Assessment of Drought Effect in Cultivars of Perennial Switchgrass (Panicum virgatum L.) Based on Elementomic Analysis and Chemometrics
Article Main Content
Panicum virgatum L. (switchgrass) is a perennial warm season native grass from North American prairies, which is used as forage due to its adaptability to grow in semi-arid regions. In this work, the assessment of response to drought in two cultivars of contrasting behaviour under water stress -Kanlow and Greenville cultivars- was performed. The aim was to evaluate the behaviour of both Panicum cultivars under water stress conditions based on elementomic study and chemometrics. 50 days old plants were grown in a growth chamber; drought treatment was applied by water suppression until moderate stress was reached. Multi-elemental analysis was performed by inductively coupled plasma atomic emission spectrometry (ICP-AES). Elemental profile data were analyzed by chemometrics methods including Principal Component Analysis (PCA) as unsupervised method and Partial least square discriminant analysis (PLS-DA) as supervised method. Remarkable differences were found based on elementomic study in both cultivars, reinforcing the ability of Kanlow to grown in semi-arid regions, where water and saline limitations are recurrent components. However, the high concentration of Pb found in Greenville samples, shows the ability of this cultivar to be used in the recovering of contaminated soils. Moreover, two elements (Si and Zn) were identified as potential markers to discriminate switchgrass samples belonging to different cultivars or geographic origins. Panicum virgatum cv. Kanlow is a good candidate to incorporate as a forage crop in semi-arid regions; however, cv. Greenville can be more recommendable to be used in the recovery of Pb contaminated soils.
References
-
Salt DE, Baxter I and Lahner B. Ionomics and the study of the plant ionome. Annu. Rev. Plant Biol., 2008; 59:709–733.
Google Scholar
1
-
Huang XY and Salt DE. Plant ionomics: from elemental profiling to environmental adaptation. Mol. Plant., 2016; 9:787–797.
Google Scholar
2
-
Baxter I. Should we treat the ionome as a combination of individual elements, or should we be deriving novel combined traits? J. Exp. Bot., 2015; 66:2127–2131.
Google Scholar
3
-
Baxter I. Ionomics: Studying the social network of mineral nutrients. Curr. Opin. Plant Biol., 2009; 12:381–386.
Google Scholar
4
-
Watanabe T, Urayama M and Shinano T. Application of ionomics to plant and soil in fields under long term fertilizer trials. SpringerPlus, 2015; 4:781.
Google Scholar
5
-
Parent SE, Parent LE and Egozcue JJ. The plant ionome revisited by the nutrient balance concept. Front. Plant Sci., 2013; 4:39.
Google Scholar
6
-
Zaldarriaga Heredia J, Moldes CA, Gil RA and Camiña JM. Elementomic and genetic technology assessment in maize (Zea mayze) plants based on analysis of leaf and seed tissues. Microchem. J., 2020; 159:105569.
Google Scholar
7
-
Aimar DC, Calafat JM, Andrade AM, Carassay LR, Bouteau F, Abdala GI and Molas ML. Drought effects on the early development stages of Panicum virgatum L: cultivar differences. Biomass Bioenerg, 2014; 66:49–59.
Google Scholar
8
-
Graham DR and Webb MJ. Micronutrients and disease resistance and tolerance in plants. Micronutrients in Agriculture. Madison: Soil Science Society of America Inc. 1991, pp. 329–370.
Google Scholar
9
-
Marschner H. Mineral nutrition of higher plants. Boston: Academic Press. 1995.
Google Scholar
10
-
Epstein E. 1999. Silicon and plant growth. Annual review of plant physiology. Plant Mol. Biol., 1999; 50:641–664.
Google Scholar
11
-
Habibi G. Role of trace elements in alleviating environmental stress. Emerging Technologies and management of crop stress tolerance. Amsterdam: Elsevier. 2014.
Google Scholar
12
-
Liu M, Wang TZ, Zhang WH. Sodium extrusion associated with enhanced expression of SOS1 underlies different salt tolerance between Medicago falcatand Medicago truncatula seedlings. Env. Exp. Bot., 2015;110:46–55.
Google Scholar
13
-
Briat JF, Curie C and Gaymard F. Iron utilization and metabolism in plants. Curr. Opin. Plant Biol., 2007;10:276–282.
Google Scholar
14
-
Ernst WHO, Krauss GJ, Verkleij JAC and Wesenberg D. Interaction of heavy metal with the sulphur metabolism in angiosperms from an ecological point of view. Plant Cell Environ., 2008; 31:123–143.
Google Scholar
15
-
Williams LE and Pittman JK. Dissecting pathways involved in manganese homeostasis and stress in higher plants. Cell biology of metals and nutrients, plant cell monographs. Berlin: Springer-Verlag, 2010 pp 95-117.
Google Scholar
16
-
Ricachenevsky FK, Menguer PK, Sperotto RA, Williams LE and Fett JP. Roles of plant metal tolerance proteins (MTP) in metal storage and potential use in biofortification strategies. Front. Plant Sci. 2013; 4:144.
