Evaluation of Yield and Agronomic Performance of Leafed and Semi-leafless Pea Blends

The abstract of this item is unavailable due to an embargo

Optimizing Seeding Ratio for Semi-Leafless and Leafed Pea Mixture with Precise UAV Quantification of Crop Lodging

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

Evaluation of Yield and Agronomic Performance of Leafed and Semi-leafless Pea Blends

The abstract of this item is unavailable due to an embargo

Improving Saskatchewan-based pea yields through blending of semi-leafless and leafed peasImproving Saskatchewan-based pea yields through blending of semi-leafless and leafed peas

Non-Peer ReviewedWestern Canadian pea production for 2018 decreased by 13% from 2017, which was due to a decline in harvested area. Our previous group research found that the blend of semi-leafless (afaf TLTL) and normal leafed (AFAF TLTL) peas could create a 10% yield increase. To enhance the potential of pea blending. This project evaluated the compatibility of mixing Near-Isogenic Line (NIL) (where the inbred line is only different from the respective recurrent parent in one genomic location) pairs within blends. As well as, we evaluated different blending ratios of semi-leafless with their respective NIL leafed to receive more significant yield increases and avoid confounding variety effects. This study utilized four pea varieties: CDC Amarillo, CDC Centennial, CDC Dakota, and CDC Striker. The research conducted at the University of Saskatchewan's Kernen Research Farm in Saskatoon, Saskatchewan, Canada, over two years in the 2018 and 2019 growing seasons. To evaluating the blending ratio benefits and NIL blending compatibility, data collection focused on yield as well as other field performances such as plant biomass, disease, standing ability, pea leaf development, and phenotyping Digital elevation model (DEM) variation. The ratio combined experiment designed as 50/50, 66/37, 83/17 semi-leafless/NIL leafed blending ratios with two sole leaf type monocultures. In first-year results, we found that Amarillo and Striker varieties in the 83/17 blending ratio facilitated low disease severity. Blends approached similar lodging resistance for semi-leafless types and significantly decreased the leafed peas lodging tendency. The 50/50 Dakota blend increased canopy density and had slower canopy greenness decline when compared with the rest of the Dakota treatments. However, no significant yield improvements by blending detected in the first-year. To determine the compatibility of NIL blends. The variety combined experiment compared NIL blends versus Non-NIL blends (two varieties blend having different genetic backgrounds) and found no significant difference between them in all field data. In the first-year report, despite the results did not detect a significant yield improvement, but we found enhanced pea field characteristics, which should promote a higher yield potential. The comparison between NIL blends and Non-NIL blends statistically showed that NIL blends would adapt in the pea blend, which encourages the possibility of this technology release commercially

Quantifying Hail Damage in Crops Using Sentinel-2 Imagery

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