Farming Systems Design 2007 - International Environmental ...

Farming Systems Design 2007
Field-farm scale design and improvement
MODELLING FOR INNOVATION IN DESIGN AND CONSTRUCTION OF
CROP PRODUCTION SYSTEMS
G. L. Hammer 1 , D. Jordan 2
1 APSRU, School of Land, Crop and Food Sciences, The University of Queensland, Brisbane, Qld.,
Australia, g.hammer@uq.edu.au
2
Queensland Dept. Primary Industries & Fisheries, Warwick, Qld., Australia, David.R.Jordan@dpi.qld.gov.au
The role of models in innovation has become widely recognised in many fields (Schrage,
2000). Schrage notes that innovative prototypes create innovative teams and treating prototypes
as conversation pieces caters for the complex human interplay required in innovation. This is his
notion of serious play. Hence, model prototyping and simulating are important, but not sufficient,
factors underlying innovation. Focusing on participation of key players, and realising the central
roles of dialogue and co-learning, are equally important factors in the innovation process.
These general concepts have now been largely accepted and adopted in various
approaches leading to the development of improved cropping practices with farmers (e.g. Hammer,
2000; Keating and McCown, 2001; McCown, 2001; Meinke et al., 2001; Carberry et al., 2002;
Nelson et al., 2002). Scientists can bring potential innovation to farming systems practice, but this
must be explored within the context of the real farming system. Simulation-based discussion
provides an effective means to achieve this.
But farmers and their advisers are not the only relevant players in using models as tools for
production system innovation. Hammer et al. (2002) set out a case for the use of models in
understanding genetic regulation and aiding crop improvement. Physiological dissection and
modelling of traits provides an avenue by which crop modelling could contribute to enhancing
integration of molecular genetic technologies in crop improvement (Yin et al., 2004; Hammer et al.,
2005; White, 2006). Models are seen as a bridge between the explosion in capacity and
knowledge in molecular biology and genetics, and its application to plant improvement. Hammer
et al. (2006) and Yin and Struik (2007) have recently reviewed the potential for models to help
navigate this complexity and a symposium was devoted to this topic at the most recent
International Crop Science Congress (see Cooper and Hammer, 2005). There has been a focus
on ways to quantitatively link model coefficients with underlying genomic regions (e.g. White and
Hoogenboom, 1996; Tardieu, 2003; Messina et al., 2006). It is then possible to incorporate such
gene-to-phenotype models within breeding system simulators to compare breeding strategies
(Cooper et al., 2002; Chapman et al., 2003; Hammer et al., 2005). Attempts to date have
reinforced the need to address the inherent level of detail and quality of crop models for this task
(Hammer et al., 2002; White, 2006). It is becoming clear that enhanced rigour in process
representation, so that interactions and feedbacks are captured correctly, is required for effective
gene-to-phenotype modelling. It is also clear that participatory research and co-learning with plant
breeders, molecular biologists and other players in this field is a key aspect of using models for
innovation in crop improvement programs (Hammer and Jordan, 2007). Again, the models and
simulations become the discussion piece in seeking innovation.
To date innovations associated with the use of models in crop management or crop
improvement have been incremental. They have been targeted at either management-byenvironment
(M*E) or genotype-by-environment (G*E) interactions. While useful changes have
resulted in agronomic practice (e.g. Meinke et al., 2001; Carberry et al., 2002) and breeding
efficiency (e.g. Campos et al., 2004; Loeffler et al., 2005), progress has been evolutionary.
In this paper we explore possibilities for revolutionary change in cropping systems by using
models to help design and construct novel and innovative production systems. We consider that
new possibilities for simultaneous consideration of genotype-by-management-by-environment
(G*M*E) interactions provide the impetus. It was over a decade ago that Cooper and Hammer
(1996) outlined the concept of fusing the agronomic (M*E) and plant breeding (G*E) perspectives
of crop improvement into a single G*M*E (or G*E*M) approach. They outlined the concept of crop
improvement as a search strategy on a complex adaptation or fitness landscape. The landscape
consists of the phenotypic consequences of G and M combinations in target E. The phenotypic
consequences of only a very small fraction of all possible G*M*E combinations can be evaluated
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