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Tagliavini, Massimo; Quartieri, Maurizio; Millard, P.

doi:10.1023/a:1004207918453

Cited by 68 publications

(12 citation statements)

References 15 publications

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“…As reported by Tagliavini et al, the remobilization of nitrogen ceased between petal fall and the beginning of pear fruit development [34]. Nitrogen was transported into shoot leaves several weeks after bloom; shoot leaves are more dependent than spur leaves on spring N uptake.…”

Section: Methodsmentioning

confidence: 80%

“…This period is a key period in fruit growth, and non-destructive diagnosis of the N status is important for timely fertilizer application for fruit quality and yield. Previous studies [5,34] have suggested that, after bloom, the earlier the leaf N level was diagnosed the better so that an increasing number of methods can be adopted to increase or decrease the leaf N concentration as soon as possible. In the present study, a closer relationship was found between the N concentration and fruit yield in the May sampling (both in 2014 and 2015) than in the June sampling (Figure 10).…”

Section: Discussionmentioning

confidence: 99%

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Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Wang

Shen

Liu

et al. 2017

Sensors

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Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha−1. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R2 values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R2 = 0.77) and 2014 (R2 = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg.

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Section: Methodsmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Wang

Shen

Liu

et al. 2017

Sensors

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“…Growth and biomass production depend on the size of internal N pools from which N can be remobilized in spring [68], as well as external N uptake [69,70], especially in woody seedlings [71,72]. A metabolically active protein that also serves as N storage pool is RuBisCo, and N storage pools turn over completely during periods of N remobilization [73].…”

Section: Variation Between Metabolites In Green Leaves Between Differmentioning

confidence: 99%

Environmental Conditions and Species Identity Drive Metabolite Levels in Green Leaves and Leaf Litter of 14 Temperate Woody Species

Simon

Dörken

L.-M.-Arnold

et al. 2018

Forests

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Research Highlights: Leaf chemistry is a key driver of litter decomposition; however, studies directly comparing metabolites that are important for tree growth and defence across different woody species are scarce. Background and Objectives: Choosing 14 temperate woody species differing in their growth rates, nutrient demand, shade tolerance, and drought sensitivity, we hypothesized that the species would group according to their metabolite profiles based on their ecological background. Materials and Methods: We analysed total N and C, soluble amino acid, protein, and phenolic levels in green leaves and leaf litter of these species, each in two consecutive years. Results: Metabolite levels varied significantly across species and between the sampling years which differed in temperature and precipitation (i.e., colder/drier vs warmer/ wetter). Conclusions: The 14 woody species could not be grouped according to their green leaf or leaf litter metabolite profiles. In litter leaves, most of the variation was explained by total phenolics and total nitrogen levels, and in green leaves by total phenolics and total soluble amino acid levels. Local climate variation between the two consecutive years for green leaves or leaf litter led to significant differences in metabolite levels, although some of them were species-specific.

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“…In deciduous trees, woody organs are the main storage sites; young trees tend to store N in trunks and/or roots (Millard and Grelet 2010), while mature trees store N in branches, particularly young branches (Bazot et al 2013;Han et al 2014;Li et al 2015). This storage distribution is generally considered advantageous because it reduces the distance required for N transport to sink organs (Staswick 1994;Tagliavini et al 1997;Bazot et al 2013). In this regard, reproductive organs are stronger N sinks than vegetative organs (Han et al 2017).…”

Section: Nitrogen Storage Locationsmentioning

confidence: 99%

“…In early spring, internally stored N is usually converted to asparagine and glutamine for remobilization to new growth in leaves and shoots to cope with the temporal uncoupling between nutrient supply from soils and growth demands (Millard et al 2006). In a range of deciduous and evergreen species, N remobilization from internal storage typically occurs for 20-30 days before the roots actively take up N (Millard and Proe 1993;Tagliavini et al 1997;Millard et al 2001Millard et al , 2006Millard and Grelet 2010;El Zein et al 2011). There is clear evidence demonstrating that flowering in orchard species relies primarily on the remobilization of internal N stores (Weinbaum and Van Kessel 1998;Policarpo et al 2002;Cheng and Raba 2009); for example, using 15 N labeling in 9-year-old walnut (Juglans regia cv.…”

Section: Remobilization In the Following Springmentioning

confidence: 99%

Recent developments in understanding mast seeding in relation to dynamics of carbon and nitrogen resources in temperate trees

Han

Kabeya

2017

Ecological Research

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Mast seeding, the synchronous intermittent production of large seed crops in populations of perennial plants, is a widespread and widely studied phenomenon. Economy of scale has been demonstrated to provide the ultimate selection factor driving the evolution of masting, for example, in terms of the predatorsatiation and pollination-efficiency hypotheses; however, its physiological mechanism is still poorly understood. The resource budget (RB) model assumes that an individual plant requires more resources to flower and fruit than it gains in a year, and therefore only flowers when a specific threshold amount of stored resources is surpassed. Although the RB models have been well explored theoretically, including for resource depletion and pollen coupling, empirical data to support these assumptions are still disputed. Here, we explore the extent to which the RB model applies to masting tree species, focusing on the dynamics of carbon and nitrogen resources in natural temperate forests. There is little empirical evidence that plants use carbohydrates stored over several years to produce fruits; however, nitrogen stores in temperate trees are more commonly depleted after masting. We review the internal nitrogen cycle including resorption during leaf senescence, storage and remobilization, discussing the effect of masting on these processes. Overall, carbohydrates and nitrogen are clearly involved in the proximate mechanisms driving mast seeding, but the determinant resource seems to be species specific.

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Cited by 68 publications

References 15 publications

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Environmental Conditions and Species Identity Drive Metabolite Levels in Green Leaves and Leaf Litter of 14 Temperate Woody Species

Recent developments in understanding mast seeding in relation to dynamics of carbon and nitrogen resources in temperate trees

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