Models of crop growth. Crop growth simulation model WOFOST

Transcription

1 COST action 734, CLIVAGRI: WG4 workshop on crop model comparison, TU Berlin, November 2008 Models of crop growth. Crop growth simulation model WOFOST Reimund P. Rötter

2 CONTENTS 1) Crop growth simulation capabilities & limitations of the C.T. De Wit Wageningen School of models 2) Structure, input and output of WOFOST 3) Comparison WOFOST to other modelling concepts and families 4) In brief : How to evaluate crop growth simulation models? 2

3 1. Crop growth simulation - a tool of (Agro-)systems analysis SYSTEM: limited part of reality that contains interrelated elements MODEL: a simplified representation of a system Models & expert systems SIMULATION: the art of building mathematical models and study their properties in reference to those of the systems (de Wit, 1982) 3

4 1. Crop growth simulation Why is dynamic crop growth simulation useful? Generally : (i) to disentangle and explain effects of yield-determining / limiting and reducing factors; (ii) to integrate fragmented agronomic with biophysical data & extrapolate in time & space In climate change impact and adaptation research: their capability allows analysis of crop response to T, P (SM), CO2 and changed management conditions => Pre-requisite : Proper evaluation (calibration, sensitivity analysis, validation..) 4

5 1. Crop growth simulation - Evolution of CT de Wit Wageningen models 1960/70s: ELCROS/BACROS comprehensive models: very close linkage: experimentation and simulation model development 1980s: Development of (summary) models for diverse applications from determining yield potentials (SOW) to improving pest, nutrient and water management (Israel, NL), e.g. SUCROS, WOFOST, ORYZA, PAPRAN, INTERCOM; 1990s till present: Further scientific-technical development (e.g. WOFOST, ORYZA) & wide applications at field /farm (e.g. In SARP; Kropff et al) and at higher aggregation levels: regional yield forecasting (CGMS-Europe), in regional land use (scenario) studies (e.g. Ground for Choices EU-15; SOLUS, SysNet/IRMLA) together with interdisciplinary teams in Asia, Africa, Latin America (Aggarwal/Rötter/Bouman et al). 5

6 1. Crop growth simulation - capabilities and limitations. Hierarchical modeling approach of C.T. de Wit School 6

7 2. WOFOST 7.1: Structure, input and output WOFOST : World Food Studies (Wageningen- Amsterdam); and centre piece of a European Crop Growth Monitoring System (CGMS) 7

8 2. WOFOST. Structure, input, output Annual field crops (10) in Version 7.1 for Europe: - Wheat - Grain Maize - Barley - Sugar beet - Potatoes - Field Beans - Soybean - Rapeseed - Sunflower - Rice (about 12 crops more for tropical regions) 8

9 2. WOFOST. Structure, input, output A schematic representation of photosynthesis module, SUCROS approach. Potential production (grey) and water-limited production. (Source: Van Ittersum et al. 2003) 9

10 SOIL PHYSICAL DATA CROP PARAMETER FILE START specifications RUN FILE 10

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12 Crop parameters (to calibrate for crop cultivars/ local conditions) TSUM1 tempsum above basetemp for vegetative growth. TSUM2 tempsum for reproductive gr SLATB -- specific leaf area FRTB etc. Partitioning R, L, S, SO AMAX --- Max leaf CO2 assimilation r. TMP,TMN assim reduction d.t. temp EFF light use efficiency CEFT correction factor ET DEPNR crop group water depletion 2. WOFOST. Sturcture, input, output. Crop parameters Crop parameters (for CO 2 : 560 ppm) -SLA: (-5% from 1990: 350 ppm) -CFET: (- 5% from 1990) -AMAX: 50 (+25% to 1990) 12

13 Estimation of crop yield from NPK uptake as applied in QUEFTS /WOFOST Two borderlines indicating maximum dilution (D) and accumulation (A) of N (left), P(centre) and K (right) example for rice after Janssen et al., 1990) 13

