School Of Dew It 1996

ELSEVIER

PII:

Agricultural .Sjmw~.~. Vol. 52, Nns 213, pp. 171 198, 1996 CopyrIght t 1996 Published by Elsewer Science Ltd Printed in Great Britain. All rights reserved 0308-521X:96 $15 00’ 00 SO308-521X(96)OOOll-X

The ‘School of de Wit’ Crop Growth Simulation Models: A Pedigree and Historical Overview B. A. M. Bournan,”

“DLO-Research

‘Department

H. van Keulen,” H. H. van Laarh & R. Rabbingeh

Institute for Agrobiology and Soil Fertility, Wageningen 6700 AA, The Netherlands

of Theoretical

Production Ecology, Agricultural The Netherlands (Received

8 March

P.O. Box 14.

University,

Wageningen.

1996)

ABSTRACT In this paper, a pedigree of rhe crop growth .simulation modeis by. the ‘School of de Wit’ is presented. The origins and philosophy of this school we trtlced,from de Wit’s classical publication on modelling photosynthesis of leqf canopies in 1965. It is shown how changing research gouls und priorities over the years have resulted in the evolution of u pedigree of‘ models that are similar in philosophy hut d@er in level of complexit!,, the processes addressed and their,functionalityl. In the beginning, modelling HUT motivated by the quest for scienttjic insight and the lvi.4 to quantify. and integrate hiophysical processes to explain the observed vuriution in crop gro\vth. Later, the emphasis of undfundingjor, agricultural research shifted to\zlardsputting ucquired insights to practical and operutional use. Model development became led by a demand,for tacticul und strategic decision support, ?,iell,fi,recusting, land zonation and explorative scenario studies. Modelling developments ,for dtyerent production situutions are illustrated using the models the uuthors consider most important, i.e. BACROS, SD’C’ROS, WOFOST, MACROS and LINTEL, but reference is also made to other models. Finully, comments we made about the usefulness and ~pplicuhilitj’of’ these models qfter nearly 30 years of’tkveiopment. and some,future cour.ses of action are suggested. Copyright t$ I996 Published h,, Elsrvier Science Lttl

INTRODUCTION By the end of the 196Os, computers even to stimulate the first attempts

had evolved sufficiently to allow and to synthesize detailed knowledge on 171

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

plant physiological processes, in order to explain the functioning of crops as a whole. Insights into various processes were expressed using mathematical equations and integrated in so-called simulation models. These first (heuristic) models were meant to increase the understanding of crop behaviour by explaining crop growth and development in terms of the underlying physiological mechanisms. Over the years, new insights and different research questions motivated the further development of simulation models. In addition to their explanatory function, the applicability of well-tested models for extrapolation and prediction was quickly recognized, and more application-oriented models were developed. For instance, demands for advisory systems for farmers and scenario studies for policy makers resulted in the evolution of models geared towards tactical and strategic decision support, respectively (Rabbinge, 1986; van Keulen & Penning de Vries, 1993). Now, crop growth modelling and simulation have become accepted tools for agricultural research (Rabbinge, 1986; Seligman, 1990). A wide variety of crop models has been developed all over the world to serve many different purposes, with major modelling groups in the USA in the former project IBSNAT (International Benchmark Sites Network for Agrotechnology Transfer) (Uehara & Tsuji, 1993; Tsuji et al., 1994) in Australia with the system APSIM (Agricultural Production system SIMulator) (McCown et al., 1995) and in The Netherlands at Wageningen. In Wageningen, crop growth modelling was initiated and developed by the late C. T. de Wit (deceased 1993) and his co-workers at the Department of Theoretical Production Ecology of the Wageningen Agricultural University (TPE-WAU) and the DLOResearch Institute for Agrobiology and Soil Fertility (AB-DLO; and CAB0 and IBS) (de Wit, 1970). Since then, many scientists have followed in his footsteps and have taken up crop modelling. In response to changing research goals and policies over the years, a range of crop models has emerged that often confuses the outsider who is ‘merely’ looking for the Wageningen crop growth model. In this paper, therefore, a historical overview is given of the pedigree and developments of the ‘School of de Wit’ models, with a brief description of some of the most significant models. The overview is limited to dynamic simulation models for growth and development of field crops I. Scientific details of the various models are not given here as they have been amply described in books and literature referenced in this paper (e.g. the series of ‘Simulation Monographs’, ‘A more complete compilation of (European) agro-ecosystem models was recently started within the framework of the concerted action for the development and testing of quantitative methods for research on agricultural systems and the environment (CAMASE) (Plentinger & Penning de Vries, 1995).

Crop growth simulation models published by PUDOC between 1971 and 1993). Finally, sent a personal review of the usefulness and applicability after more than 30 years of development.

