CropSyst is a is a user-friendly, conceptually simple but sound multi-year multi-crop daily time step simulation model. The model has been developed to serve as an analytic tool to study the effect of cropping systems management on productivity and the environment. The model simulates the soil water budget, soil-plant nitrogen budget, crop canopy and root growth, dry matter production, yield, residue production and decomposition, and erosion. Management options include: cultivar selection, crop rotation (including fallow years), irrigation, nitrogen fertilization, tillage operations (over 80 options), and residue management. The model is currently written in C++.
CS Suite is a set of programs for use in Environmental and Agricultural modeling.
- CropSyst is designed to simulate cropping systems (most crops can be simulated).
- ClimGen is designed to produce sythetic weather data sets commensurate with with historical data. It is intended to be usable worldwide and should produce satisfactory results for commonly habitable locations. It may work for some extreme climates (arctic and desert climates have not been evaluated by us) but may not adequately reproduce mountain climate (solar radiation or humidity may be problematic).
Two distribution package options are available:
- The CropSyst installation package includes all components including ClimGen (download is about 90Mb).
- ClimGen can be installed as a standalone package (download is about 50MB)
Because the installation packages are quite large, a Web-based installation option is provided. This installer will download and install individual files for the selected components. If you have a slow or unreliable Internet connection this installer can be interrupted and downloads resumed when convenient.
CropSyst is a process based model.
CropSyst (Cropping Systems Simulation Model) is a multi-year, multi-crop, daily time step crop growth simulation model, developed with emphasis on a friendly user interface, and with a link to GIS software and a weather generator (Stockle, 1996). Link to economic and risk analysis models is under development. The model’s objective is to serve as an analytical tool to study the effect of cropping systems management on crop productivity and the environment. For this purpose, CropSyst simulates the soil water budget, soil-plant nitrogen budget, crop phenology, crop canopy and root growth, biomass production, crop yield, residue production and decomposition, soil erosion by water, and pesticide fate. These are affected by weather, soil characteristics, crop characteristics, and cropping system management options including crop rotation, cultivar selection, irrigation, nitrogen fertilization, pesticide applications, soil and irrigation water salinity, tillage operations, and residue management.
The model is intended for crop growth simulation over a unit field area (m2). Growth is described at the level of whole plant and organs. Integration is performed with daily time steps
The following processes are simulated:
- Soil water budget including: water input, canopy and residue interception, runoff, infiltration, redistributioin, soil and residue evaporation, transpiration, deep percolation,
- Nitrogen budget including: Nitrate and Ammonium transformations (Net mineralization from organic matter and fresh straw and/or manure residues, nitrification and denitrification), Nitrogen transport, leaching and uptake,
- Crop growth is calculated based on radiation availability, temperature, water and N supply; and is expressed in terms of aboveground root biomass accumulation, leaf area and root depth and density development.
- Crop phenological development is calculated as a function of physiological time based on thermal time accumulation corrected by vernalization and photoperiod effects.
Key process areas: Plant growth Soil water balance and storage
PAR-biomass conversion ET/soil evaporation partitioning
Water dependent growth Nitrogen dependent growth
Light dependent growth Biomass production
Light interception Thermal time accumulation
Nitrogen uptake Water uptake
Management events Soil freezing and snowmelt
Runoff Soil erosion
Residue decomposition Nitrogen transformations
Vernalisation Photoperiod
Infiltration Harvest Index
CropSyst has been used for a wide range of model applications, including:
- Irrigation
- Climate change impacts, adaptation and mitigation
- Management practice exploration
The model is intended for crop growth simulation over a unit field area (m2). Growth is described at the level of whole plant and organs. Integration is performed with daily time steps using the Euler’s method . A complete description of the model is given in the user’s manual (Stockle and Nelson, 1994), which is currently being updated (Stockle and Nelson, 1996). The nitrogen and water submodels in CropSyst, and a general description of growth simulation have been presented elsewhere (Stockle et al., 1994). A new approach to determine crop nitrogen demand has been recently developed (Stockle and Debaeke, 1996). A finite difference solution of Richards equation to simulate water transport (as an alternative to existing cascading approach), and crop response to salinity has been also recently added (Ferrer, 1995). A general description of the model follows:
The water budget in the model includes precipitation, irrigation, runoff, interception, water infiltration, water redistribution in the soil profile, crop transpiration, and evaporation. Users may select different methods to calculate water redistribution in the soil profile and reference evapotranspiration. Water redistribution in the soil is handled by a simple cascading approach or by a finite difference approach to determine soil water fluxes. CropSyst offers three options to calculate grass reference ET. In decreasing order of required weather data input, these options are: the Penman-Monteith model , the Priestley-Taylor model , and a simpler implementation of the Priestley-Taylor model which only requires air temperature. Crop ET is determined from a crop coefficient at full canopy and ground coverage determined by canopy leaf area index.
The nitrogen budget in CropSyst includes N transformations, ammonium sorption, symbiotic N fixation, crop N demand and crop N uptake. Nitrogen transformations of net mineralization, nitrification and denitrification are simulated. The water and nitrogen budgets interact to produce a simulation of N transport within the soil. Chemical budgets (pesticides, salinity), including pesticide decay and absorption, are also kept and interact with the water balance. All balances within the model are check at each time step and errors are reported in case of departures within set threshold values.
Crop development is simulated based on thermal time required to reach specific growth stages. The accumulation of thermal time may be accelerated by water stress. Thermal time may be also modulated by photoperiod and vernalization requirements whenever pertinent. Daily crop growth is expressed as biomass increase per unit ground area. The model accounts for four limiting factors to crop growth: water, nitrogen, light, and temperature. Given the common pathway for carbon and vapor exchange of leaves, there is a conservative relationship between crop transpiration and biomass production. Following Tanner and Sinclair (1983), daily biomass accumulation is calculated as:
BT = KBT T / VPD [Eq. 1]
where BT is the transpiration-dependent biomass production (kg m-2 day-1), T is actual transpiration (kg m-2 day-1), and VPD is the mean daily vapor pressure deficit of the air (kPa). The Tanner-Sinclair relationship has the advantage of capturing the effect of site atmospheric humidity on transpiration-use efficiency. However, this relationship becomes unstable at low VPD; indeed it would predict infinite growth at near zero VPD. To overcome this problem, a second estimate of biomass production is calculated following Monteith (1977):
BL = e IPAR [Eq. 2]
where BL is the light-dependent biomass production (kg m-2 day-1), e is the light-use efficiency (kg MJ-1) and IPAR is the daily amount of crop-intercepted photosynthetically active radiation (MJ-1 m-2 day-1). Each simulation day, the minimum of B T and BL is taken as the biomass production for the day.
Although the parameter e (Eq. 2) includes the effect of the temperature regime prevailing during its experimental determination, temperature limitations during early growth are not captured and a single value is determined for the vegetative period or, more usually, for the entire growing season. However, more detailed measurements will show a decrease of e during early growth due to low temperature. Not accounting for this temperature effect may result in overprediction of biomass production during early growth, particularly in the case of winter crops. A temperature limitation factor is included in CropSyst to correct the value of e during this period, which is assumed to increase linearly from zero to one as air temperature fluctuates from the base temperature for development to an optimum temperature for early growth.
To account for nitrogen effects on biomass production, the minimum of BT and B L is used as base to determine the nitrogen-dependent biomass production (BN ):
BN = Min {BT , BL} [1 - (Npcrit - Np) / (Npcrit - Npmin)] [Eq. 3]
where BN is in kg m-2 day-1, Np is plant nitrogen concentration (kg kg-1), Npcrit is the critical plant N concentration (kg kg-1) below which growth is limited, and Npmin is the minimum plant nitrogen concentration (kg kg-1) at which growth stops. The values of Npcrit and Npmin (and also of maximum plant nitrogen concentration, needed to establish crop nitrogen demand) fluctuate as a function of accumulated biomass, following the concept of growth dilution. More detail on this is given by Stockle and Debaeke (1996).
