Simple and Universal Crop growth Simulator (SUCROS)

Submitted by marcel.lubbers on Thu, 03/24/2011 - 12:06
Program type
Crop Growth or soil simulation model, optimization model
Available since


SUCROS1&2 is a mechanistic crop growth model as influenced by environmental conditions. SUCROS2 simulates both potential and water limited growth of a crop, i.e. its dry matter accumulation under resp. ample and rainfed supply of water and nutrients in a pest-, disease- and weed-free environment under the prevailing weather conditions. The rate of dry matter accumulation is a function of irradiation, temperature, crop characteristics and water supply. The basis for the calculation is the rate of CO2 assimilation (photosynthesis) of the canopy; it is a function of incoming radiation and light. After substraction of maintenance respiration, growth of leaf, stem, root and storage organs are simulated. Biomass partitioning depends on crop development stage, which is computed as a function of temperature only. With crop specific input parameters different crops can be simulated.

A brief history of SUCROS

By Pepijn van Oort, January 2018.

SUCROS (Goudriaan and van Laar, 1994; van Laar et al., 1997) was developed as one of the models in the Wageningen school of crop growth models (see Bouman et al. 1996 and van Ittersum et al. 2003 for the origins of SUCROS). Compared with the equally famous LINTUL, SUCROS uses a more sophisticated leaf photosynthesis model. LINTUL uses radiation use efficiency (RUE). SUCROS uses a light response curve (LRC) and it integrates photosynthesis over the day, it integrates photosynthesis  over different canopy depths and it distinguishes between direct and diffuse radiation. The recent biochemically more sophisticated Farquhar-von Caemmerer-Berry biochemical model of leaf photosynthesis was never implemented in SUCROS.

The following newer models are “offspring” of the SUCROS model:

Check and search "SUCROS" for more SUCROS offspring.

Scale of application
Spatial resolution
Key outputs

crop growth, yield, soil water fluxes

Time horizon
Growing season
Time step of modeling
Required to run


Required to develop


Database I/O
Background information

Rabbinge R., S.A. Ward & H.H. van Laar (editors), 1989. Simulation and systems management incrop protection. Pudoc. Simulation Monographs; 32, 420 pp.

Keulen, H. van & G.W.J. van de Ven, 1988. Applicaton of interactive multiple goal linearprogramming techniques for analysis and planning of regional agricultural development: a case studyfor the mariut region (Egypt). In: Agriculture. Socio-economic factors in land evaluation.Proceedings of a conference held in Brussels 7 to 9 October 1987. 36-57

Rossing, W.A.H., 1993. On damage, uncertainty and risk in supervised control: aphids and brown rustin winter wheat as an example. PhD thesis, Wageningen Agricultural University. 201 pp.
Laar, H.H. van, J. Goudriaan & H. van Keulen (editors), 1992. Simulation of crop growth for potentialand water-limited production situations, as applied to spring wheat. Simulation Reports 27, 72 pp. 

Applications & Use

analysis of field experiments; analysis of impacts of climate change on crop yields

Dr. Peter Leffelaar
Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands