model

The root node of your model and the only one that you cannot delete using the Parameter View window although it's probably not a good idea to delete any of your parameter categories anyway (see notes about deleting categories).


animals

Animals Herbivore species Number of animal types 1 heads integer -
Animals Livestock Unit LSU (kg) Allometric reference to calculate stocking rate 450 Livestock Equivalents LE integer -

The root node of the animal types in your model. Automatically add animal types using the Add Animal window. The new entry will be automatically inserted into the parameter list with a set of default parameters based on animal mass that you can modify to make the new animal type different if there are already other types in your model.

The other way to add new animal types is by simply increasing the parameter value for the number of herbivores you want to include in your model using the Parameter View window, i.e., navigate to the Animals parameter category and increment the total count of Herbivore species that you will find there. Update your model using the Simulation controls in the Main Control Panel and you will see a new parameter category appear for the extra animal types. This will work for any number of extra animal types, but then you have to be able to modify their parameters to make them different, otherwise just the single entry is required.



commuting

This parameter category contains all the rule-based decisions for animal spatial foraging.


assessment

Assessment Global Scope Range of G* Yes - yes/no yes/no
Assessment ET Modifier for G* 1 - integer 0..1

You will find notes on the spatial foraging decision rules, memory, optimal foraging, marginal value theory and G* in the section on Ecological Architecture.

In Adler & Hall's model (2005, Landscape Ecology 20:319-333) consumption is allowed if the current location held more biomass than a fraction (ET) of the environmental mean. This has been refuted in Derry (2004).


local

Local Include Current Current location assessed. Yes - yes/no yes/no

Not including the current animal location in their assessment of where to go next can force animals to move if there is a suitable location to move to. If there is a profitable alternative, then staying put is not an option even if the current location can offer a faster intake rate.


memory

Memory Half Life (+) Memories of profitable sites 2 days integer 1..365
Memory Active Use stored values in assessment Yes - yes/no yes/no
Memory Half Life (-) Negative memories of unprofitable sites 10 days integer 1..365
Memory Square Function Instead of gradual decay function No - yes/no yes/no

remote

Remote Segments Directions assessed 4 - integer 4,8
Remote Frequency Day between assessment 1 days integer 1..365
Remote By Mean Assessment based on average directional profit Yes - yes/no yes/no

vision

Vision Visual Acuity Assessment distance 400 m integer -
Vision Unrestricted Limit visual acuity No - yes/no yes/no

constraints

Constraints Accuracy % Tolerence for constraint thresholds 0.001 % decimal 0..100
Constraints Time Constraints active Yes - yes/no yes/no
Constraints Digestive Constraints active Yes - yes/no yes/no
Constraints Metabolic Constraints active Yes - yes/no yes/no
Constraints Maximum Grazing time (hrs) Daily time limit 10 hr integer 0..24
Constraints Running ME Metabolic constraints based on previous intake 10 days integer 1..365
Constraints Height Constraints active No - yes/no yes/no


ldrule

LDRule Active Activate conversion Yes - yes/no yes/no

ubrule

UBRule Active Activate contingency Yes - yes/no yes/no
UBRule Exclusive Override LDRule No - yes/no yes/no

active

Active % consumption Amount removed under UBRule 50 % integer 0..100
Active Threshold Minimum % heterogeneity for LDRule 1 % integer 0..100

costs

Costs Lethargy Added incentive for no animal movement 0 % decimal 0..100

energy

Energy Foraging Costs active Yes - yes/no yes/no
Energy Foraging Multiplier Cost multiplier 1 - integer -
Energy Commuting Costs active Yes - yes/no yes/no
Energy Commuting Multiplier Cost multiplier 1 - integer -

travel

Travel Foraging Costs active Yes - yes/no yes/no
Travel Commuting Costs active Yes - yes/no yes/no

non-spatial

Spatial Foraging Distance Default daily distance 1000 m integer -
Spatial Commuting distance Default daily distance 1000 m integer -

spatial

Spatial From Water Costs active Yes - yes/no yes/no
Spatial To Water Costs active Yes - yes/no yes/no
Spatial True Diagonal Trigonometric calculation of distance Yes - yes/no yes/no
Spatial Net Foraging Foraging distance subtracted from commuting distance Yes - yes/no yes/no
Spatial Time Contribution Travel time for water added to daily total Yes - yes/no yes/no

grid

Save options for commuting grid.


output

Output Save Frequency Days between saves 0 days integer 0..365
Output Save Day Save on same day 0 days integer 0..365

intake

Rules involving prediction of animal intake


potential

Rules involving prediction of animal potential intake


herd

Herd Parts Dig Use known plant parts digestibilities No - yes/no yes/no

Use of known plant parts digestibilities in calculations of animal herd potential intake compared to mean digestibility weighted by contribution to intake mass.




