Agriculture Reference
In-Depth Information
and Integrated Management Systems for Dairy
Production (SIMS
DAIRY
) provides a static model-
ling framework that integrates other component
models to predict ammonia and greenhouse gas
emissions, nitrate leaching, P losses and net farm
profit for evaluation of dairy production systems
(del Prado and Scholefield, 2008). An online
tool, I-FARM, is a database-driven farming sys-
tem model applicable to a wide range of crop and
livestock production systems in the eastern US
(van Ouwerkerk, 2010).
Dynamic simulation models follow farm
processes through time. As such, this type of
simulation can better emulate the dynamic inter-
actions among farm components. There are a
few good examples of dynamic simulation mod-
els that predict various environmental impacts
of farming systems. The Farm Assessment Tool
(FASSET) is a farm-scale model developed and
applied in Denmark, which simulates crop pro-
duction systems with or without swine and cattle
production (Berntsen
et al
., 2006). The model
evaluates farm production, economics and the
environmental impacts of nitrate leaching,
ammonia emission and net emission of green-
house gases. The Great Plains Framework for
Agricultural Resource Management (GPFARM)
includes a dynamic simulation of crop and beef
animal production systems for the Great Plains
of the US (Ascough
et al
., 2010). The model
simulates soil, crop, pasture and animal pro-
cesses to predict production performance and
economics. Carbon and N cycling are simulated
to include environmental losses of ammonia,
nitrous oxide, nitrate leaching, erosion and pesti-
cide transport. DairyWise is a dairy farm model
that includes N and P cycling, nitrate leaching,
ammonia emissions, greenhouse gas emissions,
energy use and a financial farm budget (Schils
et al
., 2007). The Integrated Farm System Model
(IFSM) is a comprehensive simulation model of
dairy and beef production systems in the USA,
which includes the prediction of a wide range of
environmental impacts along with production
performance and economics (Rotz
et al
., 2011b).
The IFSM model will be described in more detail
and will be the focus of the model evaluation and
application sections that follow.
In recent years, LCA has become a popular
modelling tool. LCA is really an environmental
accounting procedure (Heller and Keoleian,
2011; Kristensen
et al
., 2011). Much information
and data must be collected and assumptions
made to represent all aspects of each environ-
mental impact over the full life of the milk, meat
or other products produced (Pelletier
et al
.,
2010). This includes not only the gaseous emis-
sions and runoff losses that occur directly from
the system, but also those occurring during
the production of resources used including
machinery, fertilizers, fuel, electricity, and pur-
chased feed and animals (Rotz
et al
., 2010).
A full life cycle must include transportation,
processing and marketing through to consump-
tion or waste by the consumer. In practice, the
LCA of animal production systems has often
concluded when the product leaves the farm.
This partial LCA represents a cradle to farm-
gate evaluation. Economics are normally not
included in LCA studies, limiting a full under-
standing of the impact of a management change
to the production system.
Different modelling techniques may be
combined in a given software tool. For example,
an optimization-type farm model was integrated
with a LCA for the determination of sustain-
able milk production systems in Switzerland
(Zimmermann
et al
., 2011). A partial LCA has
also been incorporated in IFSM to evaluate the
farm-gate carbon footprint of the milk or meat
produced (Rotz
et al
., 2010). By combining LCA
with mathematical optimization or simulation,
much of the information required to produce
the LCA can be obtained or derived from within
other model components. This data input greatly
reduces the labour for data collection and the
number of assumptions required to develop the
LCA. Uncertainties associated with extrapolation
or interpolation of location- and time-specific
data from existing reported measurements can
be large. Thus, using a process-based simulation
model that uses known input data to derive esti-
mates of hard-to-measure quantities can reduce
accumulated input error for the LCA.
Model purpose
Three primary purposes for farm model develop-
ment are research, education and decision sup-
port. Research tools such as FASSET and IFSM are
widely used to evaluate alternative production
strategies in search of more economically and
