School of Dew It 1996

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Agric ul tural .Sjmw~.~. Vol . 52, Nns 213, pp. 171 198, 1996Copyr Igh t t 1996 Pub l i shed by E lsewer Sc ience L tdPr in ted i n Grea t Br i ta in . A l l r i gh ts reserved0308-521X:96 $15 00 00PII: SO308-521X(96)OOOl l -X

The School o f de Wit Crop Growth Simulation Models:A Pedigree and Historical OverviewB. A. M. Bournan, H. van Keulen, H. H. van Laarh

& R. Rabbingeh DLO-Research Institute for Agrobiology and Soil Fertility, P.O. Box 14.

Wageningen 6700 AA, The NetherlandsDepartm ent of Theoretical Production Ecology, Agricultura l University, Wageningen.

The Netherlands(Received 8 March 1996)

A B S T R A C TI n t h i s p a p e r , a p ed i g r e e o f r h e c r op g r o w t h . s im u l a t i o n m o d e i s b y . t h eS c h o ol o f d e W i t i s p r e s en t e d . T h e o r ig i n s a n d p h i l os o p h y o f t h i s s c h o olw e t r t l ce d , f r om d e W i t s c la s s i c a l p u b l i ca t i o n o n m o d e l li n g p h o t os y n t h e s iso f l eq f c a n o p i es i n 1 9 6 5 . I t i s s h o w n h o w c h a n g i n g r es e a r ch g o u l s u n dpr i or i t i e s over t h e years h av e re su l t ed i n t he evo lu t i on o f u ped igree o f m o d e l s t h a t a r e s i m i l a r i n p h i l os o p h y h u t d @e r i n l ev e l o f c om p l e x it !, , t h ep r o ce s s es a d d r e s s e d a n d t h e i r ,f u n c t io n a l i t y l . I n t h e b e g in n i n g , m o d e l li n g H U Tm o t i v a t e d b y t h e q u e s t f or s ci en t t j i c i n s i g h t a n d t h e lv i . 4 t o q u a n t i f y . a n di n t e g r a t e h i o p h y s i c a l p r o ce s s es t o ex p l a i n t h e o b s er v e d v u r i u t i o n i n c r opg r o\ v t h . L a t e r , t h e e m p h a s i s o f u n d f u n d i n g j o r, a g r i cu l t u r a l r es ea r ch s h i ft edt o \ z l a r d s u t t i n g u c q u i r ed i n s i g h t s t o p r a c t ica l a n d op e r u t i on a l u s e . M o d e ld e v e l op m e n t b e ca m e l e d b y a d e m a n d , f o r t a c t i cu l u n d s t r a t e g ic d e ci s i on s u p -p o r t , ?, ie ll ,f i ,r ec u s t i n g , l a n d z o n a t i o n a n d e x p l o r a t i v e s c e n a r i o s t u d i e s . M o d e l -l i n g d e v e l op m e n t s , fo r d t y e r e n t p r o d u c t io n s i t u u t i on s a r e i l lu s t r a t e d u s i n gt h e m o d el s t h e u u t h o r s c on s i d e r m o s t i m p o r t a n t , i.e. B A C R O S , S D C R O S ,W O F O S T , M A C R O S a n d L I N T E L , b u t r ef er en c e i s a l so m a d e t o o th e rm o d e l s . F i n u l l y , c om m e n t s w e m a d e a b o u t t h e u s e f u l n e s s a n d ~ p p l i cu h i l it j o f t h e s e m o d e l s q f t er n e a r l y 3 0 y e a r s o f t k v e i o p m e n t . a n d s o m e ,f u t u r e c ou r . s e so f a c t i on a r e s u g g e s t ed . C o p y r i g h t t $ I 9 9 6 P u b l i s h e d h , , E l s r v i e r S c ie n c e L t t l

INTRODUCTIONBy the end of the 196O s, computers had evolved sufficiently to allow a ndeven to stimulate the first attempts to synthesize detailed knowledge on

171


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17 2 B. A. M. Bouman, H. v an Keulen, H. H. v an Laar, R . Rabbinge

plant physiological processes, in order to explain the functioning of cropsas a whole. Insights into various processes were expressed using math-ematical equations and integrated in so-called simulation models. Thesefirst (heuristic) models were meant to increase the understanding of cropbehaviour by explaining crop growth and development in terms of theunderlying physiological mechanisms. Over the years, new insights anddifferent research questions motivated the further development of simula-tion models. In addition to their explanatory function, the applicability ofwell-tested models for extrapolation and prediction was quickly recog-nized, and more application-oriented models were developed. Forinstance, demands for advisory systems for farmers and scenario studiesfor policy makers resulted in the evolution of models geared towardstactical and strategic decision support, respectively (Rabbinge, 1986; vanKeulen & Penning de Vries, 1993). Now, crop growth modelling andsimulation have become accepted tools for agricultural research (Rab-binge, 1986; Seligman, 1990). A wide variety of crop models has beendeveloped all over the world to serve many different purposes, with majormodelling groups in the USA in the former project IBSNAT (Interna-tional Benchmark Sites Network for Agrotechnology Transfer) (Uehara &Tsuji, 1993; Tsuji et a l . , 1994) in Australia with the system APSIM(Agricultural Production system SIMulator) (McCown et al . , 1995) andin The Netherlands at Wageningen. In Wageningen, crop growth model-ling was initiated and developed by the late C. T. de Wit (deceased 1993)and his co-workers at the Department of Theoretical Production Ecologyof the Wageningen Agricultural University (TPE-WAU) and the DLO-Research Institute for Agrobiology and Soil Fertility (AB-DLO; andCAB0 and IBS) (de Wit, 1970). Since then, many scientists have followedin his footsteps and have taken up crop modelling. In response to chan-ging research goals and policies over the years, a range of crop models hasemerged that often confuses the outsider who is merely looking for theWageningen crop growth model. In this paper, therefore, a historicaloverview is given of the pedigree and developments of the School of deWit models, with a brief description of some of the most significantmodels. The overview is limited to dynamic simulation models for growthand development of field crops I. Scientific details of the various modelsare not given here as they have been amply described in books and litera-ture referenced in this paper (e.g. the series of Simulation Monographs,