SYSTEM

AND

MODEL

113

the authors preof these models

CHARACTERISTICS

A model is a simplified representation of a system, and a system is a limited part of reality that contains interrelated elements (de Wit, 1982~). The system we consider here is the agricultural cropping system. In 1982, de Wit and Penning de Vries proposed a classification of this system into four production situations: Production situation I: Potential production. Growth occurs in conditions with ample supply of water and nutrients and growth rates are determined solely by weather conditions (solar radiation and temperature). Production situation 2: Water-limited production. Growth is limited by shortage of water during at least part of the growing period but nutrients are in ample supply. Production situation 3: Nitrogen-limited production. Growth is limited by shortage of nitrogen (N) during at least part of the growing season, and by water or weather conditions for the rest of the time. Production situation 4: Nutrient-limited production. Growth is limited by a shortage of phosphorus (P), or of other minerals for at least part of the growing season, and by N, water or weather conditions for the rest. In all four situations, pests, diseases or weeds may further reduce crop yield. In practice, actual production situations are difficult to assign to any of these four situations, but this practical simplification of schematizing specific situations allows progress to be made, particularly at the start of a study (de Wit & Penning de Vries, 1982; Rabbinge, 1986; van Duivenbooden & Gosseye, 1990). Recently, a new classification of agricultural production systems was introduced in the C. T. de Wit Graduate School of Production Ecology (Rabbinge, 1993): potential growth is defined by the concentration of atmospheric COZ, solar radiation, temperature and crop characteristics; attainable growth is determined by the limiting factors of water and nutrients; actual growth is reduced below the attainable by factors such as weeds, pests, diseases and pollutants (Fig. 1). The development of models for each of these situations proceeded at its own rate and in its own direction, depending on the research goals and objectives at the time. Technically, most models of the ‘School of de Wit’ are characterized by the labels ‘dynamic’, ‘hierarchical’, ‘state-variable based’, ‘explanatory’ and ‘deterministic’. They are dynamic because rates of change in the system (e.g. growth rate) are calculated as a function of time, using time

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

I

1.5

I

I’ 5

/I

VI 10

ton ha-’

Production level

Fig. 1. The relationship

among potential, attainable and actual yield and defining, and growth reducing factors (Rabbinge, 1993).

limiting

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 the characteristic rate of a process. This inclusion of time differentiates them from static models in which, for example, crop production is statistically regressed on weather variables. Second, they divide the system under study into hierarchical levels of organization, e.g. cells, organs, plants, crop. These hierarchical levels exhibit characteristic behaviours that result from the integration of lower-level processes (Loomis et al., 1979). For instance, a leaf light-response characteristic is the result of processes at the lower levels of cells and chloroplasts (Sinclair et al., 1977), and canopy photosynthesis is the sum of photosynthesis of all the individual leaves (de Wit, 1965). Different hierarchical levels can be combined in one model provided that time-coefficients that are appropriate for each level of hierarchy are used (de Wit, 1970). Mathematical modelling entails quantitative integration of the mechanisms at the various hierarchical levels to provide an explanation of system behaviour. Third, the system is characterized by a set of state variables (e.g. weights) that are updated at each iteration or time-step, by rate variables (e.g. carbon flux). The timestep is typically one quarter of the time coefficient. Values of the rate variables are calculated from information about the current state of the system and from external, environmental (e.g. solar radiation) and auxiliary variables (e.g. leaf area index) (Fig. 2). Fourth, the models are explanatory because the calculations involving rate variables are based on

Crop growth

simulation

175

models

air temperature

solar irradiation

I

I

i

structural biomass

stor. organs roots

I rate

I

I

Fig. 2. Diagram of the relations in a typical “School of de Wit” crop growth model (SUCROS) 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 processes. Only when knowledge is lacking, or a simplification is required, are descriptive, i.e. statistical, relationships used. However, within a hierarchical structure, descriptive relationships at lower levels become explanatory at higher levels (Loomis et al., 1979). Fifth, the dynamic simulation models of the ‘School of de Wit’ are deterministic because all plants in the crop are considered to be of the same genotype, and exposed to the same initial and environmental (soil, weather) conditions. Crop characteristics and environmental conditions are therefore expressed as a single set of model parameter values and external model input data, respectively. The

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

176

TABLE 1

Steps in the Conceptual

Phases of Model Development: Comprehensive and Summary

Conceptual

or Preliminary,

Conceptual or preliminary phase

1 2 3

Formulation of objectives Definition of system limits Conceptualization of the system (states, rates, auxiliary variables, forcing variables etc.) Comprehensive phase

4

5 6

Quantification through literature, process experiment or estimation of the relation between rate and forcing variables, states or auxillary variables Model construction (definition of computer algorithm) Model verification, i.e. testing the intended behaviour of the model Summary phase

