Modelling and monitoring the Modelling and monitoring the foraging strategies of foraging strategies of ruminants ruminants Dave Swain Dave Swain 1 , Glenn Marion , Glenn Marion 2 , Dave , Dave Walker Walker 2 , Michael Friend , Michael Friend 3 and Mike and Mike Hutchings Hutchings 4 1 CSIRO Livestock Industries, Rockhampton, CSIRO Livestock Industries, Rockhampton, Australia. Australia. 2 BioSS King’s Building, Edinburgh University, UK. BioSS King’s Building, Edinburgh University, UK. 3 Farrer Centre, Charles Sturt University, Wagga Farrer Centre, Charles Sturt University, Wagga Wagga, Australia. Wagga, Australia. 4 4 SAC Animal Biology Division, Edinburgh, UK. SAC Animal Biology Division, Edinburgh, UK.
Transcript
Modelling and monitoring the Modelling and monitoring the foraging strategies of ruminantsforaging strategies of ruminants
Dave SwainDave Swain11, Glenn Marion, Glenn Marion22, Dave Walker, Dave Walker22, , Michael FriendMichael Friend33 and Mike Hutchings and Mike Hutchings44
•Summary of spatial grazing Summary of spatial grazing modelmodel
•Experimental methodsExperimental methods
•Linking model and Linking model and experimental data to estimate experimental data to estimate grazing parametersgrazing parameters
•Where next?Where next?
Overview:
Starting point is the non-spatial deterministic foraging model:
m x0
a
1 ( )
c gd gt
g g ggd
Rate of change in grazing resource
density =
Logistic growth of resource
- Foraging resource
removal rate
Can we capture the spatial grazing selection and the temporal grass
growth and does it affect the system dynamics?
A spatial foraging model:
max
0
i
Patch i:
Growth rate: g 1
Bite rate: i i
i
c g g
g g
Search rate: i jc g
Comparing spatial and non-spatial models:
Summary of modelling:
Model captures spatial constraints of grazing systems.
The model is driven by behavioural description.
Behaviour is formulated within a stochastic framework using Markov process structure.
The model captures grazing behaviour as a two stage response: • Current patch biting decision• Next patch movement decision
•BackgroundBackground
•Summary of spatial grazing Summary of spatial grazing modelmodel
•Experimental methodsExperimental methods
•Linking model and Linking model and experimental data to estimate experimental data to estimate grazing parametersgrazing parameters
•Where next?Where next?
Overview:
Measuring investigative and grazing activity in dairy cows:
Investigating contaminated patches:
Dry cows
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04 Sep 05 Sep 06 Sep 07 Sep 08 Sep 09 Sep
Alk
ane
con
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Cow 1
Cow 2
Cow 3
Cow 4
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Ave
co
nta
ct p
er v
isit
(se
c)
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Sw
ard
hei
gh
t (c
m)
Cow 1
Cow 2
Cow 3
Cow 4
Treatment
Control
Sward height and contact at contaminated patches
Grazing of contaminated patches
Investigating contaminated patches:
Milking Cows
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04 Sep 05 Sep 06 Sep 07 Sep 08 Sep 09 Sep
Alk
ane
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Cow 5
Cow 6
Cow 7
Cow 8
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05 Sep 06 Sep 07 Sep 08 Sep
Ave
co
nta
ct p
er v
isit
(se
c)
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Sw
ard
hei
gh
t (c
m)
Cow 5
Cow 6
Cow 7
Cow 8
Treatment
Control
Sward height and contact at contaminated patches
Grazing of contaminated patches
Summary of experimental data:
• Behavioural (event)Behavioural (event) and and sward (state) sward (state) measurements.measurements.
•Exact time and duration of each visit to Exact time and duration of each visit to each contaminated plot by each individual each contaminated plot by each individual animal (active transponder data).animal (active transponder data).
• Proportion of contaminated sward Proportion of contaminated sward consumed by each individual animals consumed by each individual animals (alkane data).(alkane data).
•Sward height of contaminated and non-Sward height of contaminated and non-contaminated areas at set time intervals.contaminated areas at set time intervals.
•BackgroundBackground
•Summary of spatial grazing Summary of spatial grazing modelmodel
•Experimental methodsExperimental methods
•Linking model and Linking model and experimental data to estimate experimental data to estimate grazing parametersgrazing parameters
•Where next?Where next?
Overview:
Parameter estimation:
• Set up method to link the experimental (D) and modelling data sets.
• Utilise stochastic methodology.
• We could calculate the probability of model parameters if complete history (H) was known.
• We only observe incomplete history (D).
• Therefore must integrate over all histories consistent with the data (D) using a stochastic integration method e.g. MCMC.
MCMC parameter estimation, is the experimental data useful?
Estimating parameters using the model and experimental data:
Summary of work to date:
•Spatial constraints are important in Spatial constraints are important in grazing systems.grazing systems.
•Innovation in modelling and Innovation in modelling and experimental methods has added experimental methods has added value to the understanding of grazing value to the understanding of grazing systems.systems.
•The interaction between modellers The interaction between modellers and biologists has provided a and biologists has provided a framework to question the basic framework to question the basic drivers of grazing systems.drivers of grazing systems.
Overview:
•BackgroundBackground
•Summary of spatial grazing Summary of spatial grazing modelmodel
•Experimental methodsExperimental methods
•Linking model and Linking model and experimental data to estimate experimental data to estimate grazing parametersgrazing parameters
•Where next?Where next?
Where next?
•Extension of experimental methods Extension of experimental methods e.g. measure numbers of bites, larger e.g. measure numbers of bites, larger scale experimentsscale experiments
•Explore the predictive capabilities Explore the predictive capabilities of the model e.g. intakeof the model e.g. intake
•Develop a better understanding of Develop a better understanding of the impacts of scale e.g. bite rate or the impacts of scale e.g. bite rate or search distancesearch distance