A more complete compilation of (Europea n) agro-ecosystem models was recently startedwithin the framewo rk of the concerted action for the development and testing of quanti-tative methods for research on agricultura l systems and the environment (CAM ASE )(Plentinger & Penning de Vries, 199 5).


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Crop growth simulation models 113

published by PU DO C between 1971 and 1993). Finally, the authors pre-sent a personal review of the usefulness and applicability of these modelsafter more than 30 years of development.

SYSTEM AND MODEL CHARACTERISTICSA model is a simplified representation of a system, and a system is a lim-ited part of reality that contains interrelated elements (de W it, 1982~). Thesystem w e consider here is the agricultural cropping system. In 1982, deW it and Penning de Vries proposed a classification of this system into fourproduction situations:

Production situation I: Potential production. Growth occurs in conditionswith ample supply of water and nutrients and growth rates are determinedsolely by weather conditions (solar radiation and temperature).

Production situation 2: W ater-limited production. Grow th is limited byshortage of water during at least part of the growing period but nutrientsare in ample supply.

Production situation 3: Nitrogen-limited production. Grow th is limitedby shortage of nitrogen (N) during at least part of the growing season, andby water or weather conditions for the rest of the time.

Production situation 4: Nutrient-limited production. Growth is limitedby a shortage of phosphorus (P), or of other minerals for at least part ofthe growing season, and by N, water or weather conditions for the rest.

In all four situations, pests, diseases or weeds may further reduce cropyield. In practice, actual production situations are difficult to assign to anyof these four situations, but this practical simplification of schema tizingspecific situations allows progress to be made, particularly at the start of astudy (de W it & Penning de Vries, 1982; Rabb inge, 1986; van Duiven-booden & Gosseye, 1990). Recently, a new classification of agriculturalproduction systems wa s introduced in the C. T. de W it Gradua te School ofProduction Ecology (Ra bbinge, 1993): potential growth is defined by theconcentration of atmospheric CO Z, solar radiation, temperature and cropcharacteristics; attainable growth is determined by the limiting factors ofwater and nutrients; actual growth is reduced below the attainable by fac-tors such as weeds, pests, diseases and pollutants (Fig. 1). The developmentof models for each of these situations proceeded at its own rate and in itsown direction, depending on the research goals and objectives at the time.

Technically, most models of the School of de W it are characterized bythe labels dynamic, hierarchical, state-variable based, explanatoryand deterministic. They are dynamic because rates of change in thesystem (e.g. growth rate) are calculated as a function of time, using time


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174 B. A. M. Bouman, H. van Keulen, H. H. van Laar, R. Rabbinge

I I VI1.5 I 5 /I 10 ton ha-Production level

Fig. 1. The relationship among po tential, attainable and actual yield and defining, limitingand growth reducing factors (Rabbinge , 199 3).

coefficients that are typical for the processes that are described (de Wit,1982b). The time coefficient, which has units of time, is the inverse of thecharacteristic rate of a process. This inclusion of time differentiates themfrom static models in which, for example, crop production is statisticallyregressed on weather variables. Second, they divide the system understudy into hierarchical levels of organization, e.g. cells, organs, plants,crop. These hierarchical levels exhibit characteristic behaviours that resultfrom the integration of lower-level processes (Loomis et al . , 1979). Forinstance, a leaf light-response characteristic is the result of processes at thelower levels of cells and chloroplasts (Sinclair et a l . , 1977), and canopyphotosynthesis is the sum of photosynthesis of all the individual leaves (deWit, 1965). Different hierarchical levels can be combined in one modelprovided that time-coefficients that are appropriate for each level of hier-archy are used (de Wit, 1970). Mathematical modelling entails quantita-tive integration of the mechanisms at the various hierarchical levelsto provide an explanation of system behaviour. Third, the system ischaracterized by a set of state variables (e.g. weights) that are updated ateach iteration or time-step, by rate variables (e.g. carbon flux). The time-step is typically one quarter of the time coefficient. Values of the ratevariables are calculated from information about the current state of thesystem and from external, environmental (e.g. solar radiation) andauxiliary variables (e.g. leaf area index) (Fig. 2). Fourth, the models areexplanatory because the calculations involving rate variables are based on


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solar airirradiation temperatureI I

i

structuralbiomass stor. organs

roots I

I rate IFig. 2. Diagra m of the relations in a typical School of de Wit crop grow th model(SUCRO S) for potential production. Boxes indicate state variables, valves rate variables.circles auxiliary variables, solid lines (arrows) the flow of matter and dotted lines the flow

of information.