7 8 9 IO

Evaluation of model performance, i.e. testing the model in parts or as a whole, using independent experiments on system level Sensitivity analysis (numerical or structural) Simplification Formulation of decision rules or forecasting models to be used for practical applications

After Rabbinge & de Wit (1989).

apparently stochastic nature of real biological systems, expressed in genetic, temporal and spatial variation, can be mimicked using numerical techniques 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 ‘School of de Wit’, we follow the classification of development phases introduced by Penning de Vries (1980) who distinguished preliminary, comprehensive and summary models. These three phases of model development are described in Table 1. Preliminary, or conceptual, models reflect current scientific knowledge, and are simple in structure because of incomplete knowledge of the component processes. With increasing insight, preliminary models can evolve into comprehensive models that represent systems in which the essential elements are thoroughly understood and which contain large amounts of information. In plant physiological research the main purpose of both preliminary and comprehensive models is to formalize and integrate knowledge of plant growth processes, to test hypotheses by comparing model results with experiments, to structure research 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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177

they were quickly recognized as powerful tools for exploring situations and possibilities of crop production that were almost impossible to investigate using the conventional methods and techniques of experimentation. Because comprehensive models are typically complex and hardly accessible to potential users, this stimulated the development of summary models. A summary model can be regarded as a model of a (comprehensive) model, in which essential elements are simplified and aspects that are only marginally important are ignored. Summary models have typically been developed in response to application-oriented research questions (e.g. tactical and strategic). Their use, for example, may be in decision support systems for pest and disease management and for plant nutrient management.

MODEL

RESPONSES

TO CHANGING

The early years (1965-1980):

RESEARCH

QUESTIONS

gaining insight

Crop modelling evolved in the late 1960s as a means of integrating knowledge about plant physiological processes in order to explain the functioning of crops as a whole. Researchers set themselves the goal of quantifying qualitative speculations about the effects of canopy structure, solar radiation and transpiration on canopy photosynthesis. The outcome of their work was a set of preliminary and comprehensive models for production situations 1 and 2. Potential production situation: ELCROS and BACROS In 1965, de Wit published the classic report ‘Photosynthesis of leaf canopies’ (Fig. 3), in which a procedure was described ‘that allowed calculation of daily photosynthesis of a canopy with known characteristics for any time and place on earth, from the relevant meteorological data’. Although this calculation was not a crop growth model, it used the hierarchical, explanatory approach in that canopy photosynthesis was calculated by integrating individual leaf photosynthesis over depth in the canopy on the basis of knowledge of the underlying processes. The integration required the identification and parameterization of leaf angle distribution functions and the description of light penetration into the canopy for different conditions of solar illumination. The estimates of photosynthesis made with this procedure were tested for the potential production situation, and it was found that the measured rates of canopy photosynthesis could approach the calculated theoretical values (Alberda, 1968; Alberda & Sibma, 1968). ‘Photosynthesis of leaf canopies’, laid the foundation for the development of dynamic models of crop growth. In retrospect, the emphasis on

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

1965

‘Photosynthesisof leaf canop~s -;1

19’10

1975 ARID CROP

1980

ARID CROP (SAHEL) PAPRAN

1985

1990

1995

Fig. 3. Pedigree of crop growth simulation models of the “School of de Wit”, 1965-1995. Models in bold boxes have been “lead” models for the development of other crop models. Model names are explained in the text.

photosynthesis in crop growth simulation has remained throughout the years and the models of the ‘School of de Wit’ are all photosynthesisdriven. One of the first dynamic crop growth simulators was ELCROS (ELementary CROp Simulator), (de Wit et al., 1970), which was used for exploratory studies into the potential production levels of crops under various conditions. This preliminary model contained a detailed, mostly mechanistic, canopy photosynthesis section, a component describing organ growth rates and preliminary ideas about crop respiration. Two mainstream developments in the following years contributed to the evolution of ELCROS into the first comprehensive model: (i) the quantification of energy requirements for growth and maintenance processes, both related to crop respiration; and (ii) the detailed elaboration of crop micrometeorology in the model MICROWEATHER by Goudriaan (1977). Penning de Vries et al. (1974) showed that respiration coefficients for growth processes could be derived, using straightforward stoichiometry, from the biochemical processes composition of the biomass. ‘Insight into maintenance was improved’ (Penning de Vries, 197.5) but ‘its quantification remained

