calibration and validation of a wide-angle, vehicle-mounted, ultrasonic sensor gathering data while the unit was used in motion on seven commercial dairy farms. This was facilitated by the integration of the ultrasonic sensor, a Real-Time Kinematic Geographic Positioning System (RTK-GPS), and a datalogger into a single system that was attached via a custom bracket to the bull bar of an all-terrain vehicle. The resulting dataset was gathered over a 12-month period, and comprised 1886 reference biomass values from destructive cutting of perennial ryegrass pastures, accompanied by ultrasonic sensor height readings and compressed height readings from a rising plate meter (RPM): a tool that is widely used on commercial farms. The final root mean square errors of prediction (RMSEP) were 1034 and 904 kg DM/ha for Ultrasonic sensor and RPM respectively across all sites and seasons. These were considered high in comparison to previous literature, but this could be attributed to the very broad applicability of the models created. It was also demonstrated that the ultrasonic instrument could be used to create accurate biomass maps for paddocks in the same time taken to survey a whole paddock by RPM, a method that only results in a single paddock average value. Considering the ultrasonic sensor was only slightly less precise and accurate than the RPM at estimating biomass, but considerably more functional in its ability to determine within paddock biomass variation, it was concluded that the ultrasonic sensor was the more informative option.
... Monitoring and predicting above ground biomass yield are of key importance for crop management. According to Beukes et al. [36], a 15% increase in farm profitability could be achieved by carrying out regular herbage measurements [37]. ...
Full-text available
In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.
... The value of increasing grass utilisation has been estimated to be up to €173 tonne −1 ha −1 year −1 [17]. Frequent and accurate measurement of grass quantity and quality is one of the main methods of maximising grass utilisation and production on pasture-based farms [20,22,23]. Optimal grassland management is highly dependent on the accuracy of information on pasture quantity and quality that is available to the farmer [16,24]. ...
Full-text available
The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.
... Variability in Irish perennial rye grass (PRG) swards, in terms of HM, can range between 15 and 36% and has been reported to increase as a result of animal interaction, management and seasonal factors (Jordan et al. 2003;. Beukes et al. (2019) found that farm profitability can be improved by 15% by carrying out regular pasture measurements, which in turn leads to increased feed consistency, more optimum pasture management and reduced feed imports. Irish studies have indicated that every additional tonne of fresh grass utilised by the herd on an annual basis is worth in the range of €160-€278 (Dillon 2011;Hanrahan et al. 2018). ...
Full-text available
Efficient grass-based livestock production depends on precise allocation of pasture to the herd in the form of herbage mass (HM). Accurate measurement of HM results in increased utilisation of grass in the herd’s diet and consequently reductions in whole-farm feed inputs, emissions and costs. The rising plate meter (RPM) is an established method of estimating HM, but there is scope to improve its accuracy. Real-time meteorological data and pasture management information have never been analysed in combination with the RPM. This study aimed to utilise such data to improve the accuracy of HM prediction using multiple linear regression (MLR) and machine learning through the random forest (RF) algorithm. Seventeen variables were assessed and models were evaluated in terms of relative prediction error (RPE). Decreases of 6–12% RPE were observed for the MLR models compared with conventional models. Further decreases of 11–17% were recorded for RF models. An MLR model comprising of management data that were readily available to farmers was deemed optimum for on-farm use and included coefficients for: compressed sward height (mm), nitrogen fertiliser rate (kg ha⁻¹) and grazing rotation number (RMSE = 324 kg DM ha⁻¹). The addition of meteorological variables resulted in a further 0.9% decrease in RPE (RMSE = 312 kg DM ha⁻¹), but was not practical considering the expense of on-farm meteorological sensors. The RF model with meteorological variables (RMSE = 262 kg DM ha⁻¹) had 1.5% lower RPE compared with the RF model without (RMSE = 243 kg DM ha⁻¹).