knowledge of the underlying physical, physiological and biochemical pro-cesses. Only when knowledge is lacking, or a simplification is required, aredescriptive, i.e. statistical, relationships used. However, within a hier-archical structure, descriptive relationships at lower levels become expla-natory at higher levels (Loomis e t a l. , 1979). Fifth, the dynamic simulationmodels of the School of de W it are deterministic because all plants in thecrop are considered to be of the same genotype, and exposed to the sam einitial and environmental (soil, weather) conditions. Crop characteristicsand environmental conditions are therefore expressed as a single set ofmodel parameter values and external model input data, respectively. The


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17 6 B. A. M. Boum an, H. v an Keulen, H . H. v an Laar, R . Rabbinge

TABLE 1Steps in the Conceptual Phases of Model Development: Conceptual or Preliminary,

Comprehensive and SummaryConceptual or prelimi nary phase

1 Formulation of objectives2 Definition of system limits3 Conceptualization of the system (states, rates, auxiliary variables, forcing vari-ables etc.)Comprehensive phase

4 Quantification through literature, proc ess experimen t or estimation of the relationbetween rate and forcing variables, states or auxillary variables5 Mo del construction (definition of comp uter algorithm)6 Mo del verification, i.e. testing the intended behaviour of the modelSummary phase

7 Evaluation of model perform ance, i.e. testing the model in parts or as a whole ,using independent experiments on system level8 Sensitivity analysis (numerical or structural)9 SimplificationIO Formulation of decision rules or forecasting mode ls to be used for practicalapplicationsAfter Rabbinge & de Wit (1989).

apparently stochastic nature of real biological systems, expressed ingenetic, temporal and spatial variation, can be mimicked using numericaltechniques such as Monte Carlo simulation (Klepper & Rouse, 1991;Bouman, 1994; Rossing et al., 1994).

In describing the developments of the crop growth models of the Schoolof de Wit, we follow the classification of development phases introducedby Penning de Vries (1980) who distinguished preliminary, comprehensiveand summary models. These three phases of model development aredescribed in Table 1. Preliminary, or conceptual, models reflect currentscientific knowledge, and are simple in structure because of incompleteknowledge of the component processes. With increasing insight,preliminary models can evolve into comprehensive models that representsystems in which the essential elements are thoroughly understood andwhich contain large amounts of information. In plant physiologicalresearch the main purpose of both preliminary and comprehensive modelsis to formalize and integrate knowledge of plant growth processes, to testhypotheses by comparing model results with experiments, to structureresearch programmes and to extrapolate from the laboratory to the field.In brief, the aim is to increase our understanding of crop performance.After the first comprehensive models had been built and tested, however,


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they were quickly recognized as powerful tools for exploring situations andpossibilities of crop production that were almost impossible to investigateusing the conventional methods and techniques of experimentation.Because comprehensive models are typically complex and hardly accessibleto potential users, this stimulated the development of summary models. Asummary model can be regarded as a model of a (comprehensive) model, inwhich essential elements are simplified and aspects that are only marginallyimportant are ignored. Summary models have typically been developedin response to application-oriented research questions (e.g. tactical andstrategic). Their use, for example, may be in decision support systems forpest and disease management and for plant nutrient management.

MODEL RESPONSES TO CHANGING RESEARCH QUESTIONSThe early years (1965-1980): gaining insightCrop modelling evolved in the late 1960s as a means of integratingknowledge about plant physiological processes in order to explain thefunctioning of crops as a whole. Researchers set themselves the goal ofquantifying qualitative speculations about the effects of canopy structure,solar radiation and transpiration on canopy photosynthesis. The outcomeof their work was a set of preliminary and comprehensive models forproduction situations 1 and 2.Potential production situation: ELCROS and BACROSIn 1965, de Wit published the classic report Photosynthesis of leaf canopies(Fig. 3), in which a procedure was described that allowed calculation ofdaily photosynthesis of a canopy with known characteristics for any timeand place on earth, from the relevant meteorological data. Although thiscalculation was not a crop growth model, it used the hierarchical, explana-tory approach in that canopy photosynthesis was calculated by integratingindividual leaf photosynthesis over depth in the canopy on the basis ofknowledge of the underlying processes. The integration required the identi-fication and parameterization of leaf angle distribution functions and thedescription of light penetration into the canopy for different conditions ofsolar illumination. The estimates of photosynthesis made with this proce-dure were tested for the potential production situation, and it was found thatthe measured rates of canopy photosynthesis could approach the calculatedtheoretical values (Alberda, 1968; Alberda & Sibma, 1968).

Photosynthesis of leaf canopies, laid the foundation for the develop-ment of dynamic models of crop growth. In retrospect, the emphasis on


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1910

1975

1980

1985

1990

1995

B. A. M. Boum an, H . v an Keulen, H . H. v an Laar, R . Rabbinge

Photosynthesis f leaf canop ~s-;1

ARID CROP

ARID CROP(SAHEL)

PAPRAN

Fig. 3. Pedigree of crop growth simulation models of the School of de Wit , 196 5-19 95.Models in bold boxes have been lead models for the development of other crop models.

