IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i3p254-d1576268.html
   My bibliography  Save this article

Evaluation of Salt Tolerance in Four Self-Rooted Almond Genotypes for Super-High-Density Orchards Under Varying Salinity Levels

Author

Listed:
  • Xavier Rius-García

    (Department of Agricultural and Environmental Sciences, Higher Polytechnic School of Huesca, University of Zaragoza, Ctra. Cuarte s/n, 22071 Huesca, Spain
    Agromillora Group, Plaça Manel Raventós 3-5, St. Sadurní d’Anoia, 08770 Barcelona, Spain)

  • María Videgain-Marco

    (Department of Agricultural and Environmental Sciences, Higher Polytechnic School of Huesca, University of Zaragoza, Ctra. Cuarte s/n, 22071 Huesca, Spain
    AgriFood Institute of Aragón (IA2, CITA–University of Zaragoza), Ctra. Cuarte s/n, 22071 Huesca, Spain)

  • José Casanova-Gascón

    (Department of Agricultural and Environmental Sciences, Higher Polytechnic School of Huesca, University of Zaragoza, Ctra. Cuarte s/n, 22071 Huesca, Spain
    AgriFood Institute of Aragón (IA2, CITA–University of Zaragoza), Ctra. Cuarte s/n, 22071 Huesca, Spain)

  • Luis Acuña-Rello

    (Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avda. Madrid 44, 34004 Palencia, Spain)

  • Raquel Zufiaurre-Galarza

    (Department of Analytical Chemistry, Higher Polytechnic School of Huesca, University of Zaragoza. Ctra. Cuarte s/n, 22071 Huesca, Spain)

  • Pablo Martín-Ramos

    (Department of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avda. Madrid 44, 34004 Palencia, Spain)

Abstract

Increasing soil salinity threatens almond production globally, driving the need for the development of salt-tolerant cultivars. This study investigated the salt tolerance mechanisms of four self-rooted almond genotypes (Vialfas, Guara, Penta, and Avijor) under controlled conditions. Young plants were exposed to four salinity levels (0, 25, 50, and 75 mM NaCl) for 5 months. Growth parameters (trunk diameter, shoot length, fresh and dry weights), physiological responses (chlorophyll fluorescence, gas exchange, Soil–Plant Analysis Development (SPAD)), and mineral content were analyzed. Results show significant genotype-specific responses at the critical salinity threshold of 50 mM NaCl. Under these conditions, Guara and Vialfas maintained higher stem fresh weights (31.4 g and 37 g, respectively), while Avijor showed significant declines. Trunk diameter measurements revealed Vialfas’ superior performance (7 mm) compared to Guara and Penta (both around 6 mm), while Avijor exhibited the most significant reduction (5 mm). Chlorophyll fluorescence parameters indicated stress impact, with F v /F m values decreasing to 0.84 compared to control values of 0.87. Guara maintained higher K + /Na + ratios in leaves (3.05) compared to Avijor (1.95), while Penta showed better Na + exclusion ability with the lowest leaf Na + content (0.57%). Cl − accumulation patterns also differed among genotypes, with Avijor and Vialfas showing higher leaf Cl − concentrations (0.74% and 0.73%, respectively) compared to Penta (0.44%). Genotype responses across all salinity levels revealed distinct tolerance patterns: Guara maintained growth and physiological functions across treatments, while Penta showed remarkable stability under high salinity. Vialfas exhibited vigor at low salinity but declined sharply at 75 mM NaCl. Avijor demonstrated the highest salt sensitivity. These findings highlight the genetic variability in salt tolerance among almond cultivars and identify potential sources of salt-tolerant traits for breeding programs. The study also provides insights for optimizing genotype selection and management strategies in salt-affected orchards, contributing to more sustainable almond production in challenging environments.

Suggested Citation

  • Xavier Rius-García & María Videgain-Marco & José Casanova-Gascón & Luis Acuña-Rello & Raquel Zufiaurre-Galarza & Pablo Martín-Ramos, 2025. "Evaluation of Salt Tolerance in Four Self-Rooted Almond Genotypes for Super-High-Density Orchards Under Varying Salinity Levels," Agriculture, MDPI, vol. 15(3), pages 1-27, January.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:254-:d:1576268
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/3/254/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/15/3/254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    2. Frommlet, Florian & Ruhaltinger, Felix & Twaróg, Piotr & Bogdan, Małgorzata, 2012. "Modified versions of Bayesian Information Criterion for genome-wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1038-1051.
    3. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.
    4. Gaorong Li & Liugen Xue & Heng Lian, 2012. "SCAD-penalised generalised additive models with non-polynomial dimensionality," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 681-697.
    5. Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
    6. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    7. Shan Luo & Zehua Chen, 2014. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1229-1240, September.
    8. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.
    9. Lian, Heng & Du, Pang & Li, YuanZhang & Liang, Hua, 2014. "Partially linear structure identification in generalized additive models with NP-dimensionality," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 197-208.
    10. Molly C. Klanderman & Kathryn B. Newhart & Tzahi Y. Cath & Amanda S. Hering, 2020. "Fault isolation for a complex decentralized waste water treatment facility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 931-951, August.
    11. Tang, Yanlin & Song, Xinyuan & Wang, Huixia Judy & Zhu, Zhongyi, 2013. "Variable selection in high-dimensional quantile varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 115-132.
    12. Li, Yujie & Li, Gaorong & Lian, Heng & Tong, Tiejun, 2017. "Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 133-150.
    13. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    14. Li, Xinyi & Wang, Li & Nettleton, Dan, 2019. "Sparse model identification and learning for ultra-high-dimensional additive partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 204-228.
    15. Jones, Benjamin A., 2018. "Forest-attacking Invasive Species and Infant Health: Evidence From the Invasive Emerald Ash Borer," Ecological Economics, Elsevier, vol. 154(C), pages 282-293.
    16. Zhaoliang Wang & Liugen Xue & Gaorong Li & Fei Lu, 2019. "Spline estimator for ultra-high dimensional partially linear varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 657-677, June.
    17. Zhang, Ting & Wang, Lei, 2020. "Smoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    18. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
    19. Roberta De Vito & Ruggero Bellio & Lorenzo Trippa & Giovanni Parmigiani, 2019. "Multi‐study factor analysis," Biometrics, The International Biometric Society, vol. 75(1), pages 337-346, March.
    20. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:254-:d:1576268. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.