A radically new, raster-based type of crop growth simulation model was recently developed in the context of the G4INDO (Geodata for crop insurance in Indonesia) project, sponsored by the Netherlands Space Office (NSO). The model is described in a new report, published on 8 October 2018 by the library of Wageningen University and Research (WUR). Author is Dr. Sander C. de Vries of Kind of Green® , who also conducted most of the modelling work at WUR business unit Agrosystems Research.
The described model, called “wflow_lintul”, was developed specifically for spatial applications, in combination with remotely sensed data. Rather than consecutively and individually simulating the growing season for each grid cell in a raster map, wflow_lintul simulates crop growth simultaneously for all grid cells, thereby allowing daily interactions between those grid cells, for instance in terms of water flow. The approach allows for high calculation speeds, which may benefit machine learning, and facilitates dynamic graphic representation of the output.
Figure 1. Leaf area index (LAI) of a rice crop, simulated with wflow_lintul over two consecutive season of potential growth in the Brantas catchment, central Java, Indonesia. LAI gradually increases from zero (pink) to its maximum value (red) and then decreases again (leaf senescence) until the crop is harvested (pink). Then the next (second) crop is initiated. For an example with finer-grained weather data, please scroll down to the bottom of this page.
Wflow_lintul is based on the LINTUL (Light INterception and UtiLization) family of crop growth models, originating from Wageningen University and Research (Spitters, 1990; Spitters and Schapendonk, 1990; Shibu et al., 2010), and simulates rice growth in close conjunction with the spatial hydrological model wflow_sbm, developed at Deltares (see Vertessy et al., 1999 for a description of the SBM model). Both models are part of the Deltares wflow / OpenStreams suit of simulation models.
Further development of wflow_lintul for operational / commercial applications, also for other crops than rice, is presently being undertaken at Kind of Green®. Should you, or your organization, be interested in implementing a wflow_lintul-type of crop growth model in agricultural decision support systems, crop insurance, yield forecasting or even in modelling ecosystems, please send us a message through our contact form and we will shortly get back to you. The report, providing a concise user manual and description of core model code, can be accessed at WUR library via this link, or by clicking on its cover image below.
Shibu, M.E., Leffelaar, P.A., van Keulen, H., Aggarwal, P.K., 2010. LINTUL3, a simulation model for nitrogen-limited situations: application to rice. European Journal of Agronomy 32, 255–271.
Spitters, C.J.T., 1990. Crop growth models: their usefulness and limitations. Acta Hort. 267, 349–368.
Spitters, C.J.T., Schapendonk, A.H.C.M., 1990. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Plant Soil 123, 193–203.
Vertessy, R.A. and H. Elsenbeer, “Distributed modelling of storm flow generation in an Amazonian rainforest catchment: effects of model parameterization,” Water Resources Research, vol. 35, no. 7, pp. 2173–2187, 1999.
Figure 2. LAI of a fictitious crop, simulated with wflow_lintul for a very large catchment (can you recognize it?)