Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.)
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)
- et al.
Grain yield in wheat: effects of radiation during spike growth period
Field Crops Research
(1997) - et al.
Stochastic use of LEACHN model to forecast nitrate leaching in different maize cropping systems
European Journal of Agronomy
(2000) - et al.
A model of water limitation on spring wheat growth and yield
Field Crops Research
(1991) - et al.
A straw mulch system to allow continuous wheat production in an arid climate
Field Crops Research
(1996) - et al.
Application of crop simulation modeling and GIS to agroclimatic assessment in Burkina Faso
Agriculture, Ecosystems and Environment
(1997) - et al.
Spatial interpolation of soil physical properties for irrigation planning. A simulation study in Northern Italy
European Journal of Agronomy
(2003) Impact of canopy nitrogen profile in wheat on growth
Field Crops Research
(1999)- et al.
A preliminary evaluation of the simulation model CropSyst for alfalfa
European Journal of Agronomy
(2004) - et al.
Evaluation of CropSyst for cropping systems at two locations of northern and southern Italy
European Journal of Agronomy
(1997) - et al.
Model based impact analysis of policy options aiming at reducing diffuse pollution by agriculture – a case study for the river Ems and a sub-catchment of the Rhine
Environmental Modelling and Software
(2005)
SOILNDB: a decision support tool for assessing nitrogen leaching losses from arable land
Environmental Modelling and Software
Determination of a critical nitrogen dilution curve for winter wheat crops
Annals of Botany
Radiation-use efficiency in biomass accumulation prior to grain-filling for five grain-crop species
Field Crops Research
Simulating field-scale nitrogen management scenarios involving fertiliser and slurry applications
Agricultural Systems
Statistical and graphical methods for evaluating solute transport models: overview and application
Journal of Contaminant Hydrology
Crop nitrogen demand and canopy area expansion in winter wheat during vegetative growth
European Journal of Agronomy
Evaluating CropSyst simulations of wheat management in a wheat-fallow region of the US Pacific Northwest
Agricultural Systems
Temperatures and the growth and development of wheat: a review
European Journal of Agronomy
Model-based explorations to support development of sustainable farming systems: case studies from France and the Netherlands
European Journal of Agronomy
A model to assess nitrogen limitations on the growth and yield of spring wheat
Field Crops Research
Radiation use efficiency
Advances in Agronomy
Regional analysis of irrigation water requirements using kriging. Application to potato crop (Solanum tuberosum L.) at Trás-os-Montes
Agricultural Water Management
Modeling crop nitrogen requirements: a critical analysis
European Journal of Agronomy
CropSyst, a cropping systems simulation model
European Journal of Agronomy
CropSyst, a cropping systems simulation model: water/nitrogen budgets and crop yield
Agricultural Systems
Farming options for The Netherlands explored by multi-objective modelling
European Journal of Agronomy
Tree–crop interactions and their environmental and economic implications in the presence of carbon-sequestration payments
Environmental Modelling and Software
Cited by (63)
A parsimonious Bayesian crop growth model for water-limited winter wheat
2024, Computers and Electronics in AgricultureA hierarchical Bayesian approach to dynamic ordinary differential equations modeling for repeated measures data on wheat growth
2022, Field Crops ResearchCitation Excerpt :The medians for thermal time to end of leaf expansion (TTL) and thermal time to maturity (TTM) were estimated to be 1378 and 2162 with HDI of [1320, 1429] and [2044, 2331] respectively, which are higher than the reported values in the literature. Specifically, estimates for TTL and TTM were reported to be 550 and 1352, respectively, by (Bechini et al., 2006) whereby a fixed value of −1 ∘C was used for base temperature. Meanwhile, (McMaster et al., 2019) reported estimates of 945 and 1970 for TTL and TTM, respectively, using a base temperature of 0 ∘C. For the base temperature (Tbase), the posterior median was estimated to be −1.16 ∘C with an HDI of [−4.5, 2] by our model.
A remote sensing-based scheme to improve regional crop model calibration at sub-model component level
2020, Agricultural SystemsCalibration and evaluation of the STICS soil-crop model for faba bean to explain variability in yield and N <inf>2</inf> fixation
2019, European Journal of AgronomyBiotope conservation in a Mediterranean agricultural land by incorporating crop modelling
2019, Ecological ModellingCitation Excerpt :They can be used at any scale, from global such as, the impact of climate change, to local studies such as, land quality and crop change. In general, they are designed to model the complex interactions between crop management, soil, and the atmosphere, and to estimate agricultural yields, taking into account the environmental impacts of crop cultivation, such as leaching, erosion (Adekalu and Fapohunda, 2006; Bechini et al., 2006; Gary et al., 1998; Johnsson et al., 2002). Wetlands take place between terrestrial and aquatic ecosystems with unique hydrological and biological characteristics.