Mode l names are explained in the text.

photosynthesis in crop growth simulation has remained throughout theyears and the models of the School of de Wit are all photosynthesis-driven. One of the first dynamic crop growth simulators was ELCROS(ELementary CROp Simulator), (de Wit et al . , 1970), which was used forexploratory studies into the potential production levels of crops undervarious conditions. This preliminary model contained a detailed, mostlymechanistic, canopy photosynthesis section, a component describing organgrowth rates and preliminary ideas about crop respiration. Two main-stream developments in the following years contributed to the evolution ofELCROS into the first comprehensive model: (i) the quantification ofenergy requirements for growth and maintenance processes, both related tocrop respiration; and (ii) the detailed elaboration of crop micrometeorologyin the model MICROWEATHER by Goudriaan (1977). Penning de Vrieset a l . (1974) showed that respiration coefficients for growth processes couldbe derived, using straightforward stoichiometry, from the biochemicalcomposition of the biomass. Insight into maintenance processeswas improved (Penning de Vries, 197.5) but its quantification remained


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essentially experimental (Penning de Vries, 1980). It wa s only in the early1990s that more insight into the various processes that contribute to main-tenance respiration wa s gained, and that their rates could be theoreticallyderived (Bouma, 1995). In MICR OW EAT HER, crop microweather wasexplained as a function of properties of plants and soils, and of the weatherconditions prevalent at some height above the canopy. The elements con-sidered were solar radiation, energy and ma ss balances, wind speed andturbulence. The insights incorporated in this model allowed transpirationand photosynthesis to be driven by the aerial environment in and above thecrop canopy. The comprehensive model BA CRO S (BAsic CRO p growthSimulator) was developed from ELCRO S (de Wit et al., 1978; Penning deVries & van Laar, 1982). BA CRO S simulates the growth and transpirationof field crops in the vegetative phase u nder potential production conditions.It wa s designed for grasses such as cereals and specific parameters andfunctional relationships specify the actual species under consideration(Dayan et al., 1981). Although the carbon balance and transpiration aredescribed mecha nistically, partitioning of assimilate and the developmentof leaf area are represented empirically. BA CR OS simulates the growth ofa crop over a whole (vegetative) growing season. For more detailed stu-dies, the model PHO TON (simulation of daily PHO TOsyn thesis andtranspiratioN) wa s derived from BA CR OS to simulate photosynthesis.respiration and transpiration over the course of the day. The time-stepsused in any model should be determined by the sma llest time coefficient inthe system. PHO TON therefore uses time-steps of seconds as stomata1behaviour is considered explicitly. BA CR OS , on the other hand, uses aloop to equilibrate this fast process and can thus use a time-step of anhour without the processes that have much larger time coefficients losingaccuracy and realism. One of the major scientific discoveries using thesecomprehensive models wa s the effect of CO ? on stomata1 opening andhence on photosynthesis and transpiration (de W it et al., 1978). In theseearly years of crop model development, BA CR OS became the focus forfurther development (Fig. 3).Wuter-limited production situation: ARID CROPUp until about 1970, the main function of crop mode ls wa s to explain cropfunctioning in a quantitative way, and to explore the potential productionat different geographical locations. One of the first application-orientedresearch challenges for modelling wa s the Dutch/Israeli project Actual andPotential Production of Sem i-Arid G rasslands (APPS AG ), that wa s initi-ated by de W it in 1970 (Alberda et al., 1992). In this project, crop m odellingwa s used to quantify and formalize, as far as possible, the relevant processesinvolved in water-limited production, and to extrapolate and apply the


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resultant knowledge to agricultural production systems (van Keulen et al . ,1982a). ARID CROP (van Keulen, 1975), which was based on the conceptselaborated in ELCROS and BACROS, was developed to simulate thegrowth and water use of fertilized natural pastures in the Mediterraneanregion. This comprehensive model successfully coupled a water balancemodel, which was later developed into SAHEL (Soils in semi-Arid Habitatsthat Easily Leach; Stroosnijder, 1982), to a crop growth model via aninterface between rooting and water uptake. The model describes soilmoisture transport in a simplified way using the unconventional tippingbucket approach, and incorporates a summary sub-model of the carbonbalance and potential canopy transpiration developed from BACROS tocompute the transpiration coefficient of the crop. Potential and actual ratesof crop transpiration were then combined and used to derive the water-limited crop growth rate. ARID CROP simulates a complete crop growthcycle from germination, through the stages of vegetative and reproductivegrowth and senescence until the death of the crop. However, the processesof senescence, assimilate partitioning and leaf area development wererepresented using relationships that were largely empirical. A revised ver-sion of the model was shown to conform satisfactorily with experimentalobservations for a range of environmental conditions (van Keulen et a l . ,1981). ARID CROP was successfully incorporated into an integratedmodel of a grazing system comprising separate management and biologicalsections, which was used to examine the consequences of contrasting man-agement strategies in intensive agropastoral systems in a semi-arid region(Ungar, 1990). ARID CROP was also used in the project ProductionPrimaire du Sahel (PPS) (de Wit, 1975; Penning de Vries & Djiteye, 1982).The conclusion that production potential was limited in many years bynutrient deficiency rather than by lack of water (Breman & de Wit, 1983)was an important outcome of this modelling work in Israel and the Sahel.The mid dle years (1980-1990): towards practical applicationsDuring these years, the general emphasis and funding of agriculturalresearch started to shift from understanding and explaining towards prac-tical application of the results. Important research issues at that time, suchas agro-ecological zonation, quantitative land evaluation and yield predic-tion, required exploratory data that were almost impossible to obtain usingconventional methods. The existing comprehensive models, however, werenot very suitable for this purpose because many of the processes weredescribed in great detail, with a corresponding need for comprehensiveinput data, which were often unavailable, and extended computing time.For production situations where either growth-defining factors alone


