Nitrogen and plant production

Novoa, R.; Loomis, R. S.

doi:10.1007/978-94-015-0861-2_7

Cited by 160 publications

(181 citation statements)

References 80 publications

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“…The reason for such a result was mainly because the metabolism process of green plants in later stages became complex and nitrogen was greatly accumulated and remobilized by that point. These results were consistent with those demonstrated by the studies of Novoa, R. et al [49] and Hamid et al [50]. They found that the maximum nitrogen fertilization was accumulated in the tillering stage.…”

Section: Band Combinations Based On Hsl Data For Rice Lnc Estimationsupporting

confidence: 93%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

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Section: Band Combinations Based On Hsl Data For Rice Lnc Estimationsupporting

confidence: 93%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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“…The rhizosphere microbiome can also supply the plant with essential nutrients such as nitrogen. Nitrogen is essential for plant growth (Burns 1996;Rossato et al, 2001) and it is usually deficient in soils (Novoa and Loomis, 1981). Thus, in natural environments (as compared to agricultural systems) the plant depends on N fixing and nitrifying bacteria for their N supply.…”

Section: Discussionmentioning

confidence: 99%

Rhizosphere microbiome assemblage is affected by plant development

2013

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There is a concerted understanding of the ability of root exudates to influence the structure of rhizosphere microbial communities. However, our knowledge of the connection between plant development, root exudation and microbiome assemblage is limited. Here, we analyzed the structure of the rhizospheric bacterial community associated with Arabidopsis at four time points corresponding to distinct stages of plant development: seedling, vegetative, bolting and flowering. Overall, there were no significant differences in bacterial community structure, but we observed that the microbial community at the seedling stage was distinct from the other developmental time points. At a closer level, phylum such as Acidobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria and specific genera within those phyla followed distinct patterns associated with plant development and root exudation. These results suggested that the plant can select a subset of microbes at different stages of development, presumably for specific functions. Accordingly, metatranscriptomics analysis of the rhizosphere microbiome revealed that 81 unique transcripts were significantly (Po0.05) expressed at different stages of plant development. For instance, genes involved in streptomycin synthesis were significantly induced at bolting and flowering stages, presumably for disease suppression. We surmise that plants secrete blends of compounds and specific phytochemicals in the root exudates that are differentially produced at distinct stages of development to help orchestrate rhizosphere microbiome assemblage.

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“…3, the rate of N fertilization in Shiga prefecture began to decrease in 1980s and it now averages around 70 kg ha -1 as compared with over 100 kg ha -1 in late 1970s. Nevertheless, the average grain yield was maintained or it rather increased moderately in the same period and thus improvement of agronomic effi ciency of fertilized N (AE) (Novoa and Loomis, 1981) is evident in this region. The R-DYA (i.e., the average yielding ability of cultivars) also decreased gradually during this period (Fig.…”

Section: (C) Intermittent Irrigation and Delayed Fl Oodingmentioning

confidence: 99%