... Accurate measurement and allocation of fresh pasture to the grazing herd on a daily basis is essential in increasing the efficiency and profit of grass based livestock systems (Zegler et al. 2020). A New Zealand-based study conducted by Beukes et al. (2019) found that conducting regular grass measurements can improve farm profits by up to 15% through increased feeding consistency, reduced feed imports and improved grassland management. Financial studies carried out in Ireland on the benefits of improving grassland measurement and management, have placed a value on increasing annual fresh grass utilisation in a grazing herd's diet to be in the range of €160 ha −1 to €278 ha −1 (Hanrahan et al. 2018;Dillon, 2011). ...
Full-text available
Accurate estimation of herbage mass (HM) is essential for optimising grass utilisation and increasing profit for pasture-based livestock agriculture. The rising plate meter (RPM) is used for predicting HM based on average compressed sward height (CSH). Sampling resolution and distribution are primary parameters in determining spatial heterogeneity of HM. There is no definitive sampling protocol for the RPM. The objectives of this study were to: investigate spatial variation of HM within pastures, determine the number of RPM measurements required to accurately predict mean HM, and assess the precision of the RPM in terms of measurement repeatability. Intensive CSH measurements and HM reference cuts were carried out on controlled plots and grazed paddocks over two grazing seasons. Sward heterogeneity was estimated as the coefficient of variation (CV) of CSH and compared to empirically derived ‘true’ sward heterogeneity in terms of HM CV. Retrospective analysis simulations were performed to identify the effect of various reduced measurement resolutions on estimated mean CSH error. Repeated measures analysis was performed on grass samples to determine RPM measurement system precision. Results indicated that pasture heterogeneity varied by 36% across the growing season and was affected by grazing, fertilisation, sward composition and seasonality. Mean CSH could be estimated to within 5% relative prediction error by recording 24 measurements per ha in a random stratified manner. The standard deviation of RPM measurement repeatability was calculated to be 4.34 mm. The findings of this study will be used to inform the implementation of a more optimum grass measurement protocol.
... Accurately predicting fresh grass quantity, in terms of herbage mass (HM) (kg DM ha − 1 ), is essential in allocating the correct quantity of grass to the herd on a daily basis and in maintaining high levels of utilisation (Delaby et al., 1998;Dillon, 2006). Beukes et al., (2019) reported that a 15% increase in farm profitability could be achieved by carrying out regular pasture measurements. Dillon (2011) and Hanrahan et al., (2018) estimated that every additional tonne of HM utilised by grazing is worth between €160 -€278. ...
Full-text available
Accurate and efficient estimation of herbage mass is essential for optimising grass utilisation and increasing profit for pasture farming. There is no definitive sampling protocol for grass measurement on Irish pastures. This paper presents the Grass Measurement Optimisation Tool (GMOT), designed to generate measurement protocols that optimise for time and accuracy. The GMOT was designed in the form of a decision support tool that generates interactive paddock maps that guide the farmer on how to optimally measure their pastures in a random stratified manner based on GPS co-ordinates, resulting in accurate non-biased estimations of mean herbage mass. Rising plate meter (RPM) measurements and reference herbage cuts were performed on trial plots and grazed paddocks over three years. Measurement routes were optimised using a genetic algorithm based on a traveling salesman problem. Actual survey error was estimated in terms of relative prediction error using Monte Carlo simulations that combined measurement and calibration error distributions for the RPM. Cost benefit analysis was conducted to evaluate the feasibility of using the GMOT on Irish grasslands. Actual error for the RPM decreased from 37% to 26% as measurement rates increased from 1 to 8 /ha and reductions in error were negligible (<1%) as measurements increased from 8 to 32 /ha. Calibration error was the largest source of error (25.9%) compared to measurement error (8%). Optimal measurement value was achieved by performing 8 measures/ha and further increasing the measurement rate resulted in diminishing returns. The GMOT is compatible with a range of pasture measurement technologies.
