Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.)

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Abstract

Dynamic simulation models are frequently used for assessing agronomic and environmental effects of different management practices, under various pedo-climatic conditions. CropSyst is a suitable cropping systems simulation model for such applications. However, available CropSyst crop parameters for winter wheat, one of the most important cereals in the world, are limited. In this work we show that it is possible to parameterize separate sub-model components by using existing experimental data and literature.

The experiments, carried out in northern Italy between 1986 and 2001, quantified the dynamics of aboveground biomass (AGB), plant nitrogen (N) concentration (PNC) and N uptake (UPTK) by means of periodical measurements.

The relative root mean square error (calculated by dividing the root mean square error by the average of observations) obtained after model calibration and validation on an independent data set was, respectively, in the range 9–30% and 17–32% for AGB, 10% and 6–40% for PNC, 8–28% and 9–24% for UPTK. AGB was frequently underestimated. Despite the limited accuracy of simulations, we argue that calibrated crop parameters are adequate for scenario analysis as most differences between years and fertilization levels were reproduced by the model and final AGB and cumulative UPTK were also correctly simulated.

Introduction

Simulation models are nowadays widely applied in agriculture to make predictions about the agronomical, environmental and economic consequences of the complex interactions between crop management, soil and atmosphere (e.g. Acutis et al., 2000, Bechini et al., 2003, Donatelli et al., 2002, Gömann et al., 2005, Johnsson et al., 2002, Lewis et al., 2003, Rossing et al., 1997, Sousa and Pereira, 1999, Ten Berge et al., 2000, Wise and Cacho, 2005, Wolf et al., 2003).

Because models allow the simulation of stochastic scenarios (Badini et al., 1997, Acutis et al., 2000, Peralta and Stöckle, 2001, Bechini et al., 2003), they can be useful in the estimation of probabilities associated with the occurrence of events. Processes requiring scenario simulations in many intensive agricultural areas include the environmental impact of rural development plans, of manure and fertilizer management, and of agrotechniques involving the use of agrochemicals. CropSyst (Stöckle et al., 2003) is a cropping system simulation model which is distributed free of charge and provides: a platform for simulating crop rotations, an automatic management events scheduler, the possibility to run multiple simulations in connection with a Geographical Information System. All of the aforementioned characteristics make CropSyst particularly suitable for scenario simulations (Peralta and Stöckle, 2001, Bechini et al., 2003); other models simulate crop growth processes with more detail but have lower or no flexibility in specifying routine management techniques (e.g. SUCROS, Goudriaan and van Laar, 1994).

Realistic and accurate sets of crop parameters needed to correctly apply the models should be obtained from crop-specific field conditions. Such model parameters have been already published for several cultivated species in Italy (Donatelli et al., 1997, Giardini et al., 1998, Bellocchi et al., 2002, Bocchi et al., 2003, Confalonieri and Bechini, 2004, Confalonieri and Bocchi, in press).

Wheat (Triticum aestivum L.) is one of the most important cereals both in Italy and globally (FAOSTAT data, 2004). Despite the fact that wheat crop simulation models are now widely applied in monitoring and planning agricultural resources, CropSyst parameters for wheat are limited in the sense that they refer to older model versions (Giardini et al., 1998), to specific pedo-climatic environments (Stöckle et al., 1994, Pannkuk et al., 1998), and sometimes lack the complete list of crop parameters.

Considering the difficulty of performing serious, long-term experimental studies to set up and update species- and cultivar-specific sets of crop parameters, possible approaches for the estimation of crop model parameters include: (i) parameterize the models at species levels or, in the best cases, for sub-species groups (e.g. maturity classes for cereals such as rice, wheat and corn or plant morpho-physiological types for rice); (ii) recover, assess and use data collected for other purposes to derive the maximal amount of information for crop parameterization. Within this second approach, we may also consider specific sub-model components to determine the values of the main crop parameters.

