2472.6 Modelling Farms and Farmers
Farms are complex systems (Fig. 6 ) and modelling dryland agricultural systems is a
valuable tool to inform decision making and overcome some of these complexities.
Irrespective of their level of endowment, farmers use incomplete or imperfect knowl-
edge to make technical (e.g. agronomic, energy inputs, irrigation scheduling, etc.)
and financial (e.g. marketing, loans, off-farm investment) decisions in a context of
risk and uncertainty associated with climate variability, market volatility, and politi-
cal and global change. It is a complicated system dominated by multiple decisions on
how to allocate limited resources across a range of competing enterprises and time
pressures. Progress in our understanding of crop eco-physiological processes, animal
physiology, reproduction and nutrition, and the interactions between different com-
ponents of the farming system including soil and climate factors (Fig. 6 ) are
Fig. 4 Five water-stress
patterns common in maize
growing areas of north-
eastern Australia based on
clustering of the simulated
supply/demand ratio: e1:
mid-stress, e2: mild
terminal stress, e3:
moderate terminal stress,
e4: no stress and e5: severe
terminal stress (Source:
Chauhan et al. 2013 )
Fig. 5 Simulated frequencies of water-stress environment types (e1 to e5) for maize across sites
and soils in eastern and northern Australia. Stress types are e1: no stress, e2: mild terminal stress,
e3: mild terminal stress, e4: moderate terminal stress and e5: severe terminal stress (Source:
Chauhan et al. 2013 )
Modelling Dryland Agricultural Systems