On approaches and applications of the Wageningen crop models

Abstract Since the pioneering work of C.T. de Wit in the 1960s, the Wageningen group has built a tradition in developing and applying crop models. Rather than focusing on a few models, diversity is its trademark. Here we present an overview of the Wageningen crop and crop-soil modelling approaches along three criteria. The first criterion relates to the production situations the models are dealing with (i.e. potential, water and/or nutrient-limited, and actual production situations including pests, diseases and weeds). Second, models differ as a result of the objectives of model development, and hence required scale and degree of detail and comprehensiveness. Third, models have at least three potential application domains, i.e. research, education and support of learning and decision making processes. We describe both summary and more comprehensive modelling approaches for the major production situations. An overview of most of the Wageningen models is presented together with a more detailed description of LINTUL, SUCROS, ORYZA, WOFOST and INTERCOM. Illustrations for each of the three application domains are presented, i.e. plant type design, guiding experimental research, education, yield gap analysis, evaluation of manure policies, crop growth monitoring system and analysis and design of farming and regional land use systems. We discuss common issues of model verification, model validation, model validity and data requirements, and present information on software implementation, model and software documentation and distribution policy. Finally, we reflect upon the Wageningen modelling approaches and identify a number of key issues for future research. Major achievements of Wageningen modelling efforts include (1) a broad variety of approaches for modelling of systems at different scales and with different purposes; (2) their contribution to quantitative systems thinking in general, also for applications at higher hierarchical levels; (3) a strong linkage between crop modelling and higher education, both at undergraduate, graduate and post-doctoral level. To continually increase our understanding of crops and production systems a diversified approach must be cherished. At the same time we conclude that focus is required on a limited number of modules in a more integrated modelling framework for the benefit of analysing, evaluating and designing cropping systems. This review may be instrumental in the development of such an integrated framework.