disease

Disease Probability of Disease (0..1) Likelihood of disease 0 - probability 0..1
Disease Disease Alarm Audible notification of disease activity No - yes/no yes/no

Random chance of disease occurring with this probability and decaying from maximum mortality until end of effect, which is randomly allocated for each outbreak event.


feeding behaviour

Feeding Behaviour Feeder Type Feeding preference intermediate - text grazer, browser, intermediate
Feeding Behaviour Maximum Intake (default) Intake amount when fixed 14.28 kg decimal -
Feeding Behaviour Absolute Preference Limit to only Feeder Type No - yes/no yes/no
Feeding Behaviour Fixed Intake Fix to Maximum Intake No - yes/no yes/no
Feeding Behaviour Freeze Nullify consumption No - yes/no yes/no






forms

Internal setting for your animal module windows. You have access to these through the window controls.



restock

Restock Day Day in month to buy animals 1 days integer 1..31
Restock Month Month to buy animals December months text January .. December
Restock Threshold Maximum stocking rate before purchase 0.1 Livestock Equivalents LE decimal -
Restock Constant Target Ideal stocking rate 0.2 Livestock Equivalents LE decimal -
Restock Female Purchase Proportion females bought 0.98 - decimal 0..1
Restock Active Allow purchases Yes - yes/no yes/no

sales

Sales Offtake Day Market day 15 days integer 1..31
Sales Offtake Month Market month March months text January .. December
Sales Female Sales Sell females Yes - yes/no yes/no
Sales Minimum Males (% females) Limit female sales to this % of herd 2 % integer 0..100
Sales Killing Out Male (% LWT) Sell only individuals with more than this % of maximum fat mass 0 % integer 0..100
Sales Killing Out Female (% LWT) Sell only individuals with more than this % of maximum fat mass 0 % integer 0..100
Sales Cull Cows (%) Remove % of older females 0 % integer 0..100
Sales Sales Resolution Proportion of offtake removed until requirement is met 0.01 - decimal 0..1






sequence

Sequence MN0 - 4 - integer 1 .. # classes
Sequence MN1 - 3 - integer 1 .. # classes
Sequence MB2 - 2 - integer 1 .. # classes
Sequence MB3 - 1 - integer 1 .. # classes
Sequence FN0 - 5 - integer 1 .. # classes
Sequence FN1 - 6 - integer 1 .. # classes
Sequence FN2 - 7 - integer 1 .. # classes
Sequence FN3 - 8 - integer 1 .. # classes
Sequence FP0 - 9 - integer 1 .. # classes
Sequence FP1 - 10 - integer 1 .. # classes
Sequence FP2 - 11 - integer 1 .. # classes
Sequence FP3 - 12 - integer 1 .. # classes

Preferred sequence of sales from age classes used to satisfy offtake requirement using format [sex][mature][class] with MN : Male Neonates, MB : Male Breeding, FN : Female Neonates, FN : Female Nonpregnant, FP:Pregnant. This default sequence for cattle prefers selling males before females, starting with older animals and working through this sequence until the offtake requirement for the active management strategies is met.



dynamic market

Dynamic Market Dynamic Decision Months Months of predictions before market date 0 - integer 0..12
Dynamic Market Dynamic Market CV Coefficient of variation of market prices 0.3 - decimal -

Animal sales strategy that predicts sales prices based on historic market data and projected growth rates. See Tarr (2001).


regression / lwgs / month / M, F, P

M, F, P m - - - decimal -
M, F, P c - - - decimal -
Regression parameters for projected growth rates for use in Dynamic Market strategy. Linear regressions ( y=mx+c ) are of monthly Live Weight Gain (LWG) for Males (M), Females (F) and Pregnant Females (P) over the period for which the predictive Dynamic Market Strategy is applied.




mass

Mass Male MATR Mass of a mature male 476 kg integer -
Mass Female MATR Mass of a mature female (optional) 331.772 kg integer -
Mass Minimum Fat Mass Lower range of fat distribution 0 kg integer -
Mass Maximum Fat Mass Upper range of fat distribution (optional) 67 kg integer -
Mass Initial male juvenile mass (% MATR) Birth mass (optional) 5 kg integer -
Mass Initial male adult mass (% MATR) Starting mass (optional) 100 kg integer -
Mass Initial female juvenile mass (% MATR) Birth mass (optional) 7.1736 kg integer -
Mass Initial female adult mass (% MATR) Starting mass (optional) 100 kg integer -

