IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v486y2023ics0304380023002417.html
   My bibliography  Save this article

Phytomass productivity of cutting and grazing grasslands with special reference to small-scale spatial variation in plant nutrient resources

Author

Listed:
  • Chen, Jun
  • Shiyomi, Masae
  • Morita, Satoru

Abstract

Sown grasslands are generally managed via one of two methods: cutting or grazing. In cutting grassland (CG), fields are uniformly plowed, and subsequently, one or several mixed pasture plant species are uniformly seeded, managed, and cut. In grazing grassland (GG), several pasture plant species are uniformly seeded at the establishment stage of grassland, and livestock is introduced to the grassland after a certain volume of vegetation is achieved. In GG, nutrient cycling occurs via grassland vegetation, intake and excretion by animals in grassland, and grassland soil. The objective of this study was to build a mathematical model to investigate which of CG or GG produces more phytomass under an equal level of plant nutrient resources (PNR) such as mineral nitrogen, available phosphorous, potassium in soil and the derivatives in plant bodies. In the model, we assumed that the relationship between PNR and plant production per unit ground area (plot) in CG and GG would follow the Mitscherlich law; the frequency distributions of the PNR level in soil and vegetation per plot would follow a gamma distribution in GG, but a spatially uniform distribution in CG; PNR level in soil of CG would be equal to the mean PNR in soil of GG; and additional PNR in the form of fertilizer would be applied to achieve equal PNR levels between CG and GG ecosystems after cutting and grazing. The model outputs indicated that (1) under conditions in which mean PNR levels were equal between CG and GG, phytomass in GG after grazing was greater than that after cutting in CG if the spatial variation in PNR is not highly heterogeneous in GG, however, smaller than that just before cutting in CG for any spatial heterogeneity in GG; (2) resupply of PNR (i.e., fertilizer) after cutting or grazing practices (to maintain equivalent PNR levels between both grassland ecosystems) produce a higher phytomass in CG compared to GG. The reason is that CG receives much resupply of PNR to supplement losses by natural (such as runoff and infiltration) and artificial (cutting/carrying-out of phytomass) losses, but in GG only the natural loss is resupplied because there is no artificial loss; (3) the high spatial variations in PNR generally play a negative role to determine phytomass production (phytomass productivity, or phytomass yield) in GG; (4) the efficiency of PNR resupply to phytomass production (output/input ratio of PNR) is higher in GG, particularly under the condition with highly spatial variation in PNR, than that in CG.