14 2. WOFOST. Structure, input, output. 14

15 3. Comparison WOFOST to other modelling concepts and families DSSAT (Decision support system of agrotechnology transfer) (ref.: Jones et al., 2003) Check: to dowload latest model version. DSSAT software simulates the growth of crops like peanuts, sunflowers, sugarcane, wheat, soybeans, rice, tomatoes, sorghum, millet, barley, potatoes, corn, black-eyed peas, beans. (examples: Ceres-Maize, -Rice, -Wheat; J Ritchie, S Otter-Nacke; U Singh etc.): In use for more than 15 years as a result of IBSNAT project (motivation; knowledge integration extrapolation in space); -- new, modular cropping system model (DSSAT-CSM Cropping system model) => For other crop models (e.g. APSIMm CROPSYST and STICS), see e.g. and or ME406 course: crop model lecture 15 notes & refs. UH.

16 3. Comparison WOFOST to other modelling concepts and families Development stages (driving variables: Temp/cultivar; co-determining factors (e.g. daylength in Wofost; soil temp or soil moisture in CERES?...); Assimilation and dry matter increase (processes: leaf photosynthesis, LAI, maintenance and growth respiration (SUCROS approach); descriptive: light interception dry matter relationship; ---- effect CO2 concentration on photosynthesis (via AMAX and EFF) Partitioning of assimilates /dry matter to different plant organs (dev. stage dependent, fixed fractions or?. Root : shoot (and leaf: stem: storage organs); Leaf area development (calc. from leaf dry weight, specific leaf area for closed canopy; exponential growth unclosed.. ends at LAI 1; dependency on temperature; rel. Leaf death rate --- vs green area ) Soil water balance (tipping bucket/cascading or Richards approach; no. of layers, ETo e.g. According to Penman) Nutrient limitation (Static QUEFTS approach for NPK in WOFOST; dynamic N appproach; comprehensive approaches in CERES/DSSAT-CSM, HERMES...) 16

17 4. How to evaluate? Statistical evaluation of model performance (as recommended by e.g. Willmott 1981): - Summary measures: observed and predicted means, STD, sample size, intercept and slope of simple regression between dependent and independent(=oberved) variable; and coefficient of determination - Difference measures: mean absolute error MAE, mean square error MSE, systematic and unsystematic parts of MSE and RMSE, and the index of agreement (d) Apply models judiciously; scientist with background in major related disciplines models are not fool-proof..., Combine, whenever possible, experimentation and modelling 17

18 4. How to evaluate? Calibration, sensitivity analysis, validation - calibration: essential step in model development aimed at adjusting or deriving parameter values on the basis of experimental data -- procedure (see, successive steps for crop models ); calibration can take site differences and minor ecological processes into account, it is essential to reduce calibration for that purpose (of curve fitting) - sensitivity analysis: as a form of behavioral analysis and part of model evaluation, carried out in order to assess the influence of selected key ( critical ) parameters on, usually, most important output variables (sensitivity indicators) what-if; irrespective of real system behaviour; - validation: the examination whether a model derived from the analyses of some systems is capable of describing other systems or, in the narrow sense, how well the model outputs fit (new) data (de Wit, 1982; Joergensen, 1983); most difficult step in evaluation 18

19 4. How to evaluate? An example from Finland Observed vs simulated turnip rape yields kg ha pot yld simyld1 OBSyld1 simyld years 19

20 8. Detailed case. Oilseed rape, d: 120, Jokioinen, FI (1) Current data Model sensitivity, OSR, mean Pot Yld (indicator) (32 years) (2) TSUM2 modified 2500 (3) Crop par CO2:560 ppm Kg ha TWSO (4) T (5) T+2, P+10%, 560 ppm (6) T+4 (7) T+4, 560 ppm Current CF: TSUM2 CF: 560 ppm FCL: T+2 T+2, 560 ppm Change in variable T+4 T+4, 560 ppm 20

21 Climate change impact on agriculture application to modern turnip rape varieties in Finland Effect T.O.P. and soil type on mean yield (n=32) Pot Yld WL Yld (fine soil) WL Yld (coarse soil) Pot Yld (T+2)

22 (Averaged) yield changes for major crops in the Rhine Basin (scenario BAU-best) Simulated water-limited yield increase yield (t ha-1) (%) current future winter wheat potato sugarbeet silage maize ryegrass (Lolium perenne) 22

23 Maize yield response and climatic risk in Kenya 23