The increase of leaf area during the vegetative period, expressed as leaf area per unit soil area (leaf area index, LAI), is calculated as a function of biomass accumulation, specific leaf area, and a partitioning coefficient. Leaf area duration, specified in terms of thermal time and modulated by water stress, determines canopy senescence. Root growth is synchronized with canopy growth, and root density by soil layer is a function of root depth penetration. The prediction of yield is based on the determination of a harvest index (grain yield/aboveground biomass). Although an approach based on the prediction of yield components could be used, the harvest index seems more conservative and reliable for a generic crop simulator. The harvest index is determined using as base the unstressed harvest index, a required crop input parameter, modified according to crop stress (water and nitrogen) intensity and sensitivity during flowering and grain filling.
Four input data files are required to run CropSyst: Weather, Soil, Crop, and Management files. Separation of files allows for an easier link of CropSyst simulations with GIS software. A Simulation Control file combines the input files as desired to produce specific simulation runs. In addition, the Control file determines the start and ending day for the simulation, define the crop rotations to be simulated, and set the values of all parameters requiring initialization. Definitions, usage, and range of variation of all parameters required by CropSyst are given in the User’s Manual (Stockle and Nelson, 1994 and 1996), and they are also available in the Help facility of the model interface.
The weather file includes information such as latitude, weather file code name and directories, rainfall intensity parameters (for erosion prediction), freezing climate parameters (for locations where soil might freeze), and local parameters to generate daily solar radiation and vapor pressure deficit values.
The Soil file includes surface soil Cation Exchange Capacity and pH, required for ammonia volatilization, parameters for the curve number approach (runoff calculation), surface soil texture (for erosion calculation), and five parameters specified by soil layer: Layer thickness, Field Capacity, Permanent Wilting Point, Bulk Density, and Bypass Coefficient. The latter is an empirical parameter to add dispersion to solute transport, particularly when using the cascading approach for soil water redistribution.
The Management file includes automatic and scheduled management events. Automatic events (irrigation and nitrogen fertilization) are generally specified to provide optimum management for maximum growth, although irrigation can be also set for deficit irrigation. Management events can be scheduled using actual date, relative date (relative to year of planting), or using synchronization with phenological events (e.g., number of days after flowering). Scheduled events include irrigation (application date, amount, chemical or salinity content), nitrogen fertilization (application date, amount, source- organic and inorganic-, and application mode- broadcast, incorporated, injected), tillage operations (primary and secondary tillage operations, which are basically related to residue fate), and residue management (grazing, burning, chopping, etc.).
The Crop file allows users to select parameters to represent different crops and crop cultivars using a common set of parameters. This file is structured in the following sections: Phenology (thermal time requirements to reach specific growth stages, modulated by photoperiod and vernalization requirements if needed), Morphology (Maximum LAI, root depth, specific leaf area and other parameters defining canopy and root characteristics), Growth (transpiration-use efficiency normalized by VPD, light-use efficiency, stress response parameters, etc.), Residue (decomposition and shading parameters for crop residues), Nitrogen Parameters (defining crop N demand and root uptake), Harvest Index (unstressed harvest index and stress sensitivity parameters), and Salinity Tolerance.
CropSyst provides a range of outputs, either as Daily, Growing Season, or Annual Reports.
| Growing Season Report | Daily Report | Annual Report |
 |
 |
 |
CropSyst also provides outputs for soil profile properties:
| Hydraullic | Ammonium |
| Water content | Nitrate |
| Water potential | Denitrification |
| Temperature | Nitrification |
| Incorporated residue | Mineralization NH4 |
| Incorported manure | Organic matter % |
| Salinity | Root fraction |
| Salt | Root biomass |
The CropSyst model has been applied in many countries and for many types of research applications.
The model code is written in Pascal (DOS version) and C++ (Windows and Windows 95 versions). An advanced user-friendly interface allows users to easily manipulate input files, verify input parameters for range errors and cross compatibility, create simulations, execute single and batch run simulations, customize outputs, produce text and graphical reports, link to spreadsheet programs, and even select a preferred language for the interface text. Simulations can be customized to invoke only those modules of interest for a particular application (e.g., erosion and nitrogen simulation can be disabled if not desired), producing more efficient runs and simplifying model parameterization. The model is fully documented (Stockle and Nelson, 1994, Stockle and Nelson, 1996) , and the manual is also available as a help utility from the CropSyst interface. CropSyst executable program, manual, and tutorials can be retrieved directly over the Internet (http://www.bsyse.wsu.edu//CS_Suite).
