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

11 CSIRO Livestock Industries, Rockhampton, Australia. CSIRO Livestock Industries, Rockhampton, Australia.

22 BioSS King’s Building, Edinburgh University, UK. BioSS King’s Building, Edinburgh University, UK.

33 Farrer Centre, Charles Sturt University, Wagga Wagga, Farrer Centre, Charles Sturt University, Wagga Wagga, Australia.Australia.

4 4 SAC Animal Biology Division, Edinburgh, UK.SAC Animal Biology Division, Edinburgh, UK.

•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:

•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:

Global grazing systems, cows and grass:

Grass (g) Growth• Growth rate ()

Cows (c) Graze• Bite rate ()• Move rate ()• Avoidance rate ()

Understanding the spatial and temporal hierarchy:

Bite

Second

Patch

Field

Farm

Week Year

Spa

tial S

cale

Temporal Scale

Linking processes across scales

Bite

Second

Patch

Field

Farm

Week Year

DiseaseRejected areas Climate

System Drivers

Conservedforage

Vaccination

Stockingrate

Managem

entSpa

tial S

cale

Temporal Scale

Measure Understand Predict

Model

•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:

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

0.00

0.02

0.04

0.06

0.08

0.10

0.12

04 Sep 05 Sep 06 Sep 07 Sep 08 Sep 09 Sep

Alk

ane

con

cen

trat

ion

Cow 1

Cow 2

Cow 3

Cow 4

0

10

20

30

40

50

05 Sep 06 Sep 07 Sep 08 Sep

Ave

co

nta

ct p

er v

isit

(se

c)

6

7

8

9

10

11

12

13

14

15

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

0.00

0.02

0.04

0.06

0.08

0.10

0.12

04 Sep 05 Sep 06 Sep 07 Sep 08 Sep 09 Sep

Alk

ane

con

cen

trat

ion

Cow 5

Cow 6

Cow 7

Cow 8

0

10

20

30

40

50

05 Sep 06 Sep 07 Sep 08 Sep

Ave

co

nta

ct p

er v

isit

(se

c)

6

7

8

9

10

11

12

13

14

15

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

Linking local events to landscape processes:

Linking processes across scales

Bite

Second

Patch

Field

Farm

Week Year

DiseaseRejected areas Climate

System Drivers

Conservedforage

Vaccination

Stockingrate

Managem

entSpa

tial S

cale

Temporal Scale

Laser and GPS tracking:

Varying scales of grazing behaviour:

Genotypic variation:

Genotypic variation:

Measure Understand Predict