Measurement of pasture biomass is useful to farmers, as it enables timely and accurate management decisions. Satellite pasture measurement allows this information to be obtained with minimal time and labour on the part of the farmer. However, the accuracy of satellite measurements for high levels of pasture biomass can be impacted by a phenomenon called saturation, in which the response of the satellite estimate to increased biomass is diminished in situations of high biomass. In this investigation, a statistical pasture growth model was combined with satellite pasture measurements, with the aim of mitigating the effect of saturation on estimation accuracy. Data were captured for five farms, across two regions and an 18–21 month measurement period. Where satellite measurements appeared to be saturated, the growth model estimate was substituted. This process resulted in improved accuracy (R2 improved from 0.672 to 0.703; RMSE improved from 334 to 309 kg DM/ha; and average bias improved from -62 to -9 kg DM/ha). The statistical improvements were more pronounced where terrestrial estimates were higher so the impact of saturation would be greatest. These results indicate that the problem of saturation in satellite pasture measurement can be addressed by the incorporation of modelled data. Prior research has predicted that improved accuracy of pasture measurement would be associated with increased profitability, and this work helps achieve that goal for farmers using satellite measurement services.
Measurement and monitoring of pasture have been identified as foundations for profitable and sustainable grazing systems. The value that farmers place on pasture assessment in feed management is difficult to ascertain and has seen limited research. The objectives of this study were to test a survey to quantify the perceived value of pasture assessment and identify key criteria for design of pasture assessment technologies. An online survey methodology was piloted with 44 New Zealand farmers to assess perceptions of actual and great grazing management outcomes, good and great pasture assessment, and the value associated with moving from good to great pasture assessment. Results highlighted that many farmers perceive a small potential for improvements in their current pasture performance, whereas industry-level studies suggest that this is not the case. We found limitations with farmers linking better pasture management performance with eventual improvements in milk production. There were anomalies with assessing current and potential improved pasture performance through this type of survey methodology, with many farmers claiming very high levels of current performance, and some rating themselves as performing at more than 100% of potential. This research highlights that pasture assessment technology designers need to be aware of the high expectations of farmers regarding performance, for example measurement accuracy and data timeliness. Over, or under, specification of technology for specific tasks, such as daily allocation of pasture at a herd level, may lead to farmer dissatisfaction around costs of technology, return on investment, and if the technology is fit-for-purpose.
Full-text available
Information on the physiological ecology of grass-dominant pastures has made a substantial contribution to the development of practices that optimise the amount of feed harvested by grazing animals in temperate livestock systems. However, the contribution of ecophysiology is often under-stated, and the need for further research in this field is sometimes questioned. The challenge for ecophysiolgists, therefore, is to demonstrate how ecophysiological knowledge can help solve significant problems looming for grassland farming in temperate regions while also removing constraints to improved productivity from grazed pastures. To do this, ecophysiological research needs to align more closely with related disciplines, particularly genetics/genomics, agronomy, and farming systems, including systems modelling. This review considers how ecophysiological information has contributed to the development of grazing management practices in the New Zealand dairy industry, an industry that is generally regarded as a world leader in the efficiency with which pasture is grown and utilised for animal production. Even so, there are clear opportunities for further gains in pasture utilisation through the refinement of grazing management practices and the harnessing of those practices to improved pasture plant cultivars with phenotypes that facilitate greater grazing efficiency. Meanwhile, sub-optimal persistence of new pastures continues to constrain productivity in some environments. The underlying plant and population processes associated with this have not been clearly defined. Ecophysiological information, placed in the context of trait identification, grounded in well-designed agronomic studies and linked to plant improvements programmes, is required to address this.
