Agriculture Reference
In-Depth Information
environmentally sustainable systems (Knudsen
et al
., 2006; Rotz
et al
., 2011c). A well-developed
and evaluated research model provides new infor-
mation by representing and exploring the effects
of interacting components within the farm sys-
tem. This type of information cannot be readily
obtained through experimental measurement or
procedures other than dynamic simulation.
Decision support tools are primarily designed
to provide useful information and direction for
decision making. They are most successful when
they address specific and often reoccurring ques-
tions or needs of the producer or those consulted
by producers. Their purpose is not to develop new
information as much as to provide the appropri-
ate information needed to address a current
concern and to facilitate the interpretation of
available information. GPFARM (Ascough
et al
.,
2001) and I-FARM (van Ouwerkerk, 2010) are
examples of whole-farm models developed pri-
marily for decision support purposes. Adoption of
whole-farm models in a decision support role has
not been as successful as hoped in their early
stages of development (Ascough
et al
., 2010).
Research models can be used in education.
For example, IFSM is used in a number of under-
graduate and graduate programmes in various
universities. Decision support tools can also pro-
vide effective educational aids. However, because
of the vast difference in goals and information
needs, it is infeasible for a single model to be sat-
isfactorily used for both research and decision
support. For this reason, components of com-
prehensive research models are sometimes
repackaged to meet the needs of education and
decision support. For example, FarmN, a deci-
sion support tool extracted from the FASSET
model, aids in managing farm N flows
(Hutchings and Peterson, 2012). Likewise, the
Dairy Gas Emission Model (DairyGEM), devel-
oped from the animal and manure-handling
components of IFSM, provides a simpler educa-
tional tool for evaluating and comparing effects
of mitigation strategies on gaseous emissions
from dairy farms (Rotz
et al
., 2011a).
nutrients back to the land are simulated for many
years of weather on a crop, beef or dairy farm
(Fig. 10.1). Crop growth and development are pre-
dicted daily based upon soil water and N availa-
bility, ambient temperature and solar radiation.
Simulated tillage, planting, harvest, storage and
feeding operations predict resource use, timeli-
ness of operations, crop losses and nutritive
quality of feeds produced. Nutrient contents of
available feeds and nutrient requirements of the
animal groups making up the herd drive feed
allocation and animal responses. Manure quan-
tity and nutrient contents are functions of the
herd characteristics and feeds consumed.
Nutrient flows through the farm are mod-
elled to predict nutrient accumulation in the soil
and loss to the environment (Rotz
et al
., 2011b).
Environmental impacts include ammonia
and hydrogen sulfide emissions from manure
sources, soil denitrification and nitrate leaching
losses, sediment erosion, and soluble and sediment-
bound P runoff. Carbon dioxide, methane and
nitrous oxide emissions are tracked for crop,
animal and manure sinks and sources to predict
net greenhouse gas emission. Secondary emis-
sions that occurred during the production of
resources used on the farm, such as purchased
feed and animals, fuel, electricity, machinery,
fertilizer and pesticides, are included in a cradle
to farm gate LCA of the carbon footprint of the
milk, meat or feed produced. Whole-farm mass
balances of N, P, potassium (K) and carbon are
determined as the sum of all imports in feed,
fertilizer, deposition and crop fixation minus
the exports in milk, excess feed, animals, manure
and losses leaving the farm.
Simulated performance is used to deter-
mine production costs, incomes and economic
return for each year of weather. A whole-farm
budget includes fixed and variable production
costs (Rotz
et al
., 2011b). All major production
costs are subtracted from the total income
received for milk, animal and excess feed sales to
determine a net return to management. Each
farm simulation, conducted over a 25-year
sample of recent historical weather data,
results in a distribution of annual predictions
that provides an assessment of risk due to vary-
ing weather. By comparing simulation results,
differences among production systems are
determined including annual resource use,
production efficiency, environmental impact,
production costs and farm profit.
Integrated Farm System Model
The IFSM dynamically assesses and compares
the environmental and economic sustainabili-
ties of farming systems (Rotz
et al
., 2011b). Crop
production, feed use and the return of manure