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played a role or where water wa s the limiting factor, knowledge of therelative importance of the constituent processes allowed the derivation ofeffective summ ary models. For other production situations and levelshowever, e.g. where sh ortage of nitrogen wa s the limiting factor, there wasstill insufficient basic knowledge and the first preliminary and comprehen-sive models had first to be developed.Potential and water-limitedproduction situation: SUCROS, WOFOST andMACROSSUCROS The first summary model presented was SUC ROS (Simpleand Universal CRO p growth Simulator; van Keulen et al., 19826).SU CR OS is a simple growth model with a time-step of 1 day, that reliesheavily for its functional relationships on more detailed process-basedmodels such as BAC ROS . The original version of SUC RO S simulated drymatter production of a crop from emergence to maturity under potentialproduction conditions. Like BA CR OS , SU CR OS is universal in naturebecause the physical and physiological processes described are applicableto a wide range of environmental conditions. SU CR OS has been appliedto various crops, e.g. whea t, p otato and soybean (van Keulen et al.,1982b) by altering the crop parame ters. An updated version, SU CR OS 87.wa s published in 1989, with crop parameters for spring whea t, w interwhea t, m aize, potato and sugar beet (Spitters et al., 1989). In 1992, thelatest versions of SUC ROS for spring wheat were presented: SUC ROS lfor potential production, and SU CR OS 2 for water-limited production(van Laar et al., 1992; Goudriaan & van Laar, 1994). In the latter mo del.SUC RO Sl is linked to the soil water balance module SAHEL. Subse-quently, SU CR OS became the lead model for further simplification anddevelopment of specific purpose-oriented models, e.g. INT ERC OM(INTE Rplant CO Mpe tition) for the interaction between field crops andweeds (Kropff & van Laar, 1993) and SBjWW FLEVO (Sugar Beet/WinterW heat in FLEV Oland) which used remotely sensed inputs for growthmonitoring (Boum an, 1992; Fig. 3).WOFOST One of the first application-oriented models to be derivedfrom SUC RO S was WO FOST (World Food STudies). This model wasdeveloped by the Centre for Wo rld Food Studies, an interdisciplinarygroup of scientists from the Depa rtment of Development Economics of theFree University of Am sterdam in cooperation with TPE-W AU andCAB O-DLO in Wageningen. The aim of the Centre was to explore thepossibilities of increasing the agricultural productivity of developingcountries (van Keulen & Wolf, 1986; van Diepen et al., 1988). In thedevelopment of the successive ve rsions of W OFO ST, the emp hasis wa s on


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182 B. A. M. Bouman, H. v an Keulen, H. H. v an Laar, R . Rabbinge

their practical application for studies on quantitative land evaluation,regional yield forecasting, analysis of risk and inter-annual yield variation,and the quantification of the effects of climate change (see Hijmans et al.(1994) for a review of these WOFOST applications). As in SUCROS, theprocess descriptions are universal and the model is tailored to various cropsby altering the crop parameters (van Heemst, 1988). Because of its appli-cation-orientation, a number of user-friendly features were introducedin WOFOST. For example, there was a crude geographical informationsystem facility for presenting the output of the simulations in the form ofmaps, and a menu interface allowed the crop type and production situationand the crop, soil and weather input data files to be selected easily.MACROS and ORYZA The MACROS modules (Modules of anAnnual CROp Simulator; Penning de Vries et a l . , 1989) for crops in thesemi-humid tropics were developed as part of the SARP project (Simula-tion and Systems Analysis for Rice Production). One of the aims of thisproject was to transfer the technology of simulation and systems analysisto multi-disciplinary teams of scientists in Southeast Asia (ten Berge, 1993).MACROS aided these objectives in two ways: first, as an instructional andtraining vehicle for the transfer of agrotechnology and systems analysis;and second, as a tool for the development and application of models in thecropping systems, potential production, water, nutrients and roots,and insect pests, diseases and weeds research themes. MACROS consistsof a series of basic modules for potential and water-limited crop growthand for the water balance of soils in both freely draining (SAHEL) andwater-logged conditions (SAWAH; ten Berge et al . , 1991). Like SUCROSand WOFOST, the model is generic and parameters are given for a largenumber of crops. Compared with SUCROS, however, MACROS hasretained more of the character of a comprehensive model. An importantfeature of MACROS in its role as a training tool, was its transparent,modular structure that allowed scientists to choose and combine appro-priate crop growth and water balance modules for addressing their specificproduction situations and research questions. Case-studies based on theMACROS modules were presented at a number of international work-shops (Penning de Vries et al., 1991) at the end of the second phase ofSARP (1987-1991). In the third and last phase of the project (1992-1995),the application of the models that had been developed was emphasized byconcentrating research efforts into six application programmes. Thesewere: (i) agro-ecological zonation and characterization; (ii) optimization ofcrop rotations and water use; (iii) application of models in plant breedingprogrammes; (iv) evaluation of the impact of climate change on rice pro-duction; (v) optimization of nitrogen management; and (vi) optimization