- The software package includes:
- The simulation model with optional runtime graphic display.
- Input parameter file editors for MS-Windows.
- ClimGen synthetic weather data generator.
- GIS module.
- CS Explorer.
- The model is fully documented including detailed descriptions on how to install and use the model user interface, required input parameters with indication of the most common valid ranges for each parameter, and a complete description of all the equations and assumptions included in the model.
Support programs
The programs are "add-on" data preparation, models and analysis modules for use with CropSyst simulation model.
ClimGen
a climate generator based on WGen. This is a Windows based program and generates CropSyst weather data files.
Arc/Info - CropSyst Coöperator
A utility for running simulation models over Arc/INFO GIS map coverages.
CANMS
Comprehensive Animal Nutrient Management System. This simulation model uses CropSyst for the cropping system element for on farm production of animal fodder.
Rural watershed model
Includes an erosion and a runoff simulation for any size watershed.
EI2CSDAT
A utility for generating CropSyst weather and location data files from the EarthINFO database CD-ROM.
CS-RISK
David Hennessy's risk analysis module for CropSyst. (Not currently actively maintained).
Ecologizer
A simple front-end shell for using CropSyst as an educational tool; includes color graphics screens and learning game playing mode. (Not currently actively maintained).
Arc CropSyst Cooperator
Is a simulator environment extension in the CS Suite of programs which facilitates GIS based spatially oriented simulation capabilities to te CropSyst simulatiton. It is designed to work with database files generated by Arc/Info or ArcView GIS software. In particular it uses the Polygon Attribute Table files produced by either Arc/Info or Arc/View.
ClimGen
Climatic Data Generator
Daily weather data are needed for many applications such as design of hydraulic structures, studies in watershed hydrology, determination of evaporation, assessment of the fate of pollutants in soils, and execution of weather driven crop simulation models. Many applications require long periods of daily weather data to account for environmental variability. These data usually include total solar radiation, maximum and minimum temperature, rainfall, wind-run, and some measurement of water vapor in the air (Acock and Acock, 1991). In many agricultural areas, such data are either incomplete or not readily available. Records may be of insufficient length, or only monthly summaries may be available. Therefore, it is desirable to generate sythetic daily weather data to meet such needs. Reliable generated daily weather data must have similar statistical characteristics as actual weather data for a given area.
CropSyst has been described as a 'robust' model, in that it has been used successfully in a large and diverse range of locations.
It is relatively straightforward to operate and whilst it has a large number of parameters, it only requires calibration for a few (6-7) key ones in order to create meaningful simulations.
Input data requirements are not excessive, but inevitably the utility ofthe model estimates is a function ofthe quality of the inputs.
CropSyst has been applied to several crops (corn, wheat, barley, soybean, sorghum, and lupins) and regions (Western US, Southern France, Northern and Southern Italy, Northern Syria, Northern Spain, and Western Australia), generally with good results and also a few problems (e.g. Donatelli et al., 1996), particularly for applications to conditions not simulated by the model (for example, water balance of cracking vertisols). The quality and/or level of detail of the available data is often a constraint for more thorough model evaluation. For more information on CropSyst validation the reader is referred to Stockle et al. (1994), Pala et al. (1996), Stockle et al. (1996), Stockle and Debaeke (1996), Donatelli et al., 1996, and (Ferrer, 1995). A few examples are given here. Table 1 summarizes validation work performed using data from US locations (Stockle et al., 1994) and from Tel Hadya (headquarters of ICARDA) in Northern Syria (Pala et al., 1996). Statistical analyses have indicated a satisfactory performance of CropSyst in these evaluations. Although not shown here, good agreement with observed seasonal evolution of ET, LAI, and biomass was found for Northern Syria data, which is fundamental to provide a good base for adequate simulation of biomass and yield at harvest time.
