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FEATURED GOLDSIM APPLICATION
Use of GoldSim in Animal Nutrition Research
Dr David Pacheco
Metabolism & Microbial Genomics
AgResearch Ltd. Grasslands Research Centre. Private Bag 11008
Palmerston North, New Zealand,
david.pacheco@agresearch.co.nz
Background
New Zealand dairy products account for 19% of the value of
the country's exports. This high-value industry relies heavily
on the ability of dairy cows to utilise forages as their main
dietary staple. This is a big contrast to dairy operations
in North America, where cows are fed grains and conserved
forages of known chemical composition. In grazing systems,
the quality of the pasture is an ever-changing playing field:
weather, forage variety, level of fertilisation, and age of
the plant are some of many factors influencing the quality
of the cows' diet. The chemical composition of the forage,
and hence its nutritive value, change from season to season,
from paddock to paddock, from day to day and even within the
same day!
Because experimentation in animal nutrition can be expensive
and time consuming, a way to maximise chances of success in
our research projects is through simulation of nutritional
scenarios. In this example, GoldSim has been used to simulate
changes in the pasture chemical composition and their effects
on the digestion by cows. In particular, this simulation aims
to replicate what happens in the forestomach of a lactating
cow, and how the fermentation of the forage affects the amount
of protein available to the cow for productive purposes.
Cows are "ruminants". In common terms, ruminants
are animals that "chew the cud". They differ from
other animals because part of the digestion process in done
by microbia living in association within the cows. Thanks
to the microbia, ruminants can digest fibrous vegetal material
(like grasses) to obtain energy and protein.
In a simplified way, the bugs in the forestomach of the cow
can provide protein to their host as long as there is a source
of energy and nitrogen available. The more the energy available,
the better the bugs grow and the more the protein the cow
gets for her own use. If there is too much nitrogen relative
to the energy, then the bugs cannot make protein and the excess
of nitrogen is wasted as ammonia. Once absorbed into the bloodstream
ammonia can be toxic for animals. The cow liver is good at
inactivating this toxin by transforming it into urea, which
travels in the blood and is excreted by the kidney (in urine)
and the udder (in milk). Hence, milk can be used as an indicator
of how much nitrogen is being wasted in the rumen… which
is good, because direct measurements of the microbial activity
are technically difficult (particularly when cows are grazing
on a paddock!).
Simulation of Nitrogen Digestion in the Cow
Using GoldSim
A static model of cow digestion (National Research Council
Dairy Nutrient Requirements, 2002) was coded into an Excel
spreadsheet. To test the model, experimental data on forage
analysis and feed intake over a period of 9 weeks was used.
A GoldSim model was built with three containers:
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"DietInfo"' container was used to store all
the information on the diet;
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"NRCcontainer" consisted of a Spreadsheet Element
linking the inputs from "DietInfo" to the Excel
spreadsheet with all the calculations for the static model
of digestion; and
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"Results"' container was used to keep all the
graphical output of interest.
Figure 1. Top level of the simulation model.
The known chemical composition of pasture and feed intake
(2 weekly measurements) were entered in Information Time Series
elements. Using these elements it was possible to make the
static model behave like a dynamic simulation over time. The
data set used contained information representing the average
pasture quality of a given paddock… but there is variation
among areas in the paddock, and also between what the researcher
sampled and what the animals actually ate. To simulate this
variation, the Information Time Series data were used as the
mean of distributions defined by Stochastic elements.
Generally speaking, high values of fibre correspond to low
values of protein (CP) and sugars (SSS) in forages. This was
simulated by using correlations between the stochastic elements
and fibre (ADF) to mimic the interrelationships observed in
the real pasture. For all stochastic elements, a normal distribution
of values with means given by the observed chemical composition
was assumed.
The spreadsheet element received the information generated
by the Stochastic elements and changed the values of diet
composition and intake every day over the length (63 d) of
the simulation.
The simulation was set up to run for 50 realizations, which
included 63 days, with triggering of the Stochastic elements
every day.
The spreadsheet model estimated the amount of nitrogen the
microbia could utilise to synthesise protein according to
the chemical composition of the pasture. Any difference between
the supply of nitrogen and the microbial capacity defined
by the energy of the diet was calculated to be the "nitrogen
balance" in the forestomach of the cow. Positive balance
(i.e. more supply than what the bugs needed) was assumed to
be nitrogen excess.
Figure 2. Simulated nitrogen balance in the forestomach
of the cow.
The simulated digestion results were compared to the experimental
data on milk urea. If the model was predicting in a sensible
way, it could be expected to find positive association between
the nitrogen given in excess and the concentration of milk
urea. In general, the upward trend on the simulated nitrogen
balance in the forestomach of the cow follows the trend on
the observed milk urea values for the herd (Figure 3).
Figure 3. Plot of observed milk urea (red) and simulated
nitrogen balance (black) from digestion of forage.
The simulated values appear to be a sensible representation
of what happened as part of the digestion of the forage. This
initial assessment will be confirmed with further experimental
data and then the model will be updated as required. Further
work is intended to test the model using more frequent forage
sampling. More intensive research could also be used to generate
better estimates of forage intake to run this model.
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