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179

essentially experimental’ (Penning de Vries, 1980). It was only in the early 1990s that more insight into the various processes that contribute to maintenance respiration was gained, and that their rates could be theoretically derived (Bouma, 1995). In MICROWEATHER, crop microweather was explained as a function of properties of plants and soils, and of the weather conditions prevalent at some height above the canopy. The elements considered were solar radiation, energy and mass balances, wind speed and turbulence. The insights incorporated in this model allowed transpiration and photosynthesis to be driven by the aerial environment in and above the crop canopy. The comprehensive model BACROS (BAsic CROp growth Simulator) was developed from ELCROS (de Wit et al., 1978; Penning de Vries & van Laar, 1982). BACROS simulates the growth and transpiration of field crops in the vegetative phase under potential production conditions. It was designed for grasses such as cereals and specific parameters and functional relationships specify the actual species under consideration (Dayan et al., 1981). Although the carbon balance and transpiration are described mechanistically, partitioning of assimilate and the development of leaf area are represented empirically. BACROS simulates the growth of a crop over a whole (vegetative) growing season. For more detailed studies, the model PHOTON (simulation of daily PHOTOsynthesis and transpiratioN) was derived from BACROS to simulate photosynthesis. respiration and transpiration over the course of the day. The time-steps used in any model should be determined by the smallest time coefficient in the system. PHOTON therefore uses time-steps of seconds as stomata1 behaviour is considered explicitly. BACROS, on the other hand, uses a loop to equilibrate this fast process and can thus use a time-step of an hour without the processes that have much larger time coefficients losing accuracy and realism. One of the major scientific discoveries using these comprehensive models was the effect of CO? on stomata1 opening and hence on photosynthesis and transpiration (de Wit et al., 1978). In these early years of crop model development, BACROS became the focus for further development (Fig. 3). Wuter-limited production situation: ARID CROP Up until about 1970, the main function of crop models was to explain crop functioning in a quantitative way, and to explore the potential production at different geographical locations. One of the first application-oriented research challenges for modelling was the Dutch/Israeli project ‘Actual and Potential Production of Semi-Arid Grasslands’ (APPSAG), that was initiated by de Wit in 1970 (Alberda et al., 1992). In this project, crop modelling was used to quantify and formalize, as far as possible, the relevant processes involved in water-limited production, and to extrapolate and apply the

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

resultant knowledge to agricultural production systems (van Keulen et al., 1982a). ARID CROP (van Keulen, 1975), which was based on the concepts elaborated in ELCROS and BACROS, was developed to simulate the growth and water use of fertilized natural pastures in the Mediterranean region. This comprehensive model successfully coupled a water balance model, which was later developed into SAHEL (Soils in semi-Arid Habitats that Easily Leach; Stroosnijder, 1982), to a crop growth model via an interface between rooting and water uptake. The model describes soil moisture transport in a simplified way using the unconventional ‘tipping bucket’ approach, and incorporates a summary sub-model of the carbon balance and potential canopy transpiration developed from BACROS to compute the transpiration coefficient of the crop. Potential and actual rates of crop transpiration were then combined and used to derive the waterlimited crop growth rate. ARID CROP simulates a complete crop growth cycle from germination, through the stages of vegetative and reproductive growth and senescence until the death of the crop. However, the processes of senescence, assimilate partitioning and leaf area development were represented using relationships that were largely empirical. A revised version of the model was shown to conform satisfactorily with experimental observations for a range of environmental conditions (van Keulen et al., 1981). ARID CROP was successfully incorporated into an integrated model of a grazing system comprising separate management and biological sections, which was used to examine the consequences of contrasting management strategies in intensive agropastoral systems in a semi-arid region (Ungar, 1990). ARID CROP was also used in the project ‘Production Primaire du Sahel’ (PPS) (de Wit, 1975; Penning de Vries & Djiteye, 1982). The conclusion that production potential was limited in many years by nutrient 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 middle years (1980-1990):

towards practical applications

During these years, the general emphasis and funding of agricultural research started to shift from understanding and explaining towards practical application of the results. Important research issues at that time, such as agro-ecological zonation, quantitative land evaluation and yield prediction, required exploratory data that were almost impossible to obtain using conventional methods. The existing comprehensive models, however, were not very suitable for this purpose because many of the processes were described in great detail, with a corresponding need for comprehensive input data, which were often unavailable, and extended computing time. For production situations where either growth-defining factors alone

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models

181

played a role or where water was the limiting factor, knowledge of the relative importance of the constituent processes allowed the derivation of effective summary models. For other production situations and levels however, e.g. where shortage of nitrogen was the limiting factor, there was still insufficient basic knowledge and the first preliminary and comprehensive models had first to be developed.

