IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v259y2022ics0378377421005436.html
   My bibliography  Save this article

A lucerne-digit grass pasture offers herbage production and rainwater productivity equal to a digit grass pasture fertilized with applied nitrogen

Author

Listed:
  • Murphy, Sean R.
  • Boschma, Suzanne P.
  • Harden, Steven

Abstract

In recent years, livestock producers have widely sown tropical perennial grasses, such as digit grass (Digitaria eriantha), in the frost-prone summer dominant rainfall zone of eastern Australia. Tropical grasses require substantial nitrogen inputs to maintain water productivity and forage quality. Perennial legumes offer a potential nitrogen source for these perennial grasses when sown in a mixed sward. Lucerne (Medicago sativa) is the perennial legume most widely sown in grazing systems in south-eastern Australia. In this study, conducted over the period 2014–2018, we compared the soil water dynamics and rainwater productivity of pure stands of digit grass (fertilized with applied nitrogen), lucerne, desmanthus (Desmanthus virgatus) and leucaena (Leucaena leucocephala) and binary mixtures of digit grass (not fertilized with applied nitrogen) with each legume. We found that often growing season actual evapotranspiration (ETa) was similar among the treatments (P > 0.05), but rainwater productivity (kg DM/ha.mm) and proportion (%) of legume herbage mass were not (P < 0.05). Our results showed that the lucerne-digit grass mix was equally productive and efficient as fertilized digit grass (P > 0.05), particularly in the latter three seasons. However, lucerne dominated the herbage mass (c. > 67% legume). Leucaena production was delayed by frost and severely impacted by competition with digit grass, and generally underperformed both lucerne and desmanthus. This observation did always not hold when alfalfa mosaic virus (AMV) impacted desmanthus in specific seasons. Our experiment has shown that a mixed sward of lucerne-digit grass is highly productive and offered high rainwater productivity. Both desmanthus and leucaena, in mixes with digit grass, provided useful contributions of legume herbage mass, in specific seasons and under specific conditions, but both underperformed overall compared with lucerne.

Suggested Citation

  • Murphy, Sean R. & Boschma, Suzanne P. & Harden, Steven, 2022. "A lucerne-digit grass pasture offers herbage production and rainwater productivity equal to a digit grass pasture fertilized with applied nitrogen," Agricultural Water Management, Elsevier, vol. 259(C).
  • Handle: RePEc:eee:agiwat:v:259:y:2022:i:c:s0378377421005436
    DOI: 10.1016/j.agwat.2021.107266
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377421005436
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2021.107266?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Arũnas P. Verbyla & Brian R. Cullis & Michael G. Kenward & Sue J. Welham, 1999. "The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 269-311.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aliasghar Montazar & Daniel Putnam, 2023. "Evapotranspiration and Yield Impact Tools for More Water-Use Efficient Alfalfa Production in Desert Environments," Agriculture, MDPI, vol. 13(11), pages 1-21, November.

    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. Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Welham, S.J. & Thompson, R., 2009. "A note on bimodality in the log-likelihood function for penalized spline mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 920-931, February.
    3. Woojoo Lee & Hans‐Peter Piepho & Youngjo Lee, 2021. "Resolving the ambiguity of random‐effects models with singular precision matrix," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 482-499, November.
    4. Ruixue Du & Hiroshi Yamada, 2020. "Principle of Duality in Cubic Smoothing Spline," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    5. Beran, Jan & Liu, Haiyan, 2016. "Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 218-233.
    6. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.
    7. Nicholas Longford, 2014. "On the inefficiency of the restricted maximum likelihood," Economics Working Papers 1415, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    9. M. P. Wand, 2003. "Smoothing and mixed models," Computational Statistics, Springer, vol. 18(2), pages 223-249, July.
    10. Laurini, Fabrizio & Pauli, Francesco, 2009. "Smoothing sample extremes: The mixed model approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3842-3854, September.
    11. Xiaojun Mao & Somak Dutta & Raymond K. W. Wong & Dan Nettleton, 2020. "Adjusting for Spatial Effects in Genomic Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 699-718, December.
    12. Faustin Habyarimana & Shaun Ramroop, 2015. "Determinants of Poverty of Households: Semi parametric Analysis of Demographic and Health Survey Data from Rwanda," Journal of Economics and Behavioral Studies, AMH International, vol. 7(3), pages 47-55.
    13. Kuparinen, Anna & Björklund, Mats, 2011. "Theory put into practice: An R implementation of the infinite-dimensional model," Ecological Modelling, Elsevier, vol. 222(12), pages 2027-2030.
    14. Lihui Zhao & Tom Chen & Vladimir Novitsky & Rui Wang, 2021. "Joint penalized spline modeling of multivariate longitudinal data, with application to HIV‐1 RNA load levels and CD4 cell counts," Biometrics, The International Biometric Society, vol. 77(3), pages 1061-1074, September.
    15. Davies, Lloyd & Quinn, Helen & Della Bosca, Tony & Alford, Andrew & Griffith, Garry, 2009. "The Economic Effects of Alternate Growth Path and Breed Type Combinations to Meet Beef Market Specifications across Southern Australia," Research Reports 280782, New South Wales Department of Primary Industries Research Economists.
    16. Jan Serroyen & Geert Molenberghs & Marc Aerts & Ellen Vloeberghs & Peter Paul De Deyn & Geert Verbeke, 2010. "Flexible estimation of serial correlation in nonlinear mixed models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 833-846.
    17. Yao, Fang, 2007. "Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 40-56, January.
    18. Arūnas P. Verbyla & Joanne Faveri & John D. Wilkie & Tom Lewis, 2018. "Tensor Cubic Smoothing Splines in Designed Experiments Requiring Residual Modelling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 478-508, December.
    19. Leontine Alkema & Jin Rou New & Jon Pedersen & Danzhen You & all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group, 2014. "Child Mortality Estimation 2013: An Overview of Updates in Estimation Methods by the United Nations Inter-Agency Group for Child Mortality Estimation," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    20. Lee, Dae-Jin & Durbán, María, 2008. "Smooth-car mixed models for spatial count data," DES - Working Papers. Statistics and Econometrics. WS ws085820, Universidad Carlos III de Madrid. Departamento de Estadística.

    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:eee:agiwat:v:259:y:2022:i:c:s0378377421005436. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.