| Crop | Location | n | Obs. Mean kg/ha |
Sim. Mean kg/ha |
RMSE kg/ha |
RMSE/ Obs. Mean |
d | ||
| Wheat | Northern Syria | G | W/N | 16 | 2180 | 2410 | 550 | 0.25 | 0.92 |
| Wheat | Northern Syria | B | W/N | 16 | 7310 | 7090 | 870 | 0.12 | 0.96 |
| Wheat | Northern Syria | G | W/N | 16 | 1750 | 2080 | 560 | 0.32 | 0.90 |
| Wheat | Northern Syria | B | W/N | 16 | 7190 | 7140 | 1030 | 0.14 | 0.92 |
| Corn | Davis, CA ; Ft Collins, CO | G | W | 28 | 9831 | 9026 | 724 | 0.081 | 0.95 |
| Davis, CA ; Ft Collins, CO | B | W | 28 | 16460 | 16808 | 1246 | 0.076 | 0.954 | |
| Wheat | Logan, UT | G | W | 18 | 4100 | 4261 | 443 | 0.108 | 0.979 |
| Logan, UT | B | W | 18 | 8033 | 8460 | 1121 | 0.14 | 0.961 | |
| Wheat | Logan, UT | G | W/N | 30 | 4946 | 4963 | 383 | 0.077 | 0.975 |
| Logan, UT | B | W/N | 30 | 10293 | 10339 | 786 | 0.076 | 0.996 |
d = Willmott Index of Agreement (Willmott, 1982), ranging from 0 to 1, 1 being perfect agreement
B = Biomass, G = Grain Yield
W = Water treatments were imposed, N = Nitrogen treatments were imposed
Recent validation work was performed using data collected by the Institut National de la Recherche Agronomique (INRA) at Auzeville (near Toulouse), France (Stockle et al., 1996). These data are from long-term cropping system experiments conducted from 1983 to 1992 to evaluate crop rotations at three input levels. Input level I was unirrigated and received a minimum amount of fertilization; level II received limited irrigation, restricted to the most sensitive growth phases, and a moderate amount of fertilization; and level III received full irrigation and a large amount of fertilization. The objective was to evaluate the ability of CropSyst to predict ET, biomass, and yield of maize, sorghum, and soybean in response to weather (three dry years: 1986, 1989, and 1990) and soil water availability. In addition, simulations were performed using four combinations of two ET and two infiltration/redistribution submodels. The ET submodels corresponded to the Penman-Monteith (P-M) and Priestley-Taylor (P-T) equations, the latter applied with a VPD-dependent P-T coefficient. Infiltration/redistribution submodels corresponded to the cascading [C] method and the finite difference (FD) method. CropSyst was found able to simulate well the observed ET, biomass, and grain yield for the three crops, three years, and three irrigation input levels as given by Wilmott index of agreement consistently over 0.95. Results in Table 2, which include only crop yield simulations, show that the best simulations tended to be associated with the use of the P-M ET and the FD water transport submodels. However, results using the simpler methods are not too different, which is encouraging for applications where data input or computer CPU time constraints may be an issue.
| Sorghum | PM/FD | PM/C | PT/FD | PT/C | |
| Number of data points | 8 | 8 | 8 | 8 | |
| Observed average (Oavg) (kg/ha) | 7601 | 7601 | 7601 | 7601 | |
| Predicted average (kg/ha) | 8060 | 7852 | 8822 | 8679 | |
| RMSE (kg/ha) | 935 | 860 | 1531 | 1339 | |
| RMSE / Oavg | 0.123 | 0.113 | 0.201 | 0.176 | |
| Wilmott index of agreement | 0.963 | 0.968 | 0.911 | 0.931 | |
| Soybean | PM/FD | PM/C | PT/FD | PT/C | |
| Number of data points | 9 | 9 | 9 | 9 | |
| Observed average (Oavg) (kg/ha) | 2828 | 2828 | 2828 | 2828 | |
| Predicted average (kg/ha) | 2738 | 2819 | 2984 | 3093 | |
| RMSE (kg/ha) | 356 | 398 | 395 | 473 | |
| RMSE / Oavg | 0.126 | 0.141 | 0.140 | 0.167 | |
| Wilmott index of agreement | 0.975 | 0.965 | 0.972 | 0.955 | |
| Maize | PM/FD | PM/C | PT/FD | PT/C | |
| Number of data points | 9 | 9 | 9 | 9 | |
| Observed average (Oavg) (kg/ha) | 8026 | 8026 | 8026 | 8026 | |
| Predicted average (kg/ha) | 7494 | 7503 | 8029 | 8064 | |
| RMSE (kg/ha) | 1858 | 2043 | 2001 | 2108 | |
| RMSE / Oavg | 0.231 | 0.255 | 0.249 | 0.263 | |
| Wilmott index of agreement | 0.958 | 0.946 | 0.952 | 0.943 |
Work under progress is applying CropSyst to study the economic risk of selected crop rotations in the Palouse region of the Pacific Northwest, USA. This is a dryland region characterized by steep gradients of precipitation fluctuating from 200 to 500 mm., with weather conditions ranging from excellent to marginal for small grain production. Crop rotations evaluated include Winter Wheat/Spring Barley/Spring Peas, Winter Wheat/ Spring Peas, Winter Wheat/Spring Barley/Fallow, Winter Wheat/Fallow, and continuous Spring Barley. Thirty-year average yield of the different crops within typical rotations have been compared with long-term farm-level yield averages. Both the simulated average and the coefficient of variation for the three crops compared well with observed values. Comparisons for winter wheat and spring barley are shown across the rainfall gradient (Fig. 1).
