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

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 and G. Sylaios ()
Additional contact information
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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 1, No 35, 563-580

Abstract: 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.

Keywords: Irrigation scheduling; Precise irrigation; Meteorological model; Crop growth model; TAPM; AquaCrop (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-016-1548-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1548-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-016-1548-7

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().