Several parameters are automatically calculated from allometric scaling with Male MATR and are therefore optional.


mortality

Mortality Background Mortality Frequency How often to apply Background Mortality rates. Active if non-zero. 0 days integer 1..365
Mortality Neonate Background Mortality (% p.a.) Annual rate of mortality 10 % integer 0..100
Mortality Male (Juvenile) Background Mortality (% p.a.) Annual rate of mortality 10 % integer 0..100
Mortality Male (Adult) Background Mortality (% p.a.) Annual rate of mortality 10 % integer 0..100
Mortality Female (Juvenile) Background Mortality (% p.a.) Annual rate of mortality 10 % integer 0..100
Mortality Female (Adult) Background Mortality (% p.a.) Annual rate of mortality 10 % integer 0..100

Sources of mortality other than starvation and disease.


population

Population Freeze Fix animal number Yes - yes/no yes/no
Population Birth Ratio - males Male birth ratio 1 - integer -
Population Birth Ratio - females Female birth ratio 1 - integer -
Population Total Initial Adult Heads Starting males 133 - integer -
Population Initial Adult Fat Coefficient of Variance Spread of fat mass normal distribution in population 0.2 - decimal -
Population Manage Initial Adult Heads Limits males to Minimum Males (% females) setting in Sales, otherwise equal to females Yes - yes/no yes/no


distribution

Distribution Fat Mass Resolution Class width for the fat mass distribution 1 kg integer 0 .. Maximum Fat Mass - Minimum Fat Mass
Distribution Fat Distribution Entropy Range Active if non-zero. 0 - integer -

The Fat Distribution Entropy Range defines a window from Minimum Fat Mass + 25% of mature fat to Minimum Fat Mass + 25% of mature fat - Fat Mass Resolution x Fat Distribution Entropy Range. When the average fat mass is dropping and moves into this window, the fat distribution is redistributed normally, tending towards the initial distribution and the Initial Adult Fat Coefficient of Variance.


reproduction

Reproduction Gestation Pregnancy 263 days - -
Reproduction Lactation Postpartum 239 days - -
Reproduction Intrinsic Rate of Increase Unspecific fecundity 1 - - -
Reproduction Restricted mating season Start of pregnancies only possible in defined season No - yes/no yes/no
Reproduction Mating Season Start Days since start day of simulation 153 day - -
Reproduction Mating Season End Days since start day of simulation 180 day - -


water/bomas

.
Water Bomas Dependent Water dependency Yes - yes/no yes/no
Water Bomas Wet Season Herding Taken to water in wet season No - yes/no yes/no
Water Bomas Sequence Use water point sequence defined in Water Locator windowNo - yes/no yes/no

sequence

Sequence 1 - 1,21 - - -

This entry is for just a single water point at grid location (1,21).


climate

Climate Loop rainfall data file Cycle data input No - yes/no yes/no
Climate Rainfall target mean Scale input data to total this value. Active if non-zero. 0 mm integer -
Climate Rainfall data cv Redefine rainfall distribution. Active if non-zero. 0 - integer -
Climate Vapour Pressure Deficit - 0.009 KPa decimal -
Climate Wind Speed - 13.27 m/s decimal -
Climate Atmospheric Pressure - 1013.78 mbar decimal -
Climate Default Rain Override data input with hardwired values No - yes/no yes/no
Climate Seasonal Recognise dry and wet seasons Yes - yes/no yes/no
Climate Dry Season Dry season status if not seasonal No - yes/no yes/no
Climate Precursor data file Typically the daily mean rainfall C:\Program Files\SimSAGS4.1\Projects\example3\mean.daily.rai mm decimal -
Climate Rainfall data file Typically raw daily rainfall C:\Program Files\SimSAGS4.1\Projects\example3\daily.rai mm decimal -
Climate Atmospheric Data Radiation, Temperature and Humidity C:\Program Files\SimSAGS4.1\Projects\example3\atmosphere.txt MJ/metre squared, Celcius, % decimal -
Climate Wet season Start Day Days since simulation start 30 day integer -
Climate Dry Season Start Day Days since simulation start 240 day integer -

temperature

Monthly minimum temperatures.


minimum

Minimum January - 13.1 Celcius decimal -
Minimum February - 13.4 Celcius decimal -
Minimum March - 14.4 Celcius decimal -
Minimum April - 14.3 Celcius decimal -
Minimum May - 14.2 Celcius decimal -
Minimum June - 12.6 Celcius decimal -
Minimum July - 11.5 Celcius decimal -
Minimum August - 11.8 Celcius decimal -
Minimum September - 12.2 Celcius decimal -
Minimum October - 13.7 Celcius decimal -
Minimum November - 14.4 Celcius decimal -
Minimum December - 13.8 Celcius decimal -

