Characterising the most critical climatic parameters that impact the quality of spring-wheat (Triticum aestivum L.) on the Canadian Prairies using partial least squares (PLS) analysis

The Canadian Prairies experience wide-ranging growing-season climatic conditions, which impact spring-wheat quality. This study characterised agroclimatic parameters that impact spring-wheat quality using partial least squares (PLS) regression. Agrometeorological data collected from several spring-wheat trials across the Prairies were utilised. Fifty-nine agroclimatic parameters were derived and used as predictor variables. Wheat quality characteristics i.e., Grain Protein Content (GPC), Farinograph Absorption (FarAB), Dough Development Time (DDT) and Loaf Volume (LVol) were response variables. Quality characteristics for variety AC-Barrie were used to build the PLS models, which were then used to simulate quality characteristics for variety Superb. Results showed that three separate 3-variable PLS models explained 83%, 80% and 69% of variability in GPC, DDT and LVol, respectively; while a 4-variable model explained 82% of variability in FarAB. Simulated and observed values for Superb were not different (p > 0.05) for all quality characteristics except FarAB. Modelled and observed values correlated well with R2 values ranging from 0.69 to 0.96, indicating that the models explained 69-96% of the variability in the various quality characteristics. Mean bias error for GPC was zero indicating perfect model simulation, but negative for other quality characteristics suggesting underestimation. Generally, water-demand and water-use agroclimatic parameters had strongest relationship with all wheat quality characteristics.