Agronomy Journal • Volume 108, Issue 2 • 2016 1 C anola was developed in Canada in the 1970s as an edible cultivar of rapeseed (Brassica napus L.) with low glucosinolates and low erucic acid. Currently, canola is grown on eight million ha of agricultural land in Canada, which is 22% of the global canola area. More than half of the harvested canola seeds in Canada are exported, accounting for 46% of the international canola market (FAOSTAT, 2015). Te seed yield of canola has doubled since the 1970s (FAOSTAT, 2015), and, with increasing demands for renewable energy and dietary consumption, production is expected to increase. Environmental factors such as temperature, atmospheric CO 2 concentration, and precipitation have a signifcant efect on crop development and growth (Howden et al., 2007). Facing climate change associated with enhanced greenhouse efects (IPCC, 2007), Canadian climatic conditions are projected to be warmer, with longer growing seasons and increased annual heat units (Qian et al., 2013). However, these changes may also be accompanied by extreme climatic conditions (Qian et al., 2010). Tese projected climate changes may force canola producers to change cultivars and other management practices to mitigate the potential negative impact on yields and to beneft from the extended growing season. Evaluating canola’s response to climate change and management practices through growth experiments in climate chambers can be extremely expensive if not infeasible. On the other hand, crop growth models simulate crop development and soil processes by integrating environmental factors and crop management practices and are thus powerful tools for assessing crop responses to diferent climatic conditions and crop management practices. Compared with cereal crops, only a few crop models have been developed for canola. Canola plants have a special biophysi- cal feature in that their pods gradually take over the function of light absorption and photosynthesis during the late grow- ing season as leaves go into early senescence (Gammelvind et al., 1996). Terefore, the total area of leaves and pods function together afer fowering, and the plant area index (PAI) rather than leaf area index (LAI) might be more relevant for calculat- ing light interception in canola simulations. By mimicking Biometry, Modeling & Statistics Evaluation of the CSM-CROPGRO-Canola Model for Simulating Canola Growth and Yield at West Nipissing in Eastern Canada Qi Jing, Jiali Shang, Budong Qian,* Gerrit Hoogenboom, Ted Huffman, Jiangui Liu, Bao-Luo Ma, Xiaoyuan Geng, Xianfeng Jiao, John Kovacs, and Dan Walters Published in Agron. J. 108:1–10 (2016) doi:10.2134/agronj2015.0401 Received 21 Aug. 2015 Accepted 27 Nov. 2015 © Her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada. ABStrAct With increasing demands for renewable energy and dietary vegetable oils, the production of canola has become widespread in recent years. Modeling canola growth and yield is a helpful approach to predict canola responses to various environments, especially under climate change. However, few studies have been performed for predicting growth and yield of canola in Canada. In this study, we evaluated the CSM-CROPGRO- Canola model in Decision Support System for Agrotechnology Transfer v4.6 for simulating spring canola at West Nipissing in Eastern Canada. Te model was evaluated using plant and soil data collected from feld experiments over three growing seasons (2012–2014). Te model could predict the observed crop development and successfully mimic the characteristics of canola regarding light absorption and utilization using combinations of leaves and pods. Te accumulations of aboveground biomass were satisfactorily simulated in the life cycle under di ferent nitrogen (N) fertilizer application rates, with a normalized RMSE of 19%. Te seed yields were successfully predicted with di ferent N application rates except for an underestimation under zero N application. Te underestimation of yield under low N rates was possibly related to the defciency in the simulated N mineralization that could also be associated with inaccurate input soil data. A better simulation of seed yields under low N application was achieved when the soil organic matter module based on the CENTURY model was used in DSSAT v4.6. Te calibrated model simulated soil moisture and inorganic N contents satisfactorily, showing a good performance of the CSM-CROPGRO-Canola model for the study region. Q. Jing, J. Shang, B. Qian, T. Hufman, J. Liu, B. Ma, and X. Geng, Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada; G. Hoogenboom, AgWeatherNet, Washington State Univ., Prosser, Washington 99350-8694; and X. Jiao, J. Kovacs, and D. Walters, Dep. of Geography, Nipissing Univ., North Bay, Ontario P1B 8L7, Canada. *Corresponding author (budong.qian@canada.ca). Abbreviations: DSSAT, Decision Support System for Agrotechnology Transfer; LAI, leaf area index; LUE, light use efciency; nRMSE, normalized root mean squared error; PAI, plant area index; SOM, soil organic matter. Published January 29, 2016