Effectiveness of the entropy weight method to evaluate abiotic stress tolerance in citrus rootstocks
Abstract
Aim of study: The entropy weight method (EWM) is considered one of the most reliable and scientific approaches when decision making is influenced by multiple factors. However, there are no reports on the application of EWM in the evaluation of abiotic and biotic stress tolerance in crops. In this study, abiotic stress via saline water irrigations was imposed on different citrus rootstocks. The relative stress tolerance levels of rootstocks were ascertained using EWM and compared with standard fuzzy membership approach and the factor analysis.
Area of study: Punjab Agricultural University Regional Research Station Abohar, India, 2017-2019.
Material and methods: In a pot culture study, about 1½ yr-old rootstock seedlings were exposed to saline water irrigations with 4 and 6 dS m-1 electrical conductivity (EC) for 60 days. Saline water response index for mineral composition of plant parts, physiological and biochemical attributes of rootstocks were calculated for each salinity level over 2 dS m-1 conductivity water, considered as control and subjected to further analysis.
Main results: At 4 EC, the entropy weight and membership function value of the rootstocks ranged 0.758-0.998 and 0.682-0.731, respectively. The corresponding values at 6 EC ranged between 0.759-0.991 and 0.391-0.728, respectively. Following EWM, the rootstocks were rated for their relative tolerance levels as: Rangpur Lime>Cleopatra>Volkamer Lemon=Rough Lemon>Carrizo at 4 EC salinity level. At 6 EC, the order was: Cleopatra>Rangpur Lime>Volkamer Lemon>Rough Lemon>Carrizo. The results were consistent between EWM and standard principle component analysis approaches.
Research highlights: The study suggests that the comprehensive evaluation of crop genotypes for abiotic stress tolerance is also feasible with the EWM.
Downloads
References
Acosta-Motos JR, Ortuno MF, Bernal-Vicente A, Diaz-Vivancos P, Sanchez-Blanco MJ, Hernandez JA, 2017. Plant responses to salt stress: Adaptive mechanisms. Agron J 7: 1-38. https://doi.org/10.3390/agronomy7010018
Al-Dakheel AJ, Hussain MI, Qader MA, Rahman A, 2015. Impact of irrigation water salinity on agronomical and quality attributes of Cenchrus ciliaris L. accessions. Agric Water Manag 159: 148-54. https://doi.org/10.1016/j.agwat.2015.06.014
Balal RM, Khan MM, Shahid MA, Mattson NS, Abbas T, Asfaq M, et al., 2012. Comparative studies on the physio-biochemical, enzymatic, and ionic modifications in salt-tolerant and salt-sensitive citrus rootstocks under NaCl stress. J Am Soc Hort Sci 137: 86-95. https://doi.org/10.21273/JASHS.137.2.86
Barbosa RCA, Brito MEB, Silva SFV, Filho WSS, Fernandes PD, Silva LA, 2017. Gas exchange of citrus rootstocks in response to intensity and duration of saline stress. Cienc Agrar 38: 725-738. https://doi.org/10.5433/1679-0359.2017v38n2p725
Bates LS, Waldren RP, Tear ID, 1973. Rapid determination of free proline for water stress. Plant Soil 39: 205-207. https://doi.org/10.1007/BF00018060
Cassaniti C, Leonardi C, Flower TJ, 2009. The effect of sodium chloride on ornamental shrubs. Sci Hort 122: 586-593. https://doi.org/10.1016/j.scienta.2009.06.032
Chance B, Maehley AC, 1955. Assay of catalase and peroxidase. In: Methods in enzymology; Colowick S & Kaplan N (eds.). pp: 764-775. Academic Press, NY. https://doi.org/10.1016/S0076-6879(55)02300-8
Cummins JN, Aldwinckle HS, 1995. Breeding rootstocks for tree fruit crops. New Zeal J Crop Hort Sci 23: 395-402. https://doi.org/10.1080/01140671.1995.9513915
Flowers TJ, Flowers SA, 2005. Why does salinity pose such a difficult problem for plant breeders? Agric Water Manag 78: 15-24. https://doi.org/10.1016/j.agwat.2005.04.015
Jackson ML, 2005. Soil chemical analysis. Parallel Press, Univ. of Wisconsin, Madison, WI, USA. 925 pp.
