Socioeconomic Dimensions of Crop Modelling.

The mid-1980s and 1990s saw enormous strides in the science of crop modelling, so that by 2000, many of the well-known crop modelling platforms, including the DSSAT (Decision Support System for Agrotechnology Transfer), were already well-developed and being widely used. These platforms, many of which continue to be refined, contained not only different crop models, built around the concept of minimum data sets, but also data management and analytical tools.  Over the years, many linkages have been forged between crop models and many different types of socio-economic models.  There have been considerable successes in using crop models for decision support.  But time has also highlighted key differences between the socio-economic dimensions of crop models and the biophysical. For example, truly generic socio-economic minimum data sets may well not exist.  Second, problems of complexity and difficulties in evaluation seem to appear much quicker when crop and socio-economic models are tightly integrated than when new capabilities are being added to crop (or socio-economic) models alone.  And third, unlike in many crop models in which a single plant is an excellent proxy for a population, there are many parts of the world where representation of a single individual’s decision-making processes, even when feasible, is inadequate for addressing the household and community levels at which decisions are often made.  But crop models are now a well-established component in the analytical toolkit and are heavily relied upon in integrated assessment work.  For the future, two areas in particular require considerable investment of time and resources.  One is the need for work at the systems’ level, to understand better the risks and opportunities faced by households and communities as a result of global change, and how they can be appropriately addressed.  The second is to build on the growing recognition of the importance of data collection systems and to revitalise and expand these, particularly in developing countries.  These two things will be critical in evaluating options that can contribute to the development objectives of increasing incomes and food security, enhancing livelihoods, and promoting environmental sustainability.