1. Modelling and monitoring the foraging strategies of ruminants Dave Swain 1, Glenn Marion 2, Dave Walker 2, Michael Friend 3 and Mike Hutchings 4 1 CSIRO Livestock Industries, Rockhampton, Australia. 2 BioSS Kings Building, Edinburgh University, UK. 3 Farrer Centre, Charles Sturt University, Wagga Wagga, Australia. 4 SAC Animal Biology Division, Edinburgh, UK.

2. BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next? Overview:

3. BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next? Overview:

4. Global grazing systems, cows and grass: Grass ( g ) Growth Growth rate ( ) Cows (c) Graze Bite rate ( ) Move rate ( ) Avoidance rate ( )

5. Understanding the spatial and temporal hierarchy: Bite Second Patch Field Farm WeekYear Spatial Scale Temporal Scale

6. Linking processes across scales Bite Second Patch Field Farm WeekYear DiseaseRejected areasClimate System Drivers Conserved forage Vaccination Stocking rate Management Spatial Scale Temporal Scale Measure Understand Predict Model

7. BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next? Overview:

8. Starting point is the non-spatial deterministic foraging model: Can we capture the spatial grazing selection and the temporal grass growth and does it affect the system dynamics?

9. A spatial foraging model:

10. Comparing spatial and non-spatial models:

11. 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

12. BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next? Overview:

13. Measuring investigative and grazing activity in dairy cows:

14. Investigating contaminated patches: Dry cows Sward height and contact at contaminated patches Grazing of contaminated patches

15. Investigating contaminated patches: Milking Cows Sward height and contact at contaminated patches Grazing of contaminated patches

16. Summary of experimental data: Behavioural (event) and sward (state) measurements. Exact time and duration of each visit to each contaminated plot by each individual animal (active transponder data).Exact time and duration of each visit to each contaminated plot by each individual animal (active transponder data). Proportion of contaminated sward consumed by each individual animals (alkane data). Proportion of contaminated sward consumed by each individual animals (alkane data). Sward height of contaminated and non- contaminated areas at set time intervals.Sward height of contaminated and non- contaminated areas at set time intervals.

17. BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next? Overview:

18. 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.

19. MCMC parameter estimation, is the experimental data useful?

20. Estimating parameters using the model and experimental data:

21. Summary of work to date: Spatial constraints are important in grazing systems.Spatial constraints are important in grazing systems. Innovation in modelling and experimental methods has added value to the understanding of grazing systems.Innovation in modelling and experimental methods has added value to the understanding of grazing systems. The interaction between modellers and biologists has provided a framework to question the basic drivers of grazing systems.The interaction between modellers and biologists has provided a framework to question the basic drivers of grazing systems.

22. Overview: BackgroundBackground Summary of spatial grazing modelSummary of spatial grazing model Experimental methodsExperimental methods Linking model and experimental data to estimate grazing parametersLinking model and experimental data to estimate grazing parameters Where next?Where next?

23. Where next? Extension of experimental methods e.g. measure numbers of bites, larger scale experimentsExtension of experimental methods e.g. measure numbers of bites, larger scale experiments Explore the predictive capabilities of the model e.g. intakeExplore the predictive capabilities of the model e.g. intake Develop a better understanding of the impacts of scale e.g. bite rate or search distanceDevelop a better understanding of the impacts of scale e.g. bite rate or search distance

24. Linking local events to landscape processes: Linking processes across scales Bite Second Patch Field Farm WeekYear DiseaseRejected areasClimate System Drivers Conserved forage Vaccination Stocking rate Management Spatial Scale Temporal Scale

25. Laser and GPS tracking:

26. Varying scales of grazing behaviour:

27. Genotypic variation:

29. Measure Understand Predict