Prediction of seeding date in southern Alberta

Predicting seeding date is an important component of modeling the impact of climate change on agricultural production. The objective of this study was to develop and evaluate models for predicting seeding dates based on weather variables. Data from 60 yr of a long-term experiment conducted on a silty clay loam (Dark Brown Chernozem) at Lethbridge, Alberta, were used for modeling seeding dates. Four approaches were used: an empirical model, a stepwise regression analysis, an iterative regression analysis, and a neural network (NN). An accuracy analysis was utilized to compare the results produced by the four methods. The best method was the NN model. It required 17 inputs, derived from date, air temperature and precipitation. The empirical model — which required that maximum temperature be at least 13 °C, that precipitation be less than 1 mm, and that the surface 15 cm of soil moisture be no greater than 90% of field capacity for four consecutive days — was the next best model. Stepwise regression identified air temperature and precipitation as the two most important weather variables. The predictive capability of this method was not satisfactory. Iterative regression analysis, also using maximum air temperature and precipitation, did not produce a satisfactory model. The stepwise and iterative regression methods predicted average seeding dates accurately but did not account for the variability in seeding dates.