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of pest manag ement. A series of rice models based on the M AC RO Smodules and on SU CR OS wa s developed to serve these specific applica-tions under the generic name O RY ZA (e.g. Kropff et al., 1994).N-limited production situation: PAPRANNitrogen dynamics in soils and crops were studied under se mi-aridconditions in on-going projects in Israel and the Sahel. However, progresswa s slow because the biological and soil chemical processes involved arecomplex and difficult to quantify. The first modelling work, w hich wasbased on a relatively simple set of supply and deman d functions, resulted inthe description of N uptake and redistribution in plant tissue (Seligman etal., 1975). Com bining these descriptions with those of AR ID CR OP led tothe development of the preliminary model PA PRA N (Production of AridPastures limited by RA infall and Nitrogen) for annual pasture productionin semi-arid conditions in which grow th is limited by rainfall and nitrogen(N) (Seligman & van Keulen, 198 1; van Keulen, 1982). PAP RA N is basi-cally a soil&water balance model where p lant grow th is closely related bothto the amount of water transpired by the canopy and to its N status, andwhere N transformations in the soil are represented by immobilization andmineralization processes (van Keulen, 1982). Continuation of this line ofwork resulted in the development of a comprehensive model for springwheat at this production level (van Keulen & Seligman, 1987).Rece nt develop me nts (1990-1 995): operationalizationThe summ ary models initiated in the 1980s are increasingly being usedoperationally as a result of the dema nd by policy m akers and land mana gersfor data that can only be produced by models. Typical applications includeagro-ecological zonation, regional yield forecasting and scenario studies forexploring the effect of environmental or socioeconomic changes on agri-culture. Moreover, the increasing pressure from agricultural funding agen-cies to prove the operational applicability of modelling has led to researchbeing driven by the development of new technology. This change ofemph asis has introduced new requirements for models and has highlightedthe importance of software quality, an issue that had been recognized in theearly years of modelling, e.g. by Arnold & de W it (1976).Operational applications: WOFO ST, LINTULWOFOST Two major successful applications of WO FOS T in the 1990swere in the policy study Ground for Choices (Netherlands ScientificCouncil for Government Policy, 1992; Rabbing e & van Latestijn, 1992)and in the Monitoring Agriculture with Remote Sensing project (M AR S)


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of the Joint Research Centre (JRC) of the Commission of the EuropeanCommunities (Meyer-Roux & Vossen, 1994). In the first study, the modelwas used to explore regional yield potentials in the EU under differentmanagement intensities. The results were used to generate technicalcoefficients for different crop rotations (de Koning et al., 1995). Thesecoefficients were then used in the linear programming model GOAL(General Optimal Allocation of Land use; Scheele, 1992) to optimize landuse and production systems under four contrasting economic scenarios.The outcomes of these simulations differed widely in terms of the landrequired, the costs of production, the employment situation and the use offertilisers and pesticides. In the MARS project, WOFOST was integratedwith a geographical information system (GIS) to produce the crop growthmonitoring system (CGMS) for operational yield forecasting for the EU(van Diepen, 1991; Vossen, 1995). For this purpose, WOFOST wasupgraded to version 6.0 with new routines developed in SUCROS (seebelow) and new functionalities developed especially for the MARS project(Supit et al., 1994). For crop yield forecasting, research is continuing byexploring possibilities of integrating crop growth models with remotesensing data to improve the forecasting accuracies (e.g. Bouman, 1995).LZNTUL For many studies at scales ranging from the regional to theglobal, existing summary models needed further simplification because theavailability and quality of model input data were often found to be moreconstraining than knowledge of the basic processes incorporated in them.Spitters (1990) argued that SUCROS could be further simplified byincorporating only those processes that affect the major determinants ofgrowth, and laid the foundations for a modelling approach that wouldlater be baptised LINTUL (Light INTerception and UtiLization; Spitters& Schapendonk, 1990; Kooman, 1995). LINTUL was the first deviationfrom the photosynthesis-based models of the De Wit school. In theLINTUL models, total dry matter production is calculated using theMonteith approach (Monteith, 1969, 1990) in which crop growth rate iscalculated as the product of interception of radiation by the canopy and alight-use efficiency (LUE), which should more correctly be called a drymatter: radiation quotient (Russell et al., 1989). The LUE can often beconsidered constant over the growing season and a property of the crop ofinterest. For regional studies, LINTUL-type models have the advantagethat data input requirements are drastically reduced and model para-meterization is facilitated. The LINTUL approach was used, at therequest of the International Potato Center (CIP), for the agro-ecologicalcharacterization of global potato production to help target research atproduction problems in those regions where potato cultivation is most


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promising (van Keulen & Stol, 1995). Recently, Penning de Vries et al.(1995) used the LINTUL approach in a world food study in whichpotential and water-limited food production wa s estimated for the year2040 for 15 major regions of the world.Model quality: SUCROSSU CR OS has been used to test ways of improving model and softwarequality. A set of rules and utilities for programm ing the typical crop growthmodels of the School of de W it wa s developed in FO RTR AN and calledthe FOR TRA N Simulation Environment (FSE; van Kraalingen, 1993).FSE supports the state-variable approach by organizing process equationsinto tasks for initialization, updating of state variables, calculation of ratevariables and calculation of end-of-season characteristics such as harvestindex. These tasks are broadly equivalent to the INITIAL, DY NA M ICand TER MIN AL sections of the early simulation languages (Brennan c,tal., 1970) with the added clarity of distinguishing between the calculationof rate and state variables in what w ould be the DY NA MIC section.Moreover, the model equations in FSE are separated as much as possiblefrom the supporting code that takes care of such tasks as data rea ding, datachecking and output writing. A standard format has been introduced totake care of input a nd output data, and modellers using FSE are encour-aged by its structure to program in an orderly and modular fashion. InSUCROS87 (Spitters et al., 1989), process descriptions had been organizedinto subroutines. Further development has continued in the 1990s and hasresulted in a series of interchangeable, process description routines, e.g. forlight interception, photosynthesis and transpiration, which are universallyapplicable but that differ in the level of detail and type of input data needed.On the basis of the specific model purpose and the availability of input data(especially relevant in an operational context!), model users can select theappropriate routines to link to their main model. Finally, standardizedprocedures are being developed to test the quality of the software itself, e.g.to check for programm ing errors and confirm the reproducibility of results,and to compare the model outputs with experimental data.