The present study was performed: (i) to set up crop parameters required by CropSyst for winter wheat simulation; (ii) to assess the plausibility of reaching this objective through the integration of information recovered from relatively limited existing experimental data sets and from available literature; (iii) to highlight the potentials and the limitations of CropSyst as a tool for scenario analyses with winter wheat.

Section snippets

Experimental data

Experimental data were collected in 4 experiments (Table 1, Table 2) carried out between 1986 and 2002 in 2 locations in northern Italy. This area is characterized by a moderate continental climate, with a mean annual temperature of about 13 °C; the absolute minimum temperature occurs between January and February and the absolute maximum between July and August. Total precipitation (about 800 mm year−1) is relatively well distributed and the average wind speed is about 1.5 m s−1.

For all the

Calibration of crop model parameters

Calibrated crop model parameters are shown in Table 3.

Conclusions

The cropping systems simulation model CropSyst could be satisfactorily parameterized for winter wheat by using existing experimental data collected for purposes other than modelling and the wide range of available literature. The set of crop parameters obtained allowed reasonable estimates of aboveground biomass, plant nitrogen concentration and plant nitrogen uptake at different times during spring crop growth, for various locations/years/treatments in northern Italy. The simulated values were

References (54)

  • H. Johnsson et al.

    SOILNDB: a decision support tool for assessing nitrogen leaching losses from arable land

    Environmental Modelling and Software

    (2002)
  • E. Justes et al.

    Determination of a critical nitrogen dilution curve for winter wheat crops

    Annals of Botany

    (1994)
  • J.R. Kiniry et al.

    Radiation-use efficiency in biomass accumulation prior to grain-filling for five grain-crop species

    Field Crops Research

    (1989)
  • D.R. Lewis et al.

    Simulating field-scale nitrogen management scenarios involving fertiliser and slurry applications

    Agricultural Systems

    (2003)
  • K.M. Loague et al.

    Statistical and graphical methods for evaluating solute transport models: overview and application

    Journal of Contaminant Hydrology

    (1991)
  • J.E. Olesen et al.

    Crop nitrogen demand and canopy area expansion in winter wheat during vegetative growth

    European Journal of Agronomy

    (2002)
  • C.D. Pannkuk et al.

    Evaluating CropSyst simulations of wheat management in a wheat-fallow region of the US Pacific Northwest

    Agricultural Systems

    (1998)
  • J.R. Porter et al.

    Temperatures and the growth and development of wheat: a review

    European Journal of Agronomy

    (1999)
  • W.A.H. Rossing et al.

    Model-based explorations to support development of sustainable farming systems: case studies from France and the Netherlands

    European Journal of Agronomy

    (1997)
  • T.R. Sinclair et al.

    A model to assess nitrogen limitations on the growth and yield of spring wheat

    Field Crops Research

    (1992)
  • T.R. Sinclair et al.

    Radiation use efficiency

    Advances in Agronomy

    (1999)
  • V. Sousa et al.

    Regional analysis of irrigation water requirements using kriging. Application to potato crop (Solanum tuberosum L.) at Trás-os-Montes

    Agricultural Water Management

    (1999)
  • C.O. Stöckle et al.

    Modeling crop nitrogen requirements: a critical analysis

    European Journal of Agronomy

    (1997)
  • C.O. Stöckle et al.

    CropSyst, a cropping systems simulation model

    European Journal of Agronomy

    (2003)
  • C.O. Stöckle et al.

    CropSyst, a cropping systems simulation model: water/nitrogen budgets and crop yield

    Agricultural Systems

    (1994)
  • H.F.M. Ten Berge et al.

    Farming options for The Netherlands explored by multi-objective modelling

    European Journal of Agronomy

    (2000)
  • R. Wise et al.

    Tree–crop interactions and their environmental and economic implications in the presence of carbon-sequestration payments

    Environmental Modelling and Software

    (2005)
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