Google Scholar
17
-
Waraich EA, Ahmad R, Ashraf M, Saifullah S and Ahmad M. Improving agricultural water use efficiency by nutrient management in crop plants. Soil Sci. Plant Nutr., 2011; 61:291–304.
Google Scholar
18
-
Gill SS, Hasanuzzaman M, Nahar K, Macovei A and Tuteja N. Importance of nitric oxide in cadmium stress tolerance in crop plants. Plant Physiol. Biochem., 2013; 63:254–261.
Google Scholar
19
-
[20] Pilon-Smits EA, Quinn CF, Tapken W, Malagoli M and Schiavon M. Physiological functions of beneficial elements. Curr Opin Plant Biol., 2009; 12:267–74.
Google Scholar
20
-
[21] Moldes CA, Fontão de Lima Filho O, Camiña JM, Kiriachek SG, Molas ML and Tsai SM. Assessment of the effect of silicon on antioxidant enzymes in cotton plants by multivariate analysis. J. Agr. Food Chem. 2013;60:11243–11249.
Google Scholar
21
-
Otto M. Chemometrics, Statistics and Computer. Application in Analytical Chemistry. Weinheim: Wiley-VCH, 1999, pp. 124–168.
Google Scholar
22
-
Mongay Fernández C. Quimiometría. Editorial Universitat de Valéncia, 2005.
Google Scholar
23
-
Naes T, Isaksson T and Fearn TA. User friendly guide to multivariate calibration and classification. NIR Publications, 2002.
Google Scholar
24
-
Hendrickson L, Crow W.S and Furbank RL, Low temperature effects on grapevine photosynthesis: the role of inorganic phosphate. Funct. Plant Biol., 2004; 31:789–801.
Google Scholar
25
-
Flügge UI, Häusler RE, Ludewig F, and Fischer K. Functional genomics of phosphate antiport systems of plastids. Plant Physiol., 2003;118:475–482.
Google Scholar
26
-
Lawlor DW and Cornic G. Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants. Plant Cell Environment, 2002; 25:275–294.
Google Scholar
27
-
Firmano RS, Kuwahara FA and Souza GM. Relação entre adubação fosfatada e deficiência hídrica em soja. Ciência Rural. 2009; 39:1967-1973.
Google Scholar
28
-
Wissuwa M. How do plants achieve tolerance to phosphorus deficiency: Small causes with big effects. Plant Physiol., 2003; 133:1947–1958.
Google Scholar
29
-
Kuwahara FA and Souza GM. Fósforo como possível mitigador dos efeitos da deficiência hídrica sobre o crescimento e as trocas gasosas de Brachiaria brizantha cv. MG-5 Vitória. Acta Scientiarum. Agronomy, 2009; 31:261–267.
Google Scholar
30
-
Barney JN and Di Tomaso JM. Bioclimatic predictions of habitat suitability for the biofuel switchgrass in North America under current and future climate scenarios. Biomass Bioenerg, 2010; 34:124–133.
Google Scholar
31
-
Kim S, Rayburn AL, Voigt T, Parrish A and Lee DK. Salinity effects on germination and plant growth of prairie cordgrass and switchgrass. Bioenergy, 2012;5:225–235.
Google Scholar
32
-
Liu YM, Zhang, XZ, Miao JM, Huang LK, Frazier T and Zhao BY. Evaluation of salinity tolerance and genetic diversity of thirty-three switchgrass (Panicum virgatum) lines. Bioenerg, 2014;18:1329–1342.
Google Scholar
33
-
Hu G, Liu Y, Zhang X, Yao F, Huang Y, Ervin EH and Zhao, B. Physiological evaluation of alkali-salt tolerance of thirty switchgrass (Panicum virgatum) lines. PLOS ONE, 2015; DOI:10.1371/journal.pone.0125305.
Google Scholar
34
-
Baker M and Rayens W. Partial least squares for discrimination. J. Chemom, 2003;17:166–173.
Google Scholar
35
-
Kasemsumran S, Kang N, Christy A and Ozaki Y. Partial least squares processing of near-infrared spectra for discrimination and quantification of adulterated olive oils. Spectrosc Lett, 2005;38:839–851.
Google Scholar
36
-
Amigo J, Ravn C, Gallagher NB and Bro R. A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging. Int. J. Pharm, 2009;37:179–182.
Google Scholar
37
-
Porter CL. An analysis of variation between upland and lowland switchgrass, Panicum virgatum L in central Oklahoma. Ecol., 1966;47:980–992.
Google Scholar
38
-
Casler MD. Switchgrass Breeding, Genetics, and Genomics. Publications from USDA-ARS/UNL Faculty. 2012, pp. 1235.
Google Scholar
39
-
Moser LE and Vogel KP. Switchgrass, big bluestem, and indiangrass. Forages: An Introduction to Grassland Agriculture. Iowa State Univ. Press. 1995, pp. 409–420.
Google Scholar
40
-
Casler MD, Vogel KP, Taliaferro CM and Wynia RE. Latitudinal adaptation of switchgrass populations. Crop Sci., 2004;44:293–403.
Google Scholar
41
-
Barney JN. For switchgrass cultivated as biofuel in California, invasiveness limited by several steps. Calif. Agric., 2013;67:96–103.
Google Scholar
42