Abstract
Humic-like substances (HLSs) isolated by alkaline oxidative hydrolysis from lignin-rich agro-industrial residues have been shown to exert biostimulant activity toward maize (Zea mays L.) germination and early growth. The definition of a quantitative structure-activity relationship (QSAR) between HLS and their bioactivity could be useful to predict their biological properties and tailor plant biostimulants for specific agronomic and industrial uses. Here, we created several projection on latent structure (PLS) regression by using published analytical data on the molecular composition of lignin-derived HLS obtained by both 13C-CPMAS-NMR spectra directly on samples and 31P-NMR spectra after derivatization of hydroxyl functions with a P-containing reagent (2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane). These spectral data were used to model the effect of HLS on the elongation of primary root, lateral seminal roots, total root apparatus, and coleoptile of maize. The 13C-CPMAS-NMR data suggested that methoxyl and aromatic moieties positively affected plant growth, while the carboxyl/esterified functions showed a negative impact on the overall seedling development. Alkyl C seems to promote Col elongation while concomitantly reducing that of the root system. Additionally, 31P-NMR-derived spectra revealed that the elongation of roots and Col were enhanced by the occurrence of aliphatic hydroxyl groups, and guaiacyl and p-Hydroxyphenyl lignin monomers. The PLS models based on raw dataset from 13C-CPMAS-NMR spectra explained more than 74% of the variance for the length of lateral seminal roots, total root system and coleoptile, while other parameters derived from 13C-CPMAS-NMR spectra, namely the Hydrophobicity and Hydrophilicity of materials were necessary to explain 83% of the variance of the primary root length. The results from 31P-NMR spectra explained the observed biological variance by 90, 96, 96, and 93% for the length of primary root, lateral seminal roots, total root system and coleoptile, respectively. This work shows that different NMR spectroscopy techniques can be used to build up PLS models which can predict the bioactivity of lignin-derived HLS toward early growth of maize plants. The established QSAR may also be exploited to enhance by chemical techniques the bioactive properties of HLS and enhance their plant stimulation capacity.
Highlights
Plant biostimulants are a novel class of fertilizing products that improve the “plants’ nutrient use efficiency, tolerance to abiotic stress, quality traits, and/or increasing the availability of confined nutrients in the soil or rhizosphere” (EU Fertilising Products Regulation, 1009/2019)
The molecular composition based on 13C-CPMAS-Nuclear Magnetic Resonance (NMR) spectra of various humic-like substances (HLSs) and their bioactive responses on maize germination are reported in Table 1, whereas the original spectra were shown in Supporting Figure SF1
The aromatic and phenolic content in HLS isolated from Liliopsida (MG, humic-like substances isolated from giant reed (AD), BYP 1, and BYP 2) was more abundant than in those extracted from Magnoliopsida (CAR, RIP, LIM, and humic-like substances isolated from eucalypt (EUC)) (Table 1)
Summary
Plant biostimulants are a novel class of fertilizing products that improve the “plants’ nutrient use efficiency, tolerance to abiotic stress, quality traits, and/or increasing the availability of confined nutrients in the soil or rhizosphere” (EU Fertilising Products Regulation, 1009/2019). Extracting HLS from energy crops or agro-industrial residues is important from the environmental point of view, because it involves recycling precious photosynthate, which would be otherwise burnt or landfilled (Cherubini, 2010; Savy et al, 2020). Both HS and HLS are reckoned to boost crop growth and yields, and protect plants from abiotic stresses, by triggering specific metabolic routes (Aguiar et al, 2016; Khaleda et al, 2017; Vinci et al, 2018). HS and HLS have been applied to seeds or hydroponically-grown plants to study their bioactivity without the environmental complexities of field trials (Ertani et al, 2013; Savy et al, 2018; Spaccini et al, 2019)
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