IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i10p1675-d940125.html
   My bibliography  Save this article

Soil Compaction Drives an Intra-Genotype Leaf Economics Spectrum in Wine Grapes

Author

Listed:
  • Adam R. Martin

    (Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada)

  • Rachel O. Mariani

    (Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada)

  • Kimberley A. Cathline

    (Agriculture & Environmental Technologies Innovation Centre, Niagara College, Niagara-on-the-Lake, ON L0S 1J0, Canada)

  • Michael Duncan

    (Agriculture & Environmental Technologies Innovation Centre, Niagara College, Niagara-on-the-Lake, ON L0S 1J0, Canada)

  • Nicholas J. Paroshy

    (Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada)

  • Gavin Robertson

    (Agriculture & Environmental Technologies Innovation Centre, Niagara College, Niagara-on-the-Lake, ON L0S 1J0, Canada)

Abstract

Intraspecific trait variation is a critical determinant of ecosystem processes, especially in agroecosystems where single species or genotypes exist in very high abundance. Yet to date, only a small number of studies have evaluated if, how, or why traits forming the Leaf Economics Spectrum (LES) vary within crops, despite such studies informing our understanding of: (1) the environmental factors that drive crop LES trait variation and (2) how domestication has altered LES traits in crops vs. wild plants. We assess intragenotype variation in LES traits in ‘Chardonnay’ ( Vitis vinifera )—one of the world’s most commercially important crops—across a soil compaction gradient: one of the most prominent characteristics of agricultural soils that may drive crop trait variation. Our early evidence indicates that ‘Chardonnay’ traits covary along an intragenotype LES in patterns that are qualitatively similar to those observed among wild plants: resource-acquiring vines expressed a combination of high mass-based photosynthesis ( A mass ), mass-based dark respiration ( R mass ), and leaf nitrogen concentrations (N), coupled with low leaf mass per area (LMA); the opposite set of trait values defined the resource-conserving end of the ‘Chardonnay’ LES. Traits reflecting resource acquisition strategies ( A mass , R mass , and leaf N) declined with greater bulk density, while traits related to investment in leaf construction costs (LMA) increased with greater bulk density. Our findings contribute to an understanding of the domestication syndrome in grapevines and also provide information relevant for quantifying trait-based crop responses to environmental change and gradients.

Suggested Citation

  • Adam R. Martin & Rachel O. Mariani & Kimberley A. Cathline & Michael Duncan & Nicholas J. Paroshy & Gavin Robertson, 2022. "Soil Compaction Drives an Intra-Genotype Leaf Economics Spectrum in Wine Grapes," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1675-:d:940125
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/10/1675/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/10/1675/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. H. J. D. Thomas & A. D. Bjorkman & I. H. Myers-Smith & S. C. Elmendorf & J. Kattge & S. Diaz & M. Vellend & D. Blok & J. H. C. Cornelissen & B. C. Forbes & G. H. R. Henry & R. D. Hollister & S. Norman, 2020. "Global plant trait relationships extend to the climatic extremes of the tundra biome," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    2. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
    3. Ian J. Wright & Peter B. Reich & Mark Westoby & David D. Ackerly & Zdravko Baruch & Frans Bongers & Jeannine Cavender-Bares & Terry Chapin & Johannes H. C. Cornelissen & Matthias Diemer & Jaume Flexas, 2004. "The worldwide leaf economics spectrum," Nature, Nature, vol. 428(6985), pages 821-827, April.
    4. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    5. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
    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. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    2. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    3. K. G. Reddy & M. G. M. Khan, 2020. "stratifyR: An R Package for optimal stratification and sample allocation for univariate populations," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 383-405, September.
    4. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    5. Riva-Palacio, Alan & Leisen, Fabrizio, 2021. "Compound vectors of subordinators and their associated positive Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    6. Minji Lee & Sun Ju Chung & Youngjo Lee & Sera Park & Jun-Gun Kwon & Dai Jin Kim & Donghwan Lee & Jung-Seok Choi, 2020. "Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-12, August.
    7. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    8. Daijun Liu & Adriane Esquivel-Muelbert & Nezha Acil & Julen Astigarraga & Emil Cienciala & Jonas Fridman & Georges Kunstler & Thomas J. Matthews & Paloma Ruiz-Benito & Jonathan P. Sadler & Mart-Jan Sc, 2024. "Mapping multi-dimensional variability in water stress strategies across temperate forests," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Phillip M. Gurman & Tom Ross & Andreas Kiermeier, 2018. "Quantitative Microbial Risk Assessment of Salmonellosis from the Consumption of Australian Pork: Minced Meat from Retail to Burgers Prepared and Consumed at Home," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2625-2645, December.
    10. Héctor Nájera & David Gordon, 2023. "A Monte Carlo Study of Some Empirical Methods to Find the Optimal Poverty Line in Multidimensional Poverty Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 391-419, June.
    11. Athanasios Zisos & Georgia-Konstantina Sakki & Andreas Efstratiadis, 2023. "Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    12. Valentas Gruzauskas & Aurelija Burinskiene & Andrius Krisciunas, 2023. "Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    13. Amanda M. Wilson & Kelly A. Reynolds & Marc P. Verhougstraete & Robert A. Canales, 2019. "Validation of a Stochastic Discrete Event Model Predicting Virus Concentration on Nurse Hands," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1812-1824, August.
    14. Wang, Xialin & Nuppenau, Ernst-August, 2021. "Modelling payments for ecosystem services for solving future water conflicts at spatial scales: The Okavango River Basin example," Ecological Economics, Elsevier, vol. 184(C).
    15. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    16. Kun Mo Lee & Min Hyeok Lee & Jong Seok Lee & Joo Young Lee, 2020. "Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method," Energies, MDPI, vol. 13(18), pages 1-15, September.
    17. Alexander Webb & Pramesh Kumar & Alireza Khani, 2020. "Estimation of passenger waiting time using automatically collected transit data," Public Transport, Springer, vol. 12(2), pages 299-311, June.
    18. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    19. Bejarano, Adriana C. & Wells, Randall S. & Costa, Daniel P., 2017. "Development of a bioenergetic model for estimating energy requirements and prey biomass consumption of the bottlenose dolphin Tursiops truncatus," Ecological Modelling, Elsevier, vol. 356(C), pages 162-172.
    20. Negahban, Ashkan, 2019. "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 317-332.

    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:12:y:2022:i:10:p:1675-:d:940125. 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.