Potential and water-limitedproduction situation: SUCROS, WOFOST and MACROS The first summary model presented was SUCROS (Simple SUCROS and Universal CROp growth Simulator; van Keulen et al., 19826). SUCROS is a simple growth model with a time-step of 1 day, that relies heavily for its functional relationships on more detailed process-based models such as BACROS. The original version of SUCROS simulated dry matter production of a crop from emergence to maturity under potential production conditions. Like BACROS, SUCROS is universal in nature because the physical and physiological processes described are applicable to a wide range of environmental conditions. SUCROS has been applied to various crops, e.g. wheat, potato and soybean (van Keulen et al., 1982b) by altering the crop parameters. An updated version, SUCROS87. was published in 1989, with crop parameters for spring wheat, winter wheat, maize, potato and sugar beet (Spitters et al., 1989). In 1992, the latest versions of SUCROS for spring wheat were presented: SUCROSl for potential production, and SUCROS2 for water-limited production (van Laar et al., 1992; Goudriaan & van Laar, 1994). In the latter model. SUCROSl is linked to the soil water balance module SAHEL. Subsequently, SUCROS became the lead model for further simplification and development of specific purpose-oriented models, e.g. INTERCOM (INTERplant COMpetition) for the interaction between field crops and weeds (Kropff & van Laar, 1993) and SBjWWFLEVO (Sugar Beet/Winter Wheat in FLEVOland) which used remotely sensed inputs for growth monitoring (Bouman, 1992; Fig. 3).

WOFOST

One of the first application-oriented models to be derived from SUCROS was WOFOST (World Food STudies). This model was developed by the Centre for World Food Studies, an interdisciplinary group of scientists from the Department of Development Economics of the Free University of Amsterdam in cooperation with TPE-WAU and CABO-DLO in Wageningen. The aim of the Centre was to explore the possibilities of increasing the agricultural productivity of developing countries (van Keulen & Wolf, 1986; van Diepen et al., 1988). In the development of the successive versions of WOFOST, the emphasis was on

182

B. A. M. Bouman, H. van Keulen, H. H. van 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, the process descriptions are universal and the model is tailored to various crops by altering the crop parameters (van Heemst, 1988). Because of its application-orientation, a number of user-friendly features were introduced in WOFOST. For example, there was a crude geographical information system facility for presenting the output of the simulations in the form of maps, and a menu interface allowed the crop type and production situation and the crop, soil and weather input data files to be selected easily. MACROS and ORYZA The MACROS modules (Modules of an Annual CROp Simulator; Penning de Vries et al., 1989) for crops in the semi-humid tropics were developed as part of the SARP project (Simulation and Systems Analysis for Rice Production). One of the aims of this project was to transfer the technology of simulation and systems analysis to multi-disciplinary teams of scientists in Southeast Asia (ten Berge, 1993). MACROS aided these objectives in two ways: first, as an instructional and training vehicle for the transfer of agrotechnology and systems analysis; and second, as a tool for the development and application of models in the ‘cropping systems’, ‘potential production’, ‘water, nutrients and roots’, and ‘insect pests, diseases and weeds’ research themes. MACROS consists of a series of basic modules for potential and water-limited crop growth and for the water balance of soils in both freely draining (SAHEL) and water-logged conditions (SAWAH; ten Berge et al., 1991). Like SUCROS and WOFOST, the model is generic and parameters are given for a large number of crops. Compared with SUCROS, however, MACROS has retained more of the character of a comprehensive model. An important feature of MACROS in its role as a training tool, was its transparent, modular structure that allowed scientists to choose and combine appropriate crop growth and water balance modules for addressing their specific production situations and research questions. Case-studies based on the MACROS modules were presented at a number of international workshops (Penning de Vries et al., 1991) at the end of the second phase of SARP (1987-1991). In the third and last phase of the project (1992-1995), the application of the models that had been developed was emphasized by concentrating research efforts into six application programmes. These were: (i) agro-ecological zonation and characterization; (ii) optimization of crop rotations and water use; (iii) application of models in plant breeding programmes; (iv) evaluation of the impact of climate change on rice production; (v) optimization of nitrogen management; and (vi) optimization

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183

of pest management. A series of rice models based on the MACROS modules and on SUCROS was developed to serve these specific applications under the generic name ORYZA (e.g. Kropff et al., 1994). N-limited production situation: PAPRAN Nitrogen dynamics in soils and crops were studied under semi-arid conditions in on-going projects in Israel and the Sahel. However, progress was slow because the biological and soil chemical processes involved are complex and difficult to quantify. The first modelling work, which was based on a relatively simple set of supply and demand functions, resulted in the description of N uptake and redistribution in plant tissue (Seligman et al., 1975). Combining these descriptions with those of ARID CROP led to the development of the preliminary model PAPRAN (Production of Arid Pastures limited by RAinfall and Nitrogen) for annual pasture production in semi-arid conditions in which growth is limited by rainfall and nitrogen (N) (Seligman & van Keulen, 198 1; van Keulen, 1982). PAPRAN is basically a soil&water balance model where plant growth is closely related both to the amount of water transpired by the canopy and to its N status, and where N transformations in the soil are represented by immobilization and mineralization processes (van Keulen, 1982). Continuation of this line of work resulted in the development of a comprehensive model for spring wheat at this production level (van Keulen & Seligman, 1987). Recent developments (1990-1995):