Monthly minimum temperatures.


configuration

Configuration Start (zero-based) - 0 - integer 0 .. End
Configuration End (one-based) - 1 - integer Start .. 4

Automation mechanism used in Illius et al (1998) to select between strategies for female sales and restocking. The configuration sequence can be set to repeat simulations for Start (zero-based) < Configuration <= End (one-based) where

Configuration=1 : no female sales, no restocking
Configuration=2 : no female sales, restocking
Configuration=3 : female sales, no restocking
Configuration=4 : female sales, restocking


fire

Prescribed and natural fires included in this section.



output

Output Active Fire record No - yes/no yes/no
Output Verbose Detailed fire record No - yes/no yes/no


gis

This section stores all of the settings for your GIS input data. It is highly recommended that you access these settings via the dedicated windows designed to facilitate the import processes, including the GIS Control, Site Area Mask and Water Locator, as well as the additional GIS settings that you can make in your Preferences.


phews

PHEWS Active - No - yes/no yes/no

Currently this is only a integration test link for PHEWS (Pastoralist Household Economic Welfare Simulator). Meanwhile further details in Kathleen A. Galvin; Philip K. Thornton; Randall B. Boone; Jennifer Sunderland (2004) Climate variability and impacts on east African livestock herders: the Maasai of Ngorongoro Conservation Area, Tanzania. African Journal of Range and Forage Science, Volume 21, 183-189.








turnover

Turnover Proportion Woody Annual woody leaf turnover 1 - decimal 0..1

rates

Monthly rates of leaf turnover.



communication

Communication Diagonal Allows distances to be calculated trigonometrically rather than in unit cell dimensions. Yes - yes/no yes/no



type

Type Homogeneous Even distribution of initial plant biomass No - yes/no yes/no
Type Stochastic Random distribution of initial plant biomass Yes - yes/no yes/no
These defaults are overwritten if you supply Initial Plant Biomass GIS layers.

topography

Topography Metres Above Sea Level Base altitude 1000 m integer -
Topography Percentage variation Coefficient of variation from Metres Above Sea Level 10 % integer -
Topography Planar Force level landscape even if sloping by Percentage variation No - yes/no yes/no
Topography Topographical Landscape Activate surface water redistribution of rainfall in the landscape by runoff Yes - yes/no yes/no


time

Time Precursory years - 10 years integer -
Time Simulation years - 0 years integer -
Time Initial simulation month Day counts made from this date September months text January .. December
Time Midyear Peak growth 210 day intger 1..365
Time Start Corresponding dateline for your rainfall data 1889 year integer/td> -

vegetation

Vegetation Annual grass species - 0 - integer -
Vegetation Perennial grass species - 1 - integer -
Vegetation Forbs species - 0 - integer -
Vegetation Shrub species - 0 - integer -
Vegetation Tree species - 1 - integer -
Vegetation Transpiration Bias Preferential allocation of transpiration to grasses 0 decimal - 0..1
Vegetation TE Adjustment Activates transpiration efficiency (TE) factor. No - yes/no yes/no
Vegetation Transpiration Efficiency TE value before adjustment by % Humidity 300 kg/ha/mm - -

The root node of the plant types in your model. Automatically add plant types using the Add Plant window. The new entry will be automatically inserted into the parameter list with a set of default parameters based on either a herbaceous or woody generic type that you can modify to make the new plant type different if there are already other types in your model.

The other way to add new plant types is by simply increasing the parameter value for the number of that plant type you want to include in your model using the Parameter View window, i.e., navigate to the Vegetation parameter category and increment the total count of that plant species that you will find there. Update your model using the Simulation controls in the Main Control Panel and you will see a new parameter category appear for the extra plant types. This will work for any number of extra plant types, but then you have to be able to modify their parameters to make them different, otherwise just the single entry is required.

Default parameters are given for a herbaceous type and a woody type.

TE Adjustment activates an increase in growth by Transpiration Efficiency (TE) for high soil moisture giving average TE of 10 kg/ha/mm transpiration. Details in Walker & Langridge (1996).




