Eric N. Johnson

The production of barley cultivars with malting and brewing quality characteristics is subject to strict grading and technical standards for the end-use market. Environmental and management factors can significantly alter grain quality, and the qualities required for malting. Crop and kernel uniformity are critical factors where variability can exceed the tolerance for meeting malt quality. A practice to address variations in crop maturity is the application of pre-harvest glyphosate. Pre-harvest glyphosate applications can, however, alter malting characteristics in barley, and, if mis-timed, can also reduce yield. A 4-year study at five locations in Alberta and Saskatchewan from 2013 to 2017 was conducted to determine the effects of pre-harvest glyphosate applications on malting barley characteristics. Glyphosate was applied at 900 and 1125 g ae ha⁻¹ on malting barley cultivars ‘CDC Meredith’ and ‘AC Metcalfe’ malting barley at soft dough, hard dough, and physiological maturity growth stages. Yield reductions of up to 12% were observed from glyphosate applications at soft dough, and yield was maximized with applications at physiological maturity. Glyphosate application, at two rates, reduced percentage plump kernels, but did not affect kernel weight or protein concentration. The growth stage of barley plants did not provide an accurate indicator of seed moisture levels at the time of application, which motivated our conclusion that glyphosate applications can be mistimed by inaccurate indicators. The results motivate our questioning of the utility of pre-harvest glyphosate applications, given the adverse effects to barley yield and quality observed, even when applied according to the label instructions.

The field pea has both semi-leafless (SL) and leafed (L) types. Mixing these two types together might improve yield by optimizing pea solar radiation interception, reducing lodging, and decreasing disease. However, an optimum mixing ratio has not yet been established, since previous studies mixed two leaf types from two separate varieties. This study used four near-isogenic pairs of pea genotypes differing only in leaf type to determine the optimal mixing ratio for yield and agronomic traits. Two leaf types were mixed at seeding in five mixing ratios: 0:100, 50:50, 67:33, 83:17, and 100:0 SL/L. With precise UAV quantification of canopy height (r² = 0.88, RMSE = 2.6 cm), the results showed that a ratio of over 67% semi-leafless pea had a 10% greater lodging resistance when compared to the leafed monoculture. For mycosphaerella blight and Uromyce viciae-fabae rust diseases, the 83:17 mixture decreased disease severity by 4% when compared with the leafed monoculture. Regression analysis of yield estimated that the 86:14 ratio provided an 11% increase to the leafed monoculture, but there was no increase compared with the semi-leafless monoculture. Mixing the two types in a high semi-leafless ratio can reduce leafed lodging and prevent yield loss but does not increase the overall yield over the semi-leafless monoculture.

Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.

The traditional visual rating system is labor-intensive, time-consuming, and prone to human error. Unmanned aerial vehicle (UAV) imagery-based vegetation indices (VI) have potential applications in high-throughput plant phenotyping. The study objective is to determine if UAV imagery provides accurate and consistent estimations of crop injury from herbicide application and its potential as an alternative to visual ratings. The study was conducted at the Kernen Crop Research Farm, University of Saskatchewan in 2016 and 2017. Fababean (Vicia faba L.) crop tolerance to nine herbicide tank mixtures was evaluated with 2 rates distributed in a randomized complete block design (RCBD) with 4 blocks. The trial was imaged using a multispectral camera with a ground sample distance (GSD) of 1.2cm, one week after the treatment application. Visual ratings of growth reduction and physiological chlorosis were recorded simultaneously with imaging. The optimized soil-adjusted vegetation index (OSAVI) was calculated from the thresholded orthomosaics. The UAV-based vegetation index (OSAVI) produced more precise results compared to visual ratings for both years. The coefficient of variation (CV) of OSAVI was 1 when compared to 18-43 for the visual ratings. Furthermore, Tukeys honestly significance difference (HSD) test yielded a more precise mean separation for the UAV-based vegetation index than visual ratings. The significant correlations between OSAVI and the visual ratings from the study suggest that undesirable variability associated with visual assessments can be minimized with the UAV-based approach. UAV-based imagery methods had greater precision than the visual-based ratings for crop herbicide damage. These methods have the potential to replace visual ratings and aid in screening crops for herbicide tolerance.

Field trials were initiated in fall 2011 to determine the potential of pyroxasulfone to control acetolactate synthase (ALS) inhibitor-resistant weeds in field pea. Pyroxasulfone was applied in split-plot trials at five locations in western Canada using fall and PRE spring applications of 0 to 400 g ai ha⁻¹. Trial locations were chosen with a range of soil organic matter content: 2.9, 4.3, 5.5, 10.5, and 10.6% at Scott, Kernen, Kinsella, Melfort, and Ellerslie, respectively. The herbicide dose required to reduce biomass by 50% (ED₅₀) in false cleavers ranged between 53 and 395 g ha⁻¹at Scott and Ellerslie, respectively. Wild oat ED₅₀s varied between 0.54 g ha⁻¹at Scott in the fall and 410 g ai ha⁻¹in the spring at Melfort. ED₅₀s for wild oat and false cleavers varied by 7.4- and 746-fold, respectively, depending primarily on the organic matter content at the trial location. The effect of application timing was not consistent. Significant yield reductions and pea injury occurred at 150 and 100 g ha⁻¹and higher at Kernen and Scott, respectively. Low organic matter and high precipitation levels at these locations indicates increased herbicide activity under these conditions. Pyroxasulfone may allow control of ALS inhibitor-resistant false cleavers and wild oat; however, locations with high soil organic matter will require higher rates than those with low organic matter for similar control levels.

The objective of this study was to evaluate the impact of agronomic practices on net return (NR) risk for malting barley (Hordeum vulgare L.) production. This study used data from two field experiments conducted from 2005 to 2008 at eight rainfed locations in western Canada. The first part of this study included 30 production strategies of barley type, seeding rate, and N rate for four regions. The second part of this study included 10 production strategies of seeding date and seeding rate for four regions. A stochastic simulation model was specified to compute the NR. Yield, protein, plumpness, and price were random in the model, drawn from multivariate distributions based on field data and historical price data. The malting cultivar CDC Copeland had higher NR than AC Metcalfe or feed barley. Seeding early at a rate of 200 to 300 seeds m⁻² had higher NR than late seeding or higher seeding rates. A fertilizer rate of 60 to 90 kg N ha⁻¹ had higher NR. A producer with high risk aversion preferred strategies that were less risky including: less N fertilizer, growing feed barley in regions that have high protein and smaller price premiums for malting, and seeding later.