operationalization

The summary models initiated in the 1980s are increasingly being used operationally as a result of the demand by policy makers and land managers for data that can only be produced by models. Typical applications include agro-ecological zonation, regional yield forecasting and scenario studies for exploring the effect of environmental or socioeconomic changes on agriculture. Moreover, the increasing pressure from agricultural funding agencies to ‘prove’ the operational applicability of modelling has led to research being driven by the development of new technology. This change of emphasis has introduced new requirements for models and has highlighted the importance of software quality, an issue that had been recognized in the early years of modelling, e.g. by Arnold & de Wit (1976). Operational applications: WOFOST, LINTUL WOFOST Two major successful applications of WOFOST in the 1990s were in the policy study ‘Ground for Choices’ (Netherlands Scientific Council for Government Policy, 1992; Rabbinge & van Latestijn, 1992) and in the Monitoring Agriculture with Remote Sensing project (MARS)

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

of the Joint Research Centre (JRC) of the Commission of the European Communities (Meyer-Roux & Vossen, 1994). In the first study, the model was used to explore regional yield potentials in the EU under different management intensities. The results were used to generate technical coefficients for different crop rotations (de Koning et al., 1995). These coefficients were then used in the linear programming model GOAL (General Optimal Allocation of Land use; Scheele, 1992) to optimize land use and production systems under four contrasting economic scenarios. The outcomes of these simulations differed widely in terms of the land required, the costs of production, the employment situation and the use of fertilisers and pesticides. In the MARS project, WOFOST was integrated with a geographical information system (GIS) to produce the crop growth monitoring system (CGMS) for operational yield forecasting for the EU (van Diepen, 1991; Vossen, 1995). For this purpose, WOFOST was upgraded to version 6.0 with new routines developed in SUCROS (see below) and new functionalities developed especially for the MARS project (Supit et al., 1994). For crop yield forecasting, research is continuing by exploring possibilities of integrating crop growth models with remote sensing data to improve the forecasting accuracies (e.g. Bouman, 1995). LZNTUL For many studies at scales ranging from the regional to the global, existing summary models needed further simplification because the availability and quality of model input data were often found to be more constraining than knowledge of the basic processes incorporated in them. Spitters (1990) argued that SUCROS could be further simplified by incorporating only those processes that affect the major determinants of growth, and laid the foundations for a modelling approach that would later be baptised LINTUL (Light INTerception and UtiLization; Spitters & Schapendonk, 1990; Kooman, 1995). LINTUL was the first deviation from the photosynthesis-based models of the De Wit school. In the LINTUL models, total dry matter production is calculated using the Monteith approach (Monteith, 1969, 1990) in which crop growth rate is calculated as the product of interception of radiation by the canopy and a light-use efficiency (LUE), which should more correctly be called a dry matter: radiation quotient (Russell et al., 1989). The LUE can often be considered constant over the growing season and a property of the crop of interest. For regional studies, LINTUL-type models have the advantage that data input requirements are drastically reduced and model parameterization is facilitated. The LINTUL approach was used, at the request of the International Potato Center (CIP), for the agro-ecological characterization of global potato production to help target research at production problems in those regions where potato cultivation is most

185

Crop growth simulation models

promising (van Keulen & Stol, 1995). Recently, Penning de Vries et al. (1995) used the LINTUL approach in a world food study in which potential and water-limited food production was estimated for the year 2040 for 15 major regions of the world. Model quality: SUCROS SUCROS has been used to test ways of improving model and software quality. A set of rules and utilities for programming the typical crop growth models of the ‘School of de Wit’ was developed in FORTRAN and called the FORTRAN Simulation Environment (FSE; van Kraalingen, 1993). FSE supports the state-variable approach by organizing process equations into tasks for initialization, updating of state variables, calculation of rate variables and calculation of end-of-season characteristics such as harvest index. These tasks are broadly equivalent to the INITIAL, DYNAMIC and TERMINAL sections of the early simulation languages (Brennan c,t al., 1970) with the added clarity of distinguishing between the calculation of rate and state variables in what would be the DYNAMIC section. Moreover, the model equations in FSE are separated as much as possible from the supporting code that takes care of such tasks as data reading, data checking and output writing. A standard format has been introduced to take care of input and output data, and modellers using FSE are encouraged by its structure to program in an orderly and modular fashion. In SUCROS87 (Spitters et al., 1989), process descriptions had been organized into subroutines. Further development has continued in the 1990s and has resulted in a series of interchangeable, process description routines, e.g. for light interception, photosynthesis and transpiration, which are universally applicable 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 the appropriate routines to link to their main model. Finally, standardized procedures are being developed to test the quality of the software itself, e.g. to check for programming errors and confirm the reproducibility of results, and to compare the model outputs with experimental data.

