Lodging is a factor that negatively affects yield, seed quality, and harvest ability in winter rapeseed (Brassica napus L.). In this study, we quantified the lodging-induced yield losses, changes in fatty acid composition, and oil quality in rapeseed under different nitrogen application rates and planting densities. Field experiments were conducted in 2014–2017 for studying the effect of manually-induced lodging angles (0°, 30°, 60°, and 90°), 10, 20 and 30 d post-flowering at different densities and nitrogen application rates. The fertilization/planting density combination N270D45 produced the maximum observed yield and seed quality. Timing and angle of lodging had significant effects on yield. Lodging at 90° induced at 10 d post-flowering caused the maximum reduction in yield, biomass, and silique photosynthesis. Seed yield losses were higher at high N application rates, the maximum being at N360D45. Lodging decreased seed oil content and altered its fatty acid composition by increasing stearic and palmitic acid content, while decreasing linoleic and linolenic acid content, and deteriorating oil quality by increasing erucic acid and glucosinolate content. Therefore, lodging-induced yield loss and reduction in oil content might be reduced by selecting optimum N level and planting density.
Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.
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