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Non-invasive spectral detection of the beneficial effects of Bradyrhizobium spp. and plant growth-promoting rhizobacteria under different levels of nitrogen application on the biomass, nitrogen status, and yield of peanut cultivars

机译:非侵入性光谱检测对缓生根瘤菌的有益作用。施氮水平对花生品种生物量,氮素状况和产量的影响和促进植物生长的根瘤菌

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High-throughput phenotyping using spectral reflectance measurements offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of the crop canopy for the assessment of biomass, nitrogen concentration, nitrogen uptake, relative chlorophyll contents, and yield in 2 peanut cultivars, Giza 5 and Giza 6. Peanuts were grown under field conditions and subjected to 3 doses of nitrogen fertilizer with or without the application of 2 bio-fertilizers, Bradyrhizobium spp. or plant growth-promoting rhizobacteria. Simple linear regression of normalized difference spectral indices and partial least square regression (PLSR) were employed to develop predictive models to estimate the measured parameters. The tested spectral reflectance indices were significantly related to all measured parameters with R 2 of up to 0.89. The spectral reflectance index values differed at the same level of nitrogen fertilizer, as well as among the 3 levels of nitrogen fertilizer application for inoculation with Bradyrhizobium and co-inoculation with Bradyrhizobium and plant growth-promoting rhizobacteria. The calibration models of PLSR data analysis further improved the results, with R 2 values reaching 0.95. The overall results of this study indicate that hyperspectral reflectance measurements monitoring peanut plants enable rapid and non-destructive assessment of biomass, nitrogen status, and yield parameters of peanut cultivars subjected to various agronomic treatments.
机译:使用光谱反射率测量的高通量表型为潜在地提供更多信息提供了信息,以便在作物冠层水平上实时做出更明智的管理决策。这项研究的目的是调查对作物冠层进行高光谱反射测量的适用性,以评估2个花生品种Giza 5和Giza 6的生物量,氮浓度,氮吸收,相对叶绿素含量和产量。在田间条件下,在施用或不施用2种生物肥料Bradyrhizobium spp的情况下,要施用3份氮肥。或促进植物生长的根瘤菌。使用归一化差异光谱指数的简单线性回归和偏最小二乘回归(PLSR)来开发预测模型,以估计测量的参数。测试的光谱反射指数与所有测量参数显着相关,R 2最高为0.89。光谱反射指数值在相同水平的氮肥中以及在三个水平的氮肥施用量中均存在差异,在三个水平的氮肥施用中,根瘤菌接种和根瘤菌共接种以及促进植物生长的根瘤菌。 PLSR数据分析的校准模型进一步改善了结果,R 2值达到0.95。这项研究的总体结果表明,监测花生植物的高光谱反射率测量能够快速,无损地评估经过各种农艺处理的花生品种的生物量,氮素状况和产量参数。

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