THE

CROP

MODEL RECORD: USEFULNESS APPLICABILITY

AND

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

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using mathematical equations. This is still the main aim of developing crop growth models. Simulation models are powerful tools for testing our understanding of crop performance by comparing simulation results and experimental observations, thus making explicit gaps in our knowledge. Experiments can then be designed to fill these gaps. This function of crop growth modelling is difficult to evaluate because it is almost impossible to predict what would have been our knowledge of crop performance had the model paradigm not been so pervasive in research (Seligman, 1990). Critics such as Passioura (1973) and Monteith (1981) have suggested that complex models cannot show anything that could not be deduced by the use of straightforward common sense. Crop modellers, however, insist that no other technique is as powerful for synthesizing knowledge on subprocesses and increasing our understanding of whole crop behaviour (de Wit et al., 1978; Loomis et al., 1979; Penning de Vries et al., 1989). Welltested crop growth models can be used to explore, in a quantitative way, the relative importance of crop characteristics, such as physiological and morphological traits, and environmental characteristics, in a manner that would not be possible in field experimentation. Sometimes, the simulations produce counterintuitive results. An example of this is found in the effects of stomata1 behaviour on crop growth and development when the environment changes (de Wit et al., 1978). These interesting cases stimulate further thinking and experimentation and are good evidence for refuting the criticisms of this type of modelling. Operational applications

Yield prediction Validated models can be used in application-oriented research by scientists and in operational applications where their users are managers or other non-scientists. Most of the application-oriented research using the crop growth models of the ‘School of de Wit’ has been related to the problem of yield prediction (Seligman, 1990) including world food production studies (Buringh et al., 1979; Penning de Vries et al., 1995), agro-ecological zonation (Aggarwal, 1993; van Keulen & Stol, 1995) and explorations of the effects of climate change on crop production (Wolf, 1993; Matthews et al., 1995). Operational applications include the use of the crop growth monitoring system (CGMS) by the Joint Research Centre for producing monthly yield predictions for the regions of the EU (Vossen, 199.5), and the use of WOFOST by Dutch consultancy agencies in land use planning projects (personal communication). Two reasons may be postulated for the relative success of crop models in yield prediction. First, models are the only means of systematically exploring the production potential of

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agricultural crops in historical or predicted future weather conditions. Second, there is generally no way of testing the validity of predictions at regional or global scales. An interesting exception to this second issue was the recent comparison in the MARS project of yields predicted by CGMS and by conventional regression techniques with long-term yield statistics (de Koning et al., 1993). The results indicated that, for most crops, the accuracy of yield predictions made using only time-series regression models could not yet be improved by adding the output of the WOFOST model at the NUTS-O (country) or NUTS1 (primary administrative region) level. However, because the accuracy of the official yield statistics is unknown, it was impossible to separate the effects of unrealistic simulations from errors in the statistics. The actual user of CGMS, i.e. the JRC, however, found many additional benefits of the modelling approach that were not included in the scientific evaluation. The WOFOST predictions were thus timely(!), objective, quantitative, and consistent over large areas (Heath, 1991; Vossen (JRC), personal communication). Additionally, the model simulations provided additional means of comparison with other sources of information, such as field-sampling, remote sensing and expert knowledge, which are all used to derive the final yield estimates for the EU. Similar views on the benefits of integrated techniques (including crop growth models) have been reported by Horie et al. (1992) for rice yield forecasting in Japan, and Gommes (199 1) for early warning systems in Africa, Asia and Latin America. Plant breeding Crop growth models have been used in plant breeding to simulate the effects of changes in the morphological and physiological characteristics of crops and thus to aid in the identification of ideotypes (Donald, 1968) for different environments (Dingkuhn et al., 1993; Hunt, 1993; Kropff er al., 1995). Hunt (1993) and Palanisamy et al. (1993) suggested that crop growth models that have been parameterized for new cultivars in field experiments can be used to simulate the long-term yield stability of these cultivars at a location under the expected range of climatic conditions. This technique holds out the promise of reducing the cost of breeding programmes by limiting the number and years of expensive, multi-location trials that are currently required to ensure statistical reliability. A recent review of literature on the use of modelling in potato breeding led Ellis&he & Hoogendoorn (1995) to the conclusion that ‘simulation modelling can contribute to the efficiency of potato breeding programmes, because modelling analyses complex characteristics, indicates the most promising components for selection, can forecast plant growth under various conditions, including biotic and abiotic stress, and helps, therefore.