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The effect of herbage allowance (20, 30, 40, 50, 60 and 70 kg DM/cow. day) on the consumption of nutrients from herbage and milk production by cows in early lactation, was examined. The experiment was conducted on rainfed perennial ryegrass pastures in September and October 1997 in south-eastern Victoria, Australia. The herbage on offer comprised 64% perennial ryegrass, 21% other grasses, 1% white clover, 5% weeds and 9% dead material on a dry matter (DM) basis. The average pregrazing herbage height was 13 cm, at an estimated pregrazing herbage mass of 3.6 t DM/ha. The herbage on offer was of high quality containing 11.6 MJ metabolisable energy/kg DM, 202 g crude protein/kg DM and 525 g neutral detergent fibre/kg DM. Concentrations of calcium, magnesium, sodium, potassium, phosphorus, sulfur and chloride were 4.4, 2.2, 4.4, 31.2, 3.5, 2.7 and 11.4 g/kg DM, respectively. As daily herbage allowance per cow increased, dry matter intake increased curvilinearly (P<0.01) from 11.2 to 18.7 kg DM/cow. day. This was associated with a decrease in utilisation of herbage from 54 to 26% and an increase in milk production from 25.9 to 29.1 kg/cow. day. The cows on all treatments grazed for less than 8.7 h/day. The increase in intake was achieved by an increase in the rate of herbage intake from 1.5 to 2.2 kg DM/h for herbage allowances of 20 and 70 kg/, respectively. Irrespective of herbage allowance, cows selected a diet that was approximately 10% higher in in vitro dry matter digestibility and 30% higher in crude protein than that in the herbage on offer. The neutral detergent fibre content of the herbage selected was lower (P<0.05) than that on offer. The herbage consumed contained more (P<0.05) magnesium, potassium and sulfur, the same amount of calcium and phosphorus and less (P<0.05) sodium and chloride than the herbage on offer. For rainfed perennial pastures in spring, herbage allowance is an important factor in determining voluntary feed intake and production of dairy cows. To achieve 30 L from herbage, without supplementation, high herbage allowances are required. The increase in herbage intake, with increasing herbage allowance, resulted from an increase in rate of dry matter intake and not an increase in grazing time. No relationship was evident between herbage allowance and the selection differentials for in vitro dry matter digestibility, crude protein and neutral detergent fibre. Selection differentials for rainfed perennial pastures in spring are similar to those reported for irrigated perennial pastures in northern Victoria in spring and autumn. When determining nutrient requirements it is important to consider the interaction between herbage intake and nutrient concentration in the herbage.
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The DairyNZ whole farm model (WFM) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692 kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user.
Improved efficiency in growing and converting pasture into product is required to maintain New Zealand's competitive advantage in dairying. This study focused on two areas of grazing management, the first an assessment of the indicators leaf stage, pre-grazing yield and grazing residual. In summary, 49% of measured paddocks were grazed too soon based on leaf stage, 62% were grazed outside the recommended pre-grazing yield, and 48% of measured paddocks were not grazed to a desirable height. The second part of the study provided an insight into farmer decision making at an operational level of grazing management with three key components identified. These were: 1) The recruitment of paddocks into a grazing plan; 2) The shuffling of the paddock grazing sequence within the grazing plan; and 3) The management of individual grazing events before, during and after the event. An improved understanding by rural professionals of grazing management decision making would result in extension strategies which generate increased farmer engagement, adoption of grazing management technologies and improved onfarm productivity. Keywords: dairy, grazing management
The objective of this study was to compare the accuracy of four non-destructive techniques for estimation of herbage mass (HM) in swards rotationally grazed by dairy cows. Visual estimation (VE) of HM, a rising plate meter (RPM), the Hill Farm Research Organisation sward stick (SS) and the pasture probe capacitance meter (PPCM) were evaluated. Estimates were obtained on five occasions (July, September and November, 1997 and April and May, 1998). Mean dry matter (DM) yields (kg/ha) of available herbage cut to 40 mm above ground level (AH) and total herbage cut to ground level (TH) were 2161 (s.d. 850.2) and 2762 (s.d. 890.9) kg, respectively. Relationships were obtained for VE, RPM, SS and PPCM with AH, and for RPM, SS and PPCM with TH. For AH, the proportions of variance (R2) accounted for by the model were 0.95, 0.94, 0.92 and 0.72 for VE, RPM, SS and PPCM, respectively. The corresponding residual s.d. values were 193, 222, 249 and 458 kg DM/ha, and the coefficients of variation (CVs) were 9%, 10%, 12% and 21%, respectively. For TH, the R2 values were 0.88, 0.87 and 0.76 for RPM, SS and PPCM, respectively. The corresponding residual s.d. values were 318, 331, and 442 kg DM/ha, and the CVs were 12%, 12%, and 16%, respectively. It is concluded that VE was the most accurate method of HM prediction and that the PPCM method was an inaccurate predictor of HM.
