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Calibration and performance evaluation of soybean and spring wheat cultivars using the STICS crop model in Eastern Canada

Jégo, G., Pattey, E., Bourgeois, G., Morrison, M.J., Drury, C.F., Tremblay, N., Tremblay, G. (2010). Calibration and performance evaluation of soybean and spring wheat cultivars using the STICS crop model in Eastern Canada, 117(2-3), 183-196. http://dx.doi.org/10.1016/j.fcr.2010.03.008

Abstract

Crop modelling at a regional scale is often limited by the availability of input data. Soil databases can be an efficient way to provide spatial information regarding soil properties, and remote sensing has proved to be a valuable source of information of management practices through assimilation of variable, like leaf area index (LAI), in crop model. However, the large numbers of cultivars from seed companies used at the regional scale remain an important concern for regional crop modelling. The aim of this study is to test whether we can limit calibration to only one cultivar per crop for modelling LAI, biomass and yield dynamics in the Mixedwood Plains ecozone, which extends over 3° of latitude and 10° of longitude in Eastern Canada. The ability to use the new regionally adapted soybean cultivar (CanSoyEst) and spring wheat cultivar (CanBleEst) to predict LAI, biomass, and yield in the studied area was evaluated using data from several sites distributed between Southwestern Quebec and Southern Ontario. The model used is STICS, a generic open code crop model, which offers easy access to cultivar parameters and the ability to assimilate LAI from remote sensing to derive input data for regional scale studies. The CanBleEst cultivar provided good estimates of biomass and yield, with a root mean square error (RMSE) ranging between 10% and 20%. More scatter was observed for LAI estimates with a RMSE ranging from 21% to 25% depending on the sites. For the CanSoyEst cultivar, the predicted biomass had a RMSE ranging from 23% to 27% and the RMSE was about 26% for yield predictions whereas the predicted LAI showed more scatter (28%≤RMSE≤38%). A higher exposure to water stress and leaf shedding during senescence may explain why soybean output variables were predicted with more scatter. This work demonstrates that one cultivar per crop (i.e., soybean and spring wheat) provides enough accuracy to predict LAI, biomass and yield over the entire region. It provides a new soybean cultivar parameterization adapted to a short growing season and the first calibration and evaluation of a spring wheat cultivar in the STICS crop model. These contributions open new opportunities for using STICS in Northern climates characterized by short growing seasons. It also offers a foundation for crop modelling and prediction at regional scale in Eastern Canada. © 2010.

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