Lacroix RL, Keeney DR, Walsh LM, 1970. Potentiometric titration of chloride in plant tissue extracts using the chloride ion electrode. Commun Soil Sci Plant Anal 1: 1-6. https://doi.org/10.1080/00103627009366233
Levy Y, Syvertsen J, 2004. Irrigation water quality and salinity effects in citrus trees. In: Horticultural reviews; Janick J (ed.). John Wiley & Sons, Inc. pp. 37-82. https://doi.org/10.1002/9780470650837.ch2
Levy Y, Lifshitz J, Malach YD, David Y, 1999. The response of several citrus genotypes to high-salinity irrigation water. HortSci 34: 878-881. https://doi.org/10.21273/HORTSCI.34.5.878
Li X, Wang K, Liu L, Xin J, Yang H, Gao C, 2011. Application of entropy weight and TOPSIS method in safety evaluation of coal mines. Proced Eng 26: 2085-2091. https://doi.org/10.1016/j.proeng.2011.11.2410
Li W, Zhang H, Zeng Y, Xiang L, Lei Z, Huang Q, et al., 2020. A salt tolerance evaluation method for sunflower (Helianthus annuus L.) at the seed germination stage. Sci Reports 10: 10626. https://doi.org/10.1038/s41598-020-67210-3
Mandal S, Raju R, Kumar A, Kumar P, Sharma PC, 2018. Current status of research, technology response and policy needs of salt-affected soils in India - A review. Ind Soc Coast Agr Res 36: 40-53.
Mansour E, Moustafa ESA, Desoky El-Sayed M, Ali MMA, Yasin MAT, Attia A, et al., 2020. Multidimensional evaluation for detecting salt tolerance of bread wheat genotypes under actual saline field growing conditions. Plants 10: 1324. https://doi.org/10.3390/plants9101324
Marklund S, Marklund G. 1974. Involvement of the superoxide anion radical in the autoxidation of pyrogallol and a convenient assay for superoxide dismutase. Eur J Biol Res 47: 469-474. https://doi.org/10.1111/j.1432-1033.1974.tb03714.x
Munns R, 2005. Genes and salt tolerance: bringing them together. New Phytol 167: 645-663. https://doi.org/10.1111/j.1469-8137.2005.01487.x
Muscolo A, Mallamaci C, Panuccio MR, Caputo R, Pascale SD, 2011. Effect of long-term irrigation water salinity on soil properties and microbial biomass. Eco Quest 14: 77-83. https://doi.org/10.12775/v10090-011-0022-7
Papageorgiou EI, Kokkinos K, Dikopoulou Z, 2016. Fuzzy sets in agriculture. In: Fuzzy logic in its 50th year; Kahraman C et al. (eds.). pp: 211-233. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-319-31093-0_10
Rahman MM, Rahman MA, Miah MG, Saha SR, Karim MA, Mostofa MG, 2017. Mechanistic insight into salt tolerance of Acacia auriculiformis: The importance of ion selectivity, osmoprotection, tissue tolerance, and Na+ exclusion. Front Plant Sci 8: 155. https://doi.org/10.3389/fpls.2017.00155
Shannon CE, 1948. A mathematical theory of communications. Bell Sys Tech J 27: 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Shannon ML, Kay E, Lew JY, 1966. Peroxidase isozyme from horseradish root. J Biol Chem 241: 2166-2172. https://doi.org/10.1016/S0021-9258(18)96680-9
Smart RE, Bingham GE, 1974. Rapid estimation of relative water content. Plant Physiol 53: 258-260. https://doi.org/10.1104/pp.53.2.258
Sivakumar J, Prashanth JEP, Rajesh N, Reddy SM, Pinjari OB, 2020. Principal component analysis approach for comprehensive screening of salt stress-tolerant tomato germplasm at the seedling stage. J Biosci 45: 141. https://doi.org/10.1007/s12038-020-00111-9
Storey R, Walker RR, 1999. Citrus and salinity. Sci Hort 78: 39-81. https://doi.org/10.1016/S0304-4238(98)00190-3
Straten GV, de Vos AC, Rozema J, Bruning B, Bodegom PMV, 2013. An improved methodology to evaluate crop salt tolerance from field trials. Agric Water Manag 213: 375-387. https://doi.org/10.1016/j.agwat.2018.09.008
Wu H, Guo J, Wang C, Li K, Zhang X, Yang Z, et al., 2019. An effective screening method and a reliable screening trait for salt tolerance of Brassica napus at the germination stage. Front Plant Sci 10: 530. https://doi.org/10.3389/fpls.2019.00530
Zhi-hong Z, Yi Y, Jing-nan S, 2006. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Env Sci 18: 1020-1023. https://doi.org/10.1016/S1001-0742(06)60032-6
Zhou Y, Zhang Q, Li K, Chen X, 2011. Hydrological effects of water reservoirs on hydrological processes in the East River (China) basin: complexity evaluations based on the multi-scale entropy analysis. Hydro Processes 26: 3253-3262. https://doi.org/10.1002/hyp.8406
Zhu Y, Tian D, Yan F, 2020. Effectiveness of entropy weight method in decision-making. Math Prob Eng: Art ID 3564835. https://doi.org/10.1155/2020/3564835
Copyright (c) 2022 Spanish Journal of Agricultural Research

This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the print and online versions of this journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You may read the basic information and the legal text of the licence. The indication of the CC BY 4.0 licence must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the final version of the work produced by the publisher, is not allowed.