Tracking land condition and pasture state

Growing animals by grazing on native pasture causes changes in the condition of the land and pasture through the consumption of new growth and seeds at critical times, selection of preferred species leading to compositional shifts in pasture (i.e. favouring annual species), the removal of photosynthetic material required for additional growth and root development, and changes in soil structure, function and eco-hydrology through compaction by hard hoofed ruminants.

Herd management is therefore a trade-off between maintaining pasture biomass and landscape function and stocking herd numbers capable of providing the required economic returns. This requires continuous adjustment of herd numbers through tracking pasture utilisation and maintaining land condition through activities such as spelling pasture and seasonal destocking.

CLEM offers the ability to track land condition and pasture state along with a range of associated metrics (e.g. perennial grass percentage and run-off and sediment loss). This allows feedback of herd dynamics as a function of management decisions on pasture production even though CLEM does not have direct links with the pasture production models.

For details of the research and methods relating to this section we refer you to Scanlan et al, 2014, Resting pastures to improve land condition in northern Australia: guidelines based on the literature and simulation modelling. The Rangeland Journal, 36, 429–443.

Land condition

Numerous studies have attempted to quantify the condition of the land upon which grazing is undertaken and link this various metrics and decisions such as sustainable grazing, stocking rates, pasture composition, grass production, erosion and sediment loss (see Table 2 Scanlan et al, 2014).

Tracking change in land condition

CLEM uses the approach of Scanlan et al (2014) as used in the GRASP soil-water and pasture model where we track land condition using a relationship that provides the annual change in the land condition index that ranges from best condition (index 0, high perennial species diversity and abundance, high tussock basal area, low runoff and sediment loss) to very poor (index 11, high annual abundance, low biomass, high run off and soil loss) as a function of the annual pasture utilisation rate (annual consumption divided by annual production as a percentage).

 

The setup of this relationship is undertaken using a Relationship component below each Graze food store type representing the pasture being grazed. Each relationship also requires a Relationship running value component as a child and named “LC”. The Relationship running value allows you to specify the land condition index at the start of the simulation.

You define the relationship by providing a comma separated list of x values (utilisation rate as a percent) and a matching list of changes in condition index for each utilisation as the y values. This form can be as detailed as required to describe the changes needed. Note: Ensure you select “interpolation” method for this relationship to provide correct value of change when between two values of x.

Limiting the range of allowable land condition

You can limit the possible range of land condition that can be expressed in your simulation by setting the minimum and maximum values of the Relationship running value. This allows you to perform a simulation knowing that land condition will only be within the set bounds regardless of the herd management and utilisation rates. This may be required to reflect the fertility of the simulated pasture, your management is designed to be withing a sustainable range, or this is simply all that is needed for the questions being posed of the model.

How to use a constant land condition in your simulations

If you do not want to consider land condition in your simulations, you can set relationship representing “no change” (i.e. x = 0,100: y = 0,0), with an associated relationship tracker setting the initial land condition value that simply specifies the value to be obtained from the pasture data-cube as there is no means of turning off this feature at present and those using GRASP simulations to provide pasture data will need to include a land condition value in the simulations.

Grass basal area

Grass basal area (GBA) is a measure the amount of basal area (generally dominated by perennial tussocks) of the pasture to determine growth potential through root reserves. This relationship was provided in the NABSA model as a means of selecting the pasture growth set from the input data for a given land condition. It is currently provided similar to land condition in that it is responding to utilisation. In reality, and in biophysical pasture models (such as GRASP), GBA responds to pasture growth in the previous two years (rainfall is actually a stronger driver than utilisation) so the GBA relationship in NABSA and CLEM is probably not exactly representing what is happening. However, the feedback between extra growth achieved in above average rainfall periods and the lag in ability to change herd size and associated utilisation rates will reflect the desired impact on GBA. The inherent pasture growth in the pasture models is affected by GBA and this is mostly a product of the inherent soil fertility so the input data or a valid GBA will still need to be included.

The setup of this relationship is undertaken using a Relationship component below each GrazeFoodStoreType representing the pasture being grazed. Each relationship also requires a Relationship running value component as a child and named “GBA”. The Relationship running value allows you to specify the grass basal area at the start of the simulation.

You define the relationship by providing a comma separated list of x values (utilisation rate as a percent) and a matching list of changes in condition index for each utilisation as the y values. This form can be as detailed as required to describe the changes needed. Note: Ensure you select “interpolation” method for this relationship to provide correct value of change when between two values of x.

Limiting the range of allowable land condition

You can limit the possible range of grass basal area that can be expressed in your simulation by setting the minimum and maximum values of the Relationship running value. This allows you to perform a simulation knowing that land condition will only be within the set bounds regardless of the herd management and utilisation rates. This may be required to reflect the fertility of the simulated pasture, your management is designed to be within a sustainable range, or this is simply all that is needed for the questions being posed of the model.

How to use a constant grass basal area in your simulations

If you do not need to consider grass basal area in your simulations, you can set a Relationship representing “no change” (i.e. x = 0,100: y = 0,0), with an associated Relationship running value setting the initial GBA value that simply specifies the value to be obtained from the pasture data-cube as there is no means of turning off this feature at present and those using pasture production simulations to provide pasture data will need to include a grass basal area value in the simulations.

Linking pasture state to production and utilisation

To provide feedback between herd management and pasture production (from input data) requires a pasture production data-cube to be created for the region of interest such that regional aspects of plant production such as soil fertility and rainfall amount and seasonality are considered. This requires all simulated pasture production for the period of interest to be determined for the range of expected land condition, grass basal area and stocking rates. Then the pasture production for the given year of the simulation is obtained from this database based on the current simulated values of land conditions, grass basal areas and stocking rate.

As the CLEM simulation tracks land condition and grass basal area as a function of the herd management and utilisation, the pasture production used reflects the current state allowing rudimentary feedback to pasture production without coupling the two models. This allows grazing to influence the long-term changes in pasture production with feedbacks into the pasture quality and quantity available and animal nutrition, survival and growth.