251and used in the calculation of farm profits (i.e. before tax). Therefore, outputs from
APSFarm include, but are not limited to, production measures e.g., yields and crop
areas; economic measures such as production costs, crop gross margins, economic
risk and farm annual profit; efficiency measures such as crop and whole-farm WUE;
and environmental measures such as deep drainage, runoff and erosion.
2.8 Participatory Modelling of Dryland Farms
The need to adjust the scale of economic activity of farm businesses to fit within the
boundaries set by shifts in resource availability (e.g. water, climate, finances), envi-
ronmental change (e.g. emissions, land degradation) and farmer preference required
the development and application of more integrative and interdisciplinary model-
ling tools. In response to this demand, APSFarm uses a participatory framework
that captures the major rules and decision-making processes that farmers undergo
when managing complicated farm businesses. The framework involves interviews
and discussions with farmers and their consultants to identify, quantify and translate
rules, preferences, practices and strategies into a whole-farm systems model,
becoming a virtual representation capable of mimicking the dynamic operation of
their farm business. Once model outputs for the baseline scenario (i.e. climatology)
are accepted as realistic by the participating farmer case studies, the model is used
to address specific questions from the farmers, researchers, extension officers or
agribusinesses via What if? scenario analyses. The final outcome is that farmers,
extension officers, researchers and local agribusinesses are more confident that the
newly available technologies will successfully achieve their objective and fit in with
the existing farming systems, biophysical, social and cultural constraints.
2.9 Systems Characteristics as a Source of Resilience
in Dryland Agriculture
Using the whole-farm model APSFarm, Rodriguez et al. ( 2011 ) tested the hypoth-
esis that plasticity in farm management introduces resilience to change and allows
farm businesses to perform when operating in a highly-variable environment. Two
real farm businesses differing in management (plastic vs. rigid rotations) were inter-
viewed and set up in the model. The model was then run for two climatology and
two climate change scenarios to introduce stress into the system. Both plastic and
rigid locations were run at both locations so that the different systems could be
compared to similar baseline and climate projections. The more plastic farming
system was located in Emerald, Queensland, Australia and could be characterised
by having a management highly contingent on environmental conditions. In the
plastic farming system, the farm manager was constantly changing crops and inputs
Modelling Dryland Agricultural Systems