Figure 1. Simulated and observed long-term yields for winter wheat and spring barley in typical rotations at the Palouse region of the Pacific Northwest, USA (S = Simulated, O = Observed).
CropSyst improvement is an ongoing and challenging process. Introduction of new management capabilities or new simulation modules is not very likely in the near future, but improvement of process simulation will be given priority. Validation with data sets from all over the world is of great interest to ensure robustness of the model. Test of the model with new crops such as potato (in progress), sugarbeet, alfalfa, canola, and others will be attempted as proper data sets become available. Cooperation with agronomists and agricultural scientists around the world is desirable for further progress.
CropSyst is freeling available for download. Registration is required at : http://modeling.bsyse.wsu.edu/rnelson/registration/CropSyst.htm
CropSyst can also be run using LINUX Wine - see registration details for instructions.
Two distribution package options are available:
- The CropSyst installation package includes all components including ClimGen (download is about 90Mb).
- ClimGen can be installed as a standalone package (download is about 50MB)
Because the installation packages are quite large, a Web-based installation option is provided. This installer will download and install individual files for the selected components. If you have a slow or unreliable Internet connection this installer can be interrupted and downloads resumed when convenient.
Details of versions and development can be found at: http://modeling.bsyse.wsu.edu/CS_Suite_4/documentation/history.html
Stöckle, C.O., Kemanian, A.R. and Kremer, C. (2008). On the Use of Radiation- and Water-Use Efficiency for Biomass Production Models. In: L.R. Ahuja, V.R. Reddy, S.A. Saseendran, and Q. Yu (Eds.). Advances in Agricultural Systems Modeling 1. ASA-SSSA-CSSA, Madison, WI.
Stöckle, C.O., Donatelli, M. and Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289-307.
Stöckle, C.O., Cabelguenne, M. and Debaeke, P. (1997). Comparison of CropSyst performance for water management in south western France using submodels of different levels of complexity. European Journal of Agronomy 7, 89-98.
By location:
Cameroon:-
Tingem, M., Rivington, M. and Bellocchi, G. (2009). Adaptation assessments for crop production in response to climate change in Cameroon. Journal of Agronomy for Sustainable Development 29, 247-256.
Tingem, M., Rivington, M., Bellocchi, G., Azam-Ali, S. and Colls, J. (2008). Effects of climate change on Cameroon crop production. Climate Research 36, 65-77.
Scotland:-
Rivington, M., Matthews, K.B., Bellocchi, G. and Buchan, K. (2006). Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agricultural Systems 88, 451-471.
Syria:-
Pala, M., Stöckle, C.O. and Harris, HC. (1996). Simulation of durum wheat (Triticum turgidum ssp Durum) growth under different water and nitrogen regimes in a Mediterranean environment using CropSyst. Agricultural Systems 51, 147-163.
USA:-
Pannkuk, CD., Stöckle CO and Papendick RI. (1998). Evaluating CropSyst simulations of wheat management in a wheat-fallow region of the US Pacific Northwest. Agricultural Systems 57, 121-134. DOI: http://dx.doi.org/10.1016/S0308-521X(97)00076-0
