Transplanting canola may still be an effective measure against the constraint of season length to achieving higher yields of both rice and canola. However, this potential cannot be achieved in the rice–canola double-cropping system due to later sowing time after rice harvest in mid–later October. APSIM model overview, evaluation and sensitivity analysis The APSIM model is a modular modeling framework (Keating et al., 2003 Holzworth et al., 2014), a. The yield potential at the study region is ~3 t/ha, on average. Following model calibration of the cultivar at varying sowing dates over two growing seasons (20), a long-term simulation was run using historical weather data (1981-2010) to. It stimulates plant growth on a daily basis, based on incoming solar radiation and depends on both water content and Nitrogen supply. The results revealed that canola yield declined linearly with late sowing time, mainly due to shortened vegetative growth stages, and varied significantly due to inter-annual climate variability. APSIM was a broadly used model of the agro-ecosystems. However, the model overestimated canola yield under late sowing dates. After calibration of the phenological parameters and maximum harvest index, the model was able to simulate the onset of phenological stages with different sowing dates, and to explain 75% of the variation in biomass and yield caused by late sowings. Experimental data In 19 experiments were conducted at different locations, with a range of cultivars and sowing dates (Table 1). The 1997 experiment was used for model calibration and the 1998 experiment for model testing. Then the N rate at which the economic return on N was maximized hereafter RTN (return to N approach)-APSIM was estimated by difference: simulated yield times corn price minus fertilizer rate times N cost between two levels of N. Walton, unpublished), were used to calibrate and test the APSIM-Canola model (version 1.60) (Table1). These experiments included different cultivars and sowing dates, and recorded major phenological stages, biomass, and grain yield. The calibrated APSIM model was ran for every 5 kg N ha-1 increments from 0 to 350 kg N ha-1 to simulate corn yields. The APSIM-Canola model was calibrated and tested using data from three field experimental sites in the Yangtze River Basin. Experimental data to analyse the response of canola growth to sowing date are limited to a few seasons however, combining these data with modelling provides an efficient means to study the impact of sowing date and historical climate variation. However, there have been no studies to quantify the impacts of sowing time and climate variability. Canola is a major oil crop in the Yangtze River Basin of China, and its yield and oil content vary significantly from year to year due to changes in sowing time and inter-annual climate variability.
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