25 years of the WOFOST cropping systems model

By A. de Wit, Hendrik Boogaard, D. Fumagalli, Sander Janssen, R. Knapen, Daniel van Kraalingen, I. Supit, R. van der Wijngaart, Kees van Diepen. Agricultural Systems, 2019

402 citations recorded by the source corpus.

Abstract The WOFOST cropping systems model has been applied operationally over the last 25 years as part of the MARS crop yield forecasting system. In this paper we provide an updated description of the model and reflect on the lessons learned over the last 25 years. The latter includes issues like system performance, model sensitivity, spatial model setup, parameterization and calibration approaches as well as software implementation and version management. Particularly for spatial model calibrations we provide experience and guidelines on how to execute calibrations and how to evaluate WOFOST model simulation results, particularly under conditions of limited field data availability. As an open source model WOFOST has been a success with at least 10 different implementations of the same concept. An overview is provided for those implementations which are managed by MARS or Wageningen groups. However, the proliferation of WOFOST implementations has also led to questions on the reproducibility of results from different implementations as is demonstrated with an example from MARS. In order to certify that the different WOFOST implementations and versions available can reproduce basic sets of inputs and outputs we make available a large set of test cases as appendix to this publication. Finally, new methodological extensions have been added to WOFOST in simulating the impact of nutrients limitations, extreme events and climate variability. Also, a difference is made in the operational and scientific versions of WOFOST with different licensing models and possible revenue generation. Capitalizing both on academic development as well as model testing in real-world situations will help to enable new applications of the WOFOST model in precision agriculture and smart farming.

Subfields: Ecology, Evolution, Behavior and Systematics, General Agricultural and Biological Sciences, Plant Science

View Paper: https://www.semanticscholar.org/paper/8536bb0071f1173556831bc7cc2e5d7299d6ae36

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In Ecology, Evolution, Behavior and Systematics

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Citations in the corpus

The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know (2020, background): “When soil moisture content is greater than 0.3 (cm3 cm−3) in WOFOST (de Wit et al., 2019), a transpiration rate reduction factor indicating oxygen shortage is introduced.”
Machine learning for large-scale crop yield forecasting (2020, background): “However, they do not account for all yield-reducing factors and have considerable data and calibration requirements (De Wit et al., 2019).”