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Predicting Cyperus esculentus Biomass Using Tiller Number: A Comparative Analysis of Growth Models

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  • Ya Ding

    (College of Ecology and Environment, Xinjiang University, Urumqi 830011, China
    Xinjiang Key Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Hotan Prefecture 848300, China)

  • Yan Lu

    (Xinjiang Key Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Hotan Prefecture 848300, China)

  • Akash Tariq

    (Xinjiang Key Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Hotan Prefecture 848300, China
    Consejo Superior de Investigaciones Científicas, Global Ecology Unit, Centre de Recerca Ecològica i Aplicacions Forestals-Consejo Superior de Investigaciones Científicas-Universitat Autònoma de Barcelona (CREAF-CSIC-UAB), Bellaterra, 08193 Barcelona, Catalonia, Spain)

  • Fanjiang Zeng

    (College of Ecology and Environment, Xinjiang University, Urumqi 830011, China
    Xinjiang Key Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Hotan Prefecture 848300, China)

  • Yanju Gao

    (College of Ecology and Environment, Xinjiang University, Urumqi 830011, China
    Xinjiang Key Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Hotan Prefecture 848300, China)

  • Jordi Sardans

    (Consejo Superior de Investigaciones Científicas, Global Ecology Unit, Centre de Recerca Ecològica i Aplicacions Forestals-Consejo Superior de Investigaciones Científicas-Universitat Autònoma de Barcelona (CREAF-CSIC-UAB), Bellaterra, 08193 Barcelona, Catalonia, Spain
    Centre de Recerca Ecològica i Aplicacions Forestals, 08193 Cerdanyola del Vallès, Catalonia, Spain)

  • Dhafer A. Al-Bakre

    (Department of Biology, College of Science, University of Tabuk, Tabuk 71421, Saudi Arabia)

  • Josep Peñuelas

    (Consejo Superior de Investigaciones Científicas, Global Ecology Unit, Centre de Recerca Ecològica i Aplicacions Forestals-Consejo Superior de Investigaciones Científicas-Universitat Autònoma de Barcelona (CREAF-CSIC-UAB), Bellaterra, 08193 Barcelona, Catalonia, Spain
    Centre de Recerca Ecològica i Aplicacions Forestals, 08193 Cerdanyola del Vallès, Catalonia, Spain)

Abstract

Cyperus esculentus , a drought-resistant Cyperaceae with ecological and economic value (stems/leaves as feed, tubers as oil source), stabilizes arid soils through its extensive root system. Understanding its biomass allocation strategies is crucial for comprehending carbon storage in arid environments. The results showed that allometric models best described leaf biomass, while Gompertz and logistic models provided superior accuracy (evaluated using R 2 , p -value, AIC, RMSE, and RSS) for estimating root, tuber, and whole plant biomass. In our study, the equilibrium biomass showed that underground (74.29 g and 64.22 g) was superior to aboveground (63.63 g and 58.72 g); and the growth rate showed the same result, underground (0.112 and 0.055) surpassed aboveground (0.083 and 0.046). The initial inflection point (POI1 = 11) suggests that leaves are prioritized in acquiring limited resources to support growth. In conclusion, the tiller number is a reliable predictor for developing robust biomass models for C. esculentus . The Gompertz model is best for leaves, roots, and total biomass, while the logistic model is optimal for predicting tuber biomass in arid areas. The tiller number is a reliable predictor for developing robust biomass models for C. esculentus . The research findings have supplied useful insights into the growth modifications, production potential, and management experience gained from Cyperus esculentus plant agriculture.

Suggested Citation

  • Ya Ding & Yan Lu & Akash Tariq & Fanjiang Zeng & Yanju Gao & Jordi Sardans & Dhafer A. Al-Bakre & Josep Peñuelas, 2025. "Predicting Cyperus esculentus Biomass Using Tiller Number: A Comparative Analysis of Growth Models," Agriculture, MDPI, vol. 15(9), pages 1-17, April.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:9:p:946-:d:1643604
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    References listed on IDEAS

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    1. Nguimkeu, Pierre, 2014. "A simple selection test between the Gompertz and Logistic growth models," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 98-105.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. J. Křen & K. Klem & I. Svobodová & P. Míša & L. Neudert, 2014. "Yield and grain quality of spring barley as affected by biomass formation at early growth stages," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 60(5), pages 221-227.
    4. Cláudia Bem & Alberto Cargnelutti Filho & Gabriela Chaves & Jéssica Kleinpaul & Rafael Pezzini & André Lavezo, 2017. "Gompertz and Logistic Models to the Productive Traits of Sunn Hemp," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 10(1), pages 225-225, December.
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