% Moisture

Moisture content and drying settings to predict fire events. See Fire category.


drying

Drying Rates Active Activate drying Yes - yes/no yes/no

Used to predict fire events. See Fire category.


inside canopy

Inside Canopy Drying Rate Daily water loss from grass beneath tree canopy 0.001 proportion per day decimal 0..1

Used to predict fire events. See Fire category.


outside canopy

Outside Canopy Drying Rate Daily water loss from grass between trees 0.01 proportion per day decimal 0..1

Used to predict fire events. See Fire category.


inside canopy

Inside Canopy Tree Neighbourhood Active Allow effects of tree canopy on grass drying Yes - yes/no yes/no
Inside Canopy % Moisture Difference
[Out->In]
Moving from the open to under a tree. 60 % integer 0..100

Used to predict fire events. See Fire category.


outside canopy

Outside Canopy Partitioned Calculate moisture content per plant part rather than from total herbage mass No - yes/no yes/no

Used to predict fire events. See Fire category.


consumption

Consumption Defoliate Removal of plant biomass Yes - yes/no yes/no

invertebrates

Invertebrates Termites Termite damage 0.00245 kg/ha/day decimal -
Invertebrates Army Worms Plant consumption by Army Worms 0 kg/ha/day decimal -

digestibilities

Digestibilities Green Leaf - 0.7 - decimal 0..1
Digestibilities Dead Leaf - 0.5 - decimal 0..1
Digestibilities Green Stem - 0.6 - decimal 0..1
Digestibilities Dead Stem - 0.35 - decimal 0..1

Plant part digestibilities.


distribution

Distribution % Variation Spatial heterogeneity 50 % integer 0..100

canopy

Canopy Minimum Height - 0 - - -
Canopy Maximum Height - 1 - - -



fire

Plant damage from fire.



growth

Growth Freeze Fix plant biomass No - yes/no yes/no
Growth Fixed (-ve: all days) Fix growth to this value on growth days or every day if negative 0 kg/ha integer -

bimodal

Bimodal Active Adaptation for double rainy seasons No - yes/no yes/no

season

Season Start Begin growth 30 day integer 1..365
Season End End growth 240 day integer 1..365

temperature

Effects of cold temperatures on growth.


effects

Effects Senescence Moderate aging by temperature No - yes/no yes/no
Effects Growth Moderate growth by temperature No - yes/no yes/no

proportions

Proportions Green Leaf - 0.21277 - decimal -
Proportions Dead Leaf - 0.70922 - decimal -
Proportions Stores - 0.03546 - decimal -
Proportions Fallen Seed - 0 - decimal -
Proportions Green Stem - 0 - decimal -
Proportions Seed - 0 - decimal -
Proportions Dead Stem - 0.04255 - decimal -

Initial proportions of plant parts used to allocate Initial Biomass.



consumption

Consumption Defoliate Removal of plant biomass Yes - yes/no yes/no

invertebrates

Invertebrates Termites Termite damage 0.00245 kg/ha/day decimal -
Invertebrates Army Worms Plant consumption by Army Worms 0 kg/ha/day decimal -

digestibilities

Digestibilities Green Leaf - 0.5 - decimal 0..1
Digestibilities Dead Leaf - 0.35 - decimal 0..1
Digestibilities Fallen Seed - 0.56 - decimal 0..1

Plant part digestibilities.


distribution

Distribution % Variation Spatial heterogeneity 50 % integer 0..100




fire

Plant damage from fire.



growth

Growth Freeze Fix plant biomass No - yes/no yes/no
Growth Fixed (-ve: all days) Fix growth to this value on growth days or every day if negative 0 kg/ha integer -

season

Season Start Begin growth 10 day integer 1..365
Season End End growth 240 day integer 1..365

proportions

Proportions Green Leaf - 0.09004 - decimal -
Proportions Dead Leaf - 0.13643 - decimal -
Proportions Stores - 0.02319 - decimal -
Proportions Fallen Seed - 0.68213 - decimal -
Proportions Green Stem - 0 - decimal -
Proportions Seed - 0 - decimal -
Proportions Wood - 0.06821 - decimal -

Initial proportions of plant parts used to allocate Initial Biomass.



Supplementary Information

Cattle Prices

SimaSAGS was applied to forecasting optimals sales policies for the cattle industry throughout Namibia. To predict the best conditions - time for market trends and maximum offtake (total mass of cattle sold) - for a farmer to sell their stock, the model used regressions which could predict long-term live weight gain in combination with historical average auction prices of cattle carcasses at all markets in South Africa during 1997 (this was the best market data available). Market prices were categorized according to three categories (brackets): age class, fat mass and sale month. This system of categorization captured the market trends for different sized animals across the range of classes within the population age structure.

The model is initialized with the following data from South African markets in 1997, but you can modify these values with your own market data. The data across the page [1..12] per month, down the page by fat mass category [0...6] within age category [A,B,C]. Monthly and annual means are also provided.

Download a text file of AVERAGE AUCTION PRICE OF
CATTLE CARCASSES AT ALL RSA MARKETS 1997