Suggested Citation

  • Chen, Jun & Shiyomi, Masae & Morita, Satoru, 2023. "Phytomass productivity of cutting and grazing grasslands with special reference to small-scale spatial variation in plant nutrient resources," Ecological Modelling, Elsevier, vol. 486(C).
  • Handle: RePEc:eee:ecomod:v:486:y:2023:i:c:s0304380023002417
    DOI: 10.1016/j.ecolmodel.2023.110511
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023002417
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110511?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. Chen, Jun & Shiyomi, Masae & Hori, Yoshimichi & Yamamura, Yasuo, 2008. "Frequency distribution models for spatial patterns of vegetation abundance," Ecological Modelling, Elsevier, vol. 211(3), pages 403-410.
    2. Shiyomi, Masae & Akiyama, Tsuyoshi & Wang, Shiping & Yiruhan, & Ailikun, & Hori, Yoshimichi & Chen, Zuozhong & Yasuda, Taisuke & Kawamura, Kensuke & Yamamura, Yasuo, 2011. "A grassland ecosystem model of the Xilingol steppe, Inner Mongolia, China," Ecological Modelling, Elsevier, vol. 222(13), pages 2073-2083.
    3. Yiruhan, & Shiyomi, Masae & Akiyama, Tsuyoshi & Wang, Shiping & Yamamura, Yasuo & Hori, Yoshimichi & Ailikun,, 2014. "Long-term prediction of grassland production for five temporal patterns of precipitation during the growing season of plants based on a system model in Xilingol, Inner Mongolia, China," Ecological Modelling, Elsevier, vol. 291(C), pages 183-192.
    4. Zhao, Ying & Peth, Stephan & Krümmelbein, Julia & Horn, Rainer & Wang, Zhongyan & Steffens, Markus & Hoffmann, Carsten & Peng, Xinhua, 2007. "Spatial variability of soil properties affected by grazing intensity in Inner Mongolia grassland," Ecological Modelling, Elsevier, vol. 205(1), pages 241-254.
    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. Wang, Yuhui & Zhou, Guangsheng & Jia, Bingrui, 2008. "Modeling SOC and NPP responses of meadow steppe to different grazing intensities in Northeast China," Ecological Modelling, Elsevier, vol. 217(1), pages 72-78.
    2. Damgaard, Christian, 2008. "Modelling pin-point plant cover data along an environmental gradient," Ecological Modelling, Elsevier, vol. 214(2), pages 404-410.
    3. Guan, Qingqing & Chen, Jun & Wei, Zhicheng & Wang, Yuxia & Shiyomi, Masae & Yang, Yungui, 2016. "Analyzing the spatial heterogeneity of number of plant individuals in grassland community by using power law model," Ecological Modelling, Elsevier, vol. 320(C), pages 316-321.
    4. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.
    5. Qiuan Zhu & Huai Chen & Changhui Peng & Jinxun Liu & Shilong Piao & Jin-Sheng He & Shiping Wang & Xinquan Zhao & Jiang Zhang & Xiuqin Fang & Jiaxin Jin & Qi-En Yang & Liliang Ren & Yanfen Wang, 2023. "An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Jakubowski, Wojciech & Szulczewski, Wiesław & Żyromski, Andrzej & Biniak-Pieróg, Małgorzata, 2016. "The estimation of basket willow (Salix viminalis) yield – New approach, Part II: Theoretical model and its practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 843-851.
    7. Ahmed Ibrahim Ahmed & Lulu Hou & Ruirui Yan & Xiaoping Xin & Yousif Mohamed Zainelabdeen, 2020. "The Joint Effect of Grazing Intensity and Soil Factors on Aboveground Net Primary Production in Hulunber Grasslands Meadow Steppe," Agriculture, MDPI, vol. 10(7), pages 1-19, July.
    8. Moret-Fernández, D. & Pueyo, Y. & Bueno, C.G. & Alados, C.L., 2011. "Hydro-physical responses of gypseous and non-gypseous soils to livestock grazing in a semi-arid region of NE Spain," Agricultural Water Management, Elsevier, vol. 98(12), pages 1822-1827, October.
    9. Saldaña, A. & Ibáñez, J.J., 2007. "Pedodiversity, connectance and spatial variability of soil properties, what is the relationship?," Ecological Modelling, Elsevier, vol. 208(2), pages 342-352.
    10. Jiao Chen & Haiping Tang, 2016. "Effect of Grazing Exclusion on Vegetation Characteristics and Soil Organic Carbon of Leymus chinensis Grassland in Northern China," Sustainability, MDPI, vol. 8(1), pages 1-10, January.
    11. Deepak Singh & Alok Kumar Mishra & Sridhar Patra & Anuj Kumar Dwivedi & Chandra Shekhar Prasad Ojha & Vijay P. Singh & Sankar Mariappan & Subhash Babu & Nisha Singh & Devideen Yadav & Prabhat Ranjan O, 2023. "Effect of Long-Term Tillage Practices on Runoff and Soil Erosion in Sloping Croplands of Himalaya, India," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    12. Yiruhan, & Shiyomi, Masae & Akiyama, Tsuyoshi & Wang, Shiping & Yamamura, Yasuo & Hori, Yoshimichi & Ailikun,, 2014. "Long-term prediction of grassland production for five temporal patterns of precipitation during the growing season of plants based on a system model in Xilingol, Inner Mongolia, China," Ecological Modelling, Elsevier, vol. 291(C), pages 183-192.
    13. Yunqing Hao & Zhengwei He, 2019. "Effects of grazing patterns on grassland biomass and soil environments in China: A meta-analysis," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-15, April.

    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:ecomod:v:486:y:2023:i:c:s0304380023002417. 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.journals.elsevier.com/ecological-modelling .

    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.