THE CROP MODEL RECORD: USEFULNESS ANDAPPLICABILITY

Increasing insightCrop growth modelling started 30 years ago with the aim of increasing ourinsight into crop growth processes by a synthesis of knowledge expressed


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1 8 6 B . A. M. Bouman, H. van Keulen, H. H. van Laar, R. Rabbinge

using mathematical equations. This is still the main aim of developingcrop growth models. Simulation models are powerful tools for testing ourunderstanding of crop performance by comparing simulation results andexperimental observations, thus making explicit gaps in our knowledge.Experiments can then be designed to fill these gaps. This function of cropgrowth modelling is difficult to evaluate because it is almost impossible topredict what would have been our knowledge of crop performance had themodel paradigm not been so pervasive in research (Seligman, 1990). Crit-ics such as Passioura (1973) and Monteith (1981) have suggested thatcomplex models cannot show anything that could not be deduced by theuse of straightforward common sense. Crop modellers, however, insistthat no other technique is as powerful for synthesizing knowledge on sub-processes and increasing our understanding of whole crop behaviour (deWit et al., 1978; Loomis et al., 1979; Penning de Vries et al., 1989). Well-tested crop growth models can be used to explore, in a quantitative way,the relative importance of crop characteristics, such as physiological andmorphological traits, and environmental characteristics, in a manner thatwould not be possible in field experimentation. Sometimes, the simula-tions produce counterintuitive results. An example of this is found in theeffects of stomata1 behaviour on crop growth and development when theenvironment changes (de Wit et al., 1978). These interesting cases stimu-late further thinking and experimentation and are good evidence forrefuting the criticisms of this type of modelling.Operational applicationsYield predictionValidated models can be used in application-oriented research by scientistsand in operational applications where their users are managers or othernon-scientists. Most of the application-oriented research using the cropgrowth models of the School of de Wit has been related to the problemof yield prediction (Seligman, 1990) including world food productionstudies (Buringh et al., 1979; Penning de Vries et al., 1995), agro-ecologi-cal zonation (Aggarwal, 1993; van Keulen & Stol, 1995) and explorationsof the effects of climate change on crop production (Wolf, 1993; Matthewset al., 1995). Operational applications include the use of the crop growthmonitoring system (CGMS) by the Joint Research Centre for producingmonthly yield predictions for the regions of the EU (Vossen, 199.5), andthe use of WOFOST by Dutch consultancy agencies in land use planningprojects (personal communication). Two reasons may be postulated forthe relative success of crop models in yield prediction. First, models arethe only means of systematically exploring the production potential of


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Crop grow th sim ulation m odel.7 187

agricultural crops in historical or predicted future weather conditions.Second, there is generally no way of testing the validity of predictions atregional or global sc ales. An interesting exception to this second issue wa sthe recent comparison in the M AR S project of yields predicted by CG MSand by conventional regression techniques with long-term yield statistics(de Koning et al., 1993). The results indicated that, for most crops, theaccuracy of yield predictions made using only time-series regression mod-els could not yet be improved by adding the output of the W OFO STmodel at the NU TS-O (country) or NU TS1 (primary administrativeregion) level. How ever, because the accuracy of the official yield statistic sis unknow n, it wa s impossible to separate the effects of unrealistic simu-lations from errors in the statistics. The actual user of CG MS , i.e. theJRC , however, found many additional benefits of the modelling approachthat were not included in the scientific evaluation. The W OFO ST predic-tions were thus timely(!), objective, quantitative, and consistent over largeareas (Heath, 1991; Vossen (JRC ), personal com munication). Addition-ally, the model simulations provided additional mea ns of comparison withother sources of information, such as field-sampling, remote se nsing andexpert knowledge, which are all used to derive the final yield estimates forthe EU . Similar views on the benefits of integrated techniques (includingcrop growth models) have been reported by Horie et al. (1992) for riceyield forecasting in Japan , and Gommes (199 1) for early warning systemsin Africa, Asia and Latin Am erica.Plant breedingCrop growth models have been used in plant breeding to simulate theeffects of changes in the morphological and physiological characteristics ofcrops and thus to aid in the identification of ideotypes (Donald, 1968) fordifferent environments (Dingkuh n et al., 1993; Hunt, 1993; Kropff er al.,1995). Hunt (1993) and Palanisamy et al. (1993) suggested that cropgrowth models that have been parameterized for new cultivars in fieldexperiments can be used to simulate the long-term yield stability of thesecultivars at a location under the expected range of climatic conditions.This technique holds out the promise of reducing the cost of breedingprogramm es by limiting the number and years of expensive, multi-locationtrials that are currently required to ensure statistical reliability. A recentreview of literature on the use of modelling in potato breeding ledEllis&he & Hoogendoorn (1995) to the conclusion that simulationmodelling can contribute to the efficiency of potato breeding programm es,because modelling analyses complex characteristics, indicates the mostpromising components for selection, can forecast plant growth under var-ious conditions, including biotic and abiotic stress, and helps, therefore.


