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Remote sensing and soil moisture data for water productivity determination

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  • Chiraz, MASMOUDI CHARFI
  • Olfa, MARRAKCHI
  • Hamadi, HABAIEB

Abstract

This work was initiated in 2019 within the FAO WEPS project framework (2018–2022), aiming to determine the Water Productivity (WP) of the Tunisian olive orchards combining two scales of measurements for soil moisture determination: one for the global analysis (remote sensing) and the other for the field scale application. The experiment was made in 2019 and 2020 in the Centre of Tunisia (Kairouan city, locality of Elhouereb), which is one of the largest irrigation area of Tunisia. WP was computed regarding the actual crop evapotranspiration (ETa) which was determined following the water balance method as: ETa=R+I–ΔS, where ΔS is the amount of water stored in the soil (0–60 cm) and (R) and (I) are the amounts of the seasonal rainfall and the irrigation volume, respectively. The annual ETa reached 293 mm for the rainfed orchard, 823 mm for the semi-intensive irrigated plantation and 819 mm for the intensive fertigated orchard. The WP defined as being the ratio of Yield (kg olives/tree)/ETa ranged between 0.29 kg/m3 (rainfed) and 0.77 kg/m3 (fertigated) depending on the cultivation system. Soil moisture was found to be correlated to the average backscatter coefficient (r = −0.34 for the rainfed and 0.91 for the irrigated orchards) which was determined from the radar images which were treated by using different applications (SNAP, ODK, QGIS.), This result shows the performance of the remote sensing tools for such application. To conclude: WP is at least two times higher in the irrigated orchards than in the rainfed grove; this indicates a great potential of WP improvement if the horticultural practices are well applied in the orchad (pruning, irrigation); so that, the WP can be used as a decision-making-tool for farmers and makers in the future.

Suggested Citation

  • Chiraz, MASMOUDI CHARFI & Olfa, MARRAKCHI & Hamadi, HABAIEB, 2022. "Remote sensing and soil moisture data for water productivity determination," Agricultural Water Management, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:agiwat:v:263:y:2022:i:c:s0378377422000294
    DOI: 10.1016/j.agwat.2022.107482
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    References listed on IDEAS

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