See more from this Session: Agroclimatology & Agronomic Modeling: I/Div. A03 Business Meeting
Previous research has identified growing season weather as a factor impacting canola quality. The objective of this study was to create a predictive model for canola quality requiring only meteorological data, seed variety and seeding date as inputs. A selection of Canada Number 1, low erucic acid and low glucosinolate Brassica napus samples from the 2008 Canadian Grain Commission (CGC) harvest survey were analyzed for quality. All samples were from one of these stop-ten varieties grown in western Canada and included, 1841, 5020, 5030, 34-65, 71-45RR and SP Banner. The quality parameters investigated were oil content, oil-free protein, chlorophyll, glucosinolates content, and the fatty acid profile. The quality data were aligned with weather data from the Environment Canada or Canadian Wheat Board weather station in closest proximity to each sample site. Daily high low, and average temperatures, cumulative daily precipitation, and calculated values for evapotranspiration rates, heat stress and crop water deficit were determined for each sample over the span of various physiological development stages, using the P-day index as a method of calculating canola development. Correlations between the weather parameters and canola quality factors were determined and used to develop a canola quality predictive model, which was validated and adjusted with harvest survey canola samples collected in 2009.
See more from this Session: Agroclimatology & Agronomic Modeling: I/Div. A03 Business Meeting