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to formulate breeding strategies. Breeding objectives can be fine-tuned forparticular regions or environments on the basis of model evaluationsfor these circumstances (e.g. Kooman, 1995). The involvement of plantbreeders in recent publications on modelling and breeding suggests achange in attitude since Seligmans remark in 1990 that simulation resultsusing crop growth models have rarely inspired breeders to adapt theirbreeding programmes (Seligman, 1990).Crop managementCrop growth simulation models have been used in numerous studies tohelp farmers in day-to-day, i.e. tactical, decision making. They have beenused to investigate the effects of management options such as sowing time,plant population density, irrigation timing and frequency and fertiliserapplications in different environmental conditions on long-term meanyield and yield probability (e.g. Ungar, 1990; Carberry et al., 1992, 1993;Keating et al., 1993; Aggarwal et al., 1994; Aggarwal & Kalra, 1994;Rotter & Dreiser, 1994). In some cases, these studies have stimulated fieldexperimentation to test the outcomes predicted in the simulations (tenBerge et al., 1994). However, the operational application of crop growthmodels to support tactical decision making has generally not yet beensuccessful (Seligman, 1990) with the notable exception of the areas ofirrigation scheduling and water management (van Keulen & Penning deVries, 1993) and pest and disease management (e.g. the Epidemics Predic-tion and Prevention System (EPIPRE); Rabbinge & Rijsdijk, 1983).Treatment of the latter type of model is outside the scope of this paper andreaders are referred to relevant publications elsewhere, (e.g. Rabbinge etal., 1990; Kropff & Lotz, 1992; Teng & Savary, 1992).

Recently, researchers have started to apply the results of crop models totactical decision making using knowledge based systems such as expertsystems and decision support systems. These software systems have beenpromoted since the mid-1980s as a major breakthrough, opening newhorizons in decision support (Schiefer & da Silva, 1995). However, despitecontinuing reports of successful developments and prototyping, Hilhorst& Manders (1995) found that the overall acceptance of the technology inagriculture in The Netherlands is still limited. They suggested that a mainreason for this lack of acceptance was the knowledge-intensive nature ofsuch systems. Such systems are of strong interest to research organizationsand this has resulted in: (i) an undue emphasis on problems of a scientificrather than a practical interest; (ii) poor functionality for non-specialistusers; and (iii) an evolutionary development path which does not accordwith modern software engineering standards. However, these deficiencies,which might equally well apply to the failure of crop growth models in


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Crop growth simulation mod& 189

operational tactical decision support (see above), are now being addressed.Three other reasons can explain this particular failure of models. First,because one of the major sources of yield variation in agriculture is thevariability in weather conditions, success of decision support systemsdepends largely on their ability to predict future weather. Even today,weather predictions are at best reasonably accurate for only a few daysahead and decision support systems have to rely on probability analysesusing long-term historical or generated weather data (van Keulen &Penning de Vries, 1993). Second, the lack of accurate input data for soiland crop characteristics, particularly in respect of their spatial variability,is often a constraint on successful model applications (van Noordwijk &Wadman, 1992). Third, models are often used for field conditions whereasthey were developed for rather strictly defined hypothetical productionsituations (potential production, water-limited production, etc.) inuniform fields. In farmers fields, several limiting and yield-reducing fat-tors may occur simultaneously, so that the conditions fall outside theboundary conditions or domain of validity of the models. This raises themodellers dilemma that for ease of application in a particular practicalsituation, models should be as simple as possible and require only a smallnumber of input data, but that on the other hand, they should be complexand flexible enough to be able to represent the complex effects of the widerange of potentially interacting yield-limiting and yield-reducing factorsthat might be important for the crop of interest. For situations of poten-tial production, the summary models developed from the comprehensivecrop growth models of the School of de Wit satisfactorily predict cropbehaviour. However, although the processes of photosynthesis and growthrespiration are satisfactorily modelled mechanistically, aspects of main-tenance respiration and morphogenesis (e.g. organ growth, assimilatepartitioning and leaf area development) are still not well understood andlittle progress has been made since the release of BACROS and ARIDCROP. In water-limited conditions, the main effect of water shortage onthe reduction in photosynthesis is well understood and has been satisfac-torily incorporated in summary models. However, recent experiences inmodelling rice growth in the SARP project have indicated the need forfurther study and the inclusion of crop-specific adaptation mechanisms(Wopereis, 1993).

Major gaps still exist in our knowledge of the effects of nutrient-limita-tion and it is not yet possible to use mechanistic models directly for farmlevel applications (van Keulen & Stol, 199 1; van Keulen & Penning deVries, 1993). Therefore the operational use of deterministic models that canhandle the even more complex situations that typify actual farming condi-tions is still a long way off, and poses new challenges for the years ahead.


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One of the ways in which progress is being made in the operationalizationof crop growth models is by using other sources of information, such asfield observations, measurements or remote sensing data, for periodicadjustment of the state variables. There have been, for example, successes inthe prediction and prevention of epidemics (Rabbinge & Rijsdijk, 1983)water management and irrigation scheduling (De Falcis et al . , 1990; Hill,1991) and crop growth monitoring (Bouman, 1995). These examples alsoillustrate the importance of limited but clear-cut objectives by focusing onspecific problems in tactical decision making.

POSTSCRIPTThis paper deals with a historical overview of crop growth simulationmodels from the School of de Wit. It was, however, not our intention tosuggest that these models are better than other models that have beendeveloped elsewhere (see Introduction). The authors thank R. S. Loomisfor his helpful comments and suggestions on earlier versions of themanuscript. G. Russell is thanked for polishing the papers English andsuggesting some improvements by the way.

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