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to formulate breeding strategies’. Breeding objectives can be fine-tuned for particular regions or environments on the basis of model evaluations for these circumstances (e.g. Kooman, 1995). The involvement of plant breeders in recent publications on modelling and breeding suggests a change in attitude since Seligman’s remark in 1990 that simulation results using crop growth models have rarely inspired breeders to adapt their breeding programmes (Seligman, 1990). Crop management Crop growth simulation models have been used in numerous studies to help farmers in day-to-day, i.e. tactical, decision making. They have been used to investigate the effects of management options such as sowing time, plant population density, irrigation timing and frequency and fertiliser applications in different environmental conditions on long-term mean yield 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 field experimentation to test the outcomes predicted in the simulations (ten Berge et al., 1994). However, the operational application of crop growth models to support tactical decision making has generally not yet been successful (Seligman, 1990) with the notable exception of the areas of irrigation scheduling and water management (van Keulen & Penning de Vries, 1993) and pest and disease management (e.g. the Epidemics Prediction and Prevention System (EPIPRE); Rabbinge & Rijsdijk, 1983). Treatment of the latter type of model is outside the scope of this paper and readers are referred to relevant publications elsewhere, (e.g. Rabbinge et al., 1990; Kropff & Lotz, 1992; Teng & Savary, 1992). Recently, researchers have started to apply the results of crop models to tactical decision making using knowledge based systems such as expert systems and decision support systems. These software systems have been promoted since the mid-1980s as a major breakthrough, opening new horizons in decision support (Schiefer & da Silva, 1995). However, despite continuing reports of successful developments and prototyping, Hilhorst & Manders (1995) found that the overall acceptance of the technology in agriculture in The Netherlands is still limited. They suggested that a main reason for this lack of acceptance was the knowledge-intensive nature of such systems. Such systems are of strong interest to research organizations and this has resulted in: (i) an undue emphasis on problems of a scientific rather than a practical interest; (ii) poor functionality for non-specialist users; and (iii) an evolutionary development path which does not accord with modern software engineering standards. However, these deficiencies, which might equally well apply to the failure of crop growth models in

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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 the variability in weather conditions, success of decision support systems depends largely on their ability to predict future weather. Even today, weather predictions are at best reasonably accurate for only a few days ahead and decision support systems have to rely on probability analyses using long-term historical or generated weather data (van Keulen & Penning de Vries, 1993). Second, the lack of accurate input data for soil and 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 whereas they were developed for rather strictly defined hypothetical production situations (potential production, water-limited production, etc.) in uniform fields. In farmers’ fields, several limiting and yield-reducing fattors may occur simultaneously, so that the conditions fall outside the boundary conditions or domain of validity of the models. This raises the modeller’s dilemma that for ease of application in a particular practical situation, models should be as simple as possible and require only a small number of input data, but that on the other hand, they should be complex and flexible enough to be able to represent the complex effects of the wide range of potentially interacting yield-limiting and yield-reducing factors that might be important for the crop of interest. For situations of potential production, the summary models developed from the comprehensive crop growth models of the ‘School of de Wit’ satisfactorily predict crop behaviour. However, although the processes of photosynthesis and growth respiration are satisfactorily modelled mechanistically, aspects of maintenance respiration and morphogenesis (e.g. organ growth, assimilate partitioning and leaf area development) are still not well understood and little progress has been made since the release of BACROS and ARID CROP. In water-limited conditions, the main effect of water shortage on the reduction in photosynthesis is well understood and has been satisfactorily incorporated in summary models. However, recent experiences in modelling rice growth in the SARP project have indicated the need for further study and the inclusion of crop-specific adaptation mechanisms (Wopereis, 1993). Major gaps still exist in our knowledge of the effects of nutrient-limitation and it is not yet possible to use mechanistic models directly for farm level applications (van Keulen & Stol, 199 1; van Keulen & Penning de Vries, 1993). Therefore the operational use of deterministic models that can handle the even more complex situations that typify actual farming conditions 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 operationalization of crop growth models is by using other sources of information, such as field observations, measurements or remote sensing data, for periodic adjustment of the state variables. There have been, for example, successes in the 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 also illustrate the importance of limited but clear-cut objectives by focusing on specific problems in tactical decision making.

POSTSCRIPT This paper deals with a historical overview of crop growth simulation models from the ‘School of de Wit’. It was, however, not our intention to suggest that these models are better than other models that have been developed elsewhere (see Introduction). The authors thank R. S. Loomis for his helpful comments and suggestions on earlier versions of the manuscript. G. Russell is thanked for polishing the paper’s English and suggesting some improvements ‘by the way’.

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