. 2009. Thermal time models for estimating wheat phenological development and weather-based relationships to wheat quality. Can. J. Plant Sci. 89: 429Á439. Accurate prediction of crop phenology is a key requirement for crop development models. The prediction of spring wheat yield and quality from meteorological data can be improved by quantifying heat and moisture conditions during specified phenological phases; therefore, accurate prediction of phenological development is important for estimating weather impacts on wheat quality. The objective of this study was to test the accuracy of biometeorological time (BMT), growing degree days (GDD), and physiological days (Pdays) for prediction of wheat phenological stages and impacts of growing season weather during those stages on wheat bread-making quality. Observed crop phenological stages and detailed weather data across 17 site-years in western Canada for six hard spring wheat varieties were collected to assess BMT, GDD and Pdays. Biometeorological time was most consistent for predicting the length of the seeding to jointing and seeding to anthesis growth stages and second most consistent behind GDD for predicting seeding to soft dough and seeding to maturity. The ability of the BMT and GDD models to predict calendar days to anthesis and maturity were further tested using field data from 166 farms across western Canada. Both GDD and BMT models were effective for predicting time from seeding to anthesis (R 2 0 0.84 and 0.90, respectively) and seeding to maturity (R 2 0 0.62 and 0.66, respectively). BMT-and GDD-predicted wheat growth phases were used to calculate modeled crop water use by growth period for producer fields. Crop water use is significantly correlated to key bread-making quality parameters of flour protein, farinograph dough development time and farinograph stability. Biometeorological time predicted water use was more highly correlated to these quality parameters than GDD predictions. Accordingly, the BMT scale is recommended for estimation of wheat phenological development especially for modeling weather impacts on wheat end-use quality. For personal use only.

The sorption of 2,4-D and glyphosate herbicides in soil was quantified for 287 surface soils (0-15 cm) collected in a 10 x 10 m grid across a heavily eroded, undulating, calcareous prairie landscape. Other variables that were determined included soil carbonate content, soil pH, soil organic carbon content (SOC), soil texture, soil loss or gain by tillage and water erosion, and selected terrain attributes and landform segments. The 2,4-D sorption coefficient (Kd) was significantly associated with soil carbonate content (-0.66; P < 0.001), soil pH (-0.63; P < 0.001), and SOC (0.47; P < 0.001). Upper slopes were strongly eroded and thus had a significantly greater soil carbonate content and less SOC compared with lower slopes that were in soil accumulation zones. The 2,4-D Kd was almost twice as small in upper slopes than in lower slopes. The 2,4-D Kd was also significantly associated with nine terrain attributes, particularly with compounded topographic index (0.59; P < 0.001), gradient (-0.48; P < 0.001), mean curvature (-0.43; P < 0.001), and plan curvature (-0.42 P < 0.001). Regression equations were generated to estimate herbicide sorption in soils. The predicted power of these equations increased for 2,4-D when selected terrain attributes were combined with soil properties. In contrast, the variation of glyphosate sorption across the field was much less dependent on our measured soil properties and calculated terrain attributes. We conclude that the integration of terrain attributes or landform segments in pesticide fate modeling is more advantageous for herbicides such as 2,4-D, whose sorption to soil is weak and influenced by subtle changes in soil properties, than for herbicides such as glyphosate that are strongly bound to soil regardless of soil properties.

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Variations in the characteristics of soil organic matter (SOM) at the field-scale are largely unknown, particularly in relation to observed variations in herbicide sorption. For the herbicide 2,4-D [2,4-dichlorophenoxyacetic acid], we found that its organic carbon-normalized sorption coefficient, Koc, varied by four-fold, from 76 to 315 L kg(-1), in the Ap-horizon along a slope transect in an undulating agricultural field in Manitoba, Canada. In order to explain the relatively large in-field variation in 2,4-D Koc values, techniques ranging from conventional chemical fractionation methods to solid state Cross Polarization and Magic-Angle Spinning (13)C-Nuclear Magnetic Resonance applied on whole soils, were used to derive SOM chemical, physical and structural parameters for correlation analyses with the measured 2,4-D Koc values on whole soils. Out of the 15 parameters considered, the 2,4-D Koc was significantly positively correlated with 1) the carbon (C) content of sodium hydroxide-extracted humic acids (r = 0.83, P < 0.01), a chemical parameter indicative of free form C in soil; 2) the molar absorptivity of humic acids at wavelength 280 nm (r = 0.81, P < 0.01), a physical parameter indicative of greater SOM aromaticity; and 3) the relatively intensity of aryl C (r = 0.92, P < 0.01) and O-aryl C (r = 0.93, P < 0.01) in whole soil, both structural parameters indicative of aromatic C. Consequently, the results suggest that in-field variations in 2,4-D Koc values are induced by variations in SOM aromaticity. Koc values are among the most sensitive parameters in herbicide fate models used in regulatory and environmental assessments. Currently, these herbicide fate models do not consider associations between SOM characteristics and Koc and hence revising model equations to include these associations may improve estimates of herbicide persistence, bioavailability and transport at the field-scale.