253unprecedented change such as climate change or when new technologies are pre-
sented to traditional farmers). This is because practical wisdom cannot be taught as
it is context-sensitive (Schwartz and Sharpe 2006 ). For example, in the case of
adapting to climate change, farmers may have little or no experience to be able to
relate to and identify possible actions. This means that medium and long-term farm
planning in the face of likely change scenarios will require far greater levels of
attention and support. Yet farmers often find long-term climate projections of lim-
ited relevance while under pressure to resolve more immediate day-to-day and
season- to-season decisions. For traditional farmers from the developing world, new
technologies (e.g. conservation agriculture principles or risk management strate-
gies) have the potential to present similar challenges as they may be perceived too
difficult, too risky or even culturally wrong. To address some of these issues, partici-
patory discussions and computer-aided farming systems design have proved useful
for gaining insight into complex systems, developing intervention strategies and
generating awareness on the potential impacts of incremental or transformational
changes to adapt to change when applied in real-world situations e.g. commercial
farms, or smallholder farming systems.
4 Conclusions and Future Research Thrusts
Modelling tools have shown value in agriculture research from quantifying benefits
and trade offs in both short term management practices and tactics that reduce risk
in highly-variable climates; and the medium and longer term benefits from changes
in strategies, farming systems designs and resource allocations. Advances in peo-
ple’s connectivity, access to internet, developments in big-data, networks of sensors,
and computing speed mean that more than ever before, research and practice change
in dryland agricultural systems will rely on simulation and prediction. Over the last
twenty years we have moved on from modelling plants, crops, cropping systems and
farming systems (including livestock), to modelling farms and farmers. A number
of challenges though still exist, progress is expected in the near future from model-
ling multiple farms and farmers in a shared landscape, their interactions and con-
nectivity with markets and rural communities, in addition to the modelling of the
multiple functions of agriculture, and the resulting people’s health and nutrition. As
this chapter was produced we have already started modelling communities of farm-
ers and villages and to answer how alternative innovations, particularly among
smallholder farmers, are likely to affect local labour and commodity markets, and
how informal economies are likely to develop as we achieve food security and pov-
erty reduction targets in some of the poorest nations in the world.
Acknowledgements The authors thank the Australian Centre for International Agriculture
Research (ACIAR) and the Grains Research and Development Corporation for their support with
the many studies reported within.
Modelling Dryland Agricultural Systems