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Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models

Author

Listed:
  • I. Tsakmakis

    (Democritus University of Thrace)

  • N. Kokkos

    (Democritus University of Thrace)

  • V. Pisinaras

    (Democritus University of Thrace)

  • V. Papaevangelou

    (Democritus University of Thrace)

  • E. Hatzigiannakis

    (Soil and Water Resources Institute)

  • G. Arampatzis

    (Soil and Water Resources Institute)

  • G.D. Gikas

    (Democritus University of Thrace)

  • R. Linker

    (Israel Institute of Technology)

  • S. Zoras

    (Democritus University of Thrace)

  • V. Evagelopoulos

    (Democritus University of Thrace)

  • V.A. Tsihrintzis

    (National Technical University of Athens)

  • A. Battilani

    (Consorzio di Bonifica di secondo grado per il Canale Emiliano Romagnolo CER)

  • G. Sylaios

    (Democritus University of Thrace)

Abstract

In this paper, we tested the operational capacity of an interoperable model coupling system for the irrigation scheduling (IMCIS) at an experimental cotton (Gossypium hirsutum L.) field in Northern Greece. IMCIS comprises a meteorological model (TAPM), downscaled at field level, and a water-driven cultivation tool (AquaCrop), to optimize irrigation and enhance crop growth and yield. Both models were evaluated through on-site observations of meteorological variables, soil moisture levels and canopy cover progress. Based on irrigation management (deficit, precise and farmer’s practice) and method (drip and sprinkler), the field was divided into six sub-plots. Prognostic meteorological model results exhibited satisfactory agreement in most parameters affecting ETo, simulating adequately the soil water balance. Precipitation events were fairly predicted, although rainfall depths needed further adjustment. Soil water content levels computed by the crop growth model followed the trend of soil humidity measurements, while the canopy cover patterns and the seed cotton yield were well predicted, especially at the drip irrigated plots. Overall, the system exhibited robustness and good predicting ability for crop water needs, based on local evapotranspiration forecasts and crop phenological stages. The comparison of yield and irrigation levels at all sub-plots revealed that drip irrigation under IMCIS guidance could achieve the same yield levels as traditional farmer’s practice, utilizing approximately 32% less water, thus raising water productivity up to 0.96 kg/m3.

Suggested Citation

  • I. Tsakmakis & N. Kokkos & V. Pisinaras & V. Papaevangelou & E. Hatzigiannakis & G. Arampatzis & G.D. Gikas & R. Linker & S. Zoras & V. Evagelopoulos & V.A. Tsihrintzis & A. Battilani & G. Sylaios, 2017. "Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 563-580, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1548-7
    DOI: 10.1007/s11269-016-1548-7
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    References listed on IDEAS

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    1. R. Perea & E. Poyato & P. Montesinos & J. Díaz, 2015. "Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5551-5567, December.
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    Cited by:

    1. Ellenburg, W. Lee & Miller, Sara E. & Mishra, Vikalp & Ndungu, Lilian & Adams, Emily & Das, Narendra & Andreadis, Konstantinos M. & Limaye, Ashutosh, 2024. "Evaluation of a regional crop model implementation for sub-national yield assessments in Kenya," Agricultural Systems, Elsevier, vol. 214(C).
    2. Vassilios Pisinaras & Frank Herrmann & Andreas Panagopoulos & Evangelos Tziritis & Ian McNamara & Frank Wendland, 2023. "Fully Distributed Water Balance Modelling in Large Agricultural Areas—The Pinios River Basin (Greece) Case Study," Sustainability, MDPI, vol. 15(5), pages 1-29, February.
    3. Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    4. Hafiz Shahzad Ahmad & Muhammad Imran & Fiaz Ahmad & Shah Rukh & Rao Muhammad Ikram & Hafiz Muhammad Rafique & Zafar Iqbal & Abdulaziz Abdullah Alsahli & Mohammed Nasser Alyemeni & Shafaqat Ali & Tanve, 2021. "Improving Water Use Efficiency through Reduced Irrigation for Sustainable Cotton Production," Sustainability, MDPI, vol. 13(7), pages 1-12, April.
    5. Tsakmakis, I.D. & Kokkos, N.P. & Gikas, G.D. & Pisinaras, V. & Hatzigiannakis, E. & Arampatzis, G. & Sylaios, G.K., 2019. "Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns," Agricultural Water Management, Elsevier, vol. 213(C), pages 419-432.
    6. Iphigenia Keramitsoglou & Panagiotis Sismanidis & Olga Sykioti & Vassilios Pisinaras & Ioannis Tsakmakis & Andreas Panagopoulos & Argyrios Argyriou & Chris T. Kiranoudis, 2023. "SENSE-GDD: A Satellite-Derived Temperature Monitoring Service to Provide Growing Degree Days," Agriculture, MDPI, vol. 13(5), pages 1-13, May.

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