Production and profit in grazed systems remain inherently constrained by the fundamental trade-off between maximising individual herbage intake and pasture utilisation. The primary objective of this paper is to investigate the trade-off between herbage intake per cow and intake per hectare, from the perspective of economic optimisation, for an intensive pasture-based dairy farm in New Zealand (NZ). A detailed optimisation model of a dairy farm is applied, to allow the complex animal-plant-supplement dynamics underlying this relationship to be explicitly considered. Model output confirmed the existence of the fundamental inverse relationship between individual cow intake and herbage utilisation, which arises from the underlying biophysical dynamics within a grazing system, in the context of pasture-based NZ dairy farms. Results indicated that profitable management relies on increasing total pasture eaten (grazing plus pasture silage harvested on farm) through the use of a relatively high stocking rate and moderate levels of pasture intake per cow. Indeed, for 450 kg liveweight cows, optimal pasture intake per cow is 5 t dry matter (DM) per cow for per hectare intakes of 12-16 t DM/ha and 4.5 t DM/cow for a per hectare intake of 17 t DM/ha. Thus, a goal to maximise both individual intake and herbage utilisation in grazing systems is misinformed; it is the latter that is principally important to farm profit. Indeed, seeking to maximise both simultaneously is not possible, and trying to maximise individual intake can impose an enormous cost. However, while high herbage utilisation increases profit, this can also be associated with greater nutrient leaching, reinforcing the importance of considering the environmental impacts of grazing management.
Knowing the amount of herbage mass available on the farm (ideally measured weekly) is an important step in achieving high pasture utilization on pastoral dairy farms in New Zealand, but the information must be used in a timely manner to make efficient management decisions. However, most New Zealand dairy farmers do not measure their pastures regularly. This project aimed to develop a simple alternative, in the form of a prototype software tool (Pasture Growth Simulation Using Smalltalk, PGSUS) to predict herbage mass at an individual paddock level, which reduces (not eliminates) the requirement for physical data collection and provides more information from the measurements taken. The main data requirements are pasture herbage mass for each paddock and grazing or cutting events. A climate-driven pasture simulation model is used to predict herbage mass between intermittent pasture measurements. The pasture model contains certain empirical parameters that are fitted to the observed data for each paddock individually, using all the previous data to “train” the model. PGSUS requires daily weather data, including mean, minimum and maximum air temperature, solar radiation, rain and potential evapotranspiration. Data from the Virtual Climate Station Network (VCSN) from NIWA (National Institute of Water and Atmospheric Research Ltd., New Zealand) are used to drive the model. Preliminary testing was done on two commercial dairy farms, one in the Waikato (North Island) and the other in the Canterbury (South Island) regions of New Zealand. Up to 28 days without measurements, PGSUS estimated herbage mass with correlation of approximately 0.9 and with small bias.
The dairy industry in New Zealand is a significant contributor (7% of GDP) to the national economy. Major current issues for the industry are the lack of cheap, highly‐nutritive value feed for cows of high genetic merit for milk yield; the poorer reproductive capacity of these high merit Holstein‐Friesian cows; the quantity and skill of farm labour; and the environmental consequences of intensive dairy farming. The industry has responded to these challenges in the following ways. Increased nitrogen fertiliser use has given increased pasture yields, but also increased nitrate leaching and nitrous oxide emission from farms, which in turn has led to research on wintering pads, diet modification and nitrification inhibitors to reduce N losses to the environment. Increased use of supplementary feeds has given improved milk yield per cow, but also increased total farm variable costs, labour requirements and nutrient losses, which in turn have fostered research for cheaper feeds and rumen modifiers to improve feed utilisation. The poorer reproductive performance of Holstein‐Friesian cows with overseas genetics initially led to greater reproduction intervention treatments, but a greater awareness of welfare issues has encouraged increased use of crossbred Holstein‐Friesian × Jersey cows to improve reproductive performance through heterosis. The use of larger herds and dairies initially allowed more efficient use of labour, but continuing problems with the cost and availability of labour has seen the adoption of once‐daily milking on some farms and the experimental evaluation of automatic milking systems for pastoral systems. Future developments will include continued improvements in both quantity and quality of feed for cows of higher breeding worth, but more emphasis will be placed on traits such as feed conversion efficiency, health and survival in the herd. Automation of all farm tasks to reduce labour costs will be a major feature of future farms. Costs of environmental compliance will increase in the short‐term until research delivers ways to reduce the carbon and nitrogen losses from grazing and cropping systems.