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Dynamic Management Zones for Irrigation Scheduling

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  • Fontanet, Mireia
  • Scudiero, Elia
  • Skaggs, Todd H.
  • Fernàndez-Garcia, Daniel
  • Ferrer, Francesc
  • Rodrigo, Gema
  • Bellvert, Joaquim

Abstract

Irrigation scheduling decision-support tools can improve water use efficiency by matching irrigation recommendations to prevailing soil and crop conditions within a season. Yet, little research is available on how to support real-time precision irrigation that varies within-season in both time and space. We investigate the integration of remotely sensed NDVI time-series, soil moisture sensor measurements, and root zone simulation forecasts for in-season delineation of dynamic management zones (MZ) and for a variable rate irrigation scheduling in order to improve irrigation scheduling and crop performance. Delineation of MZ was conducted in a 5.8-ha maize field during 2018 using Sentinel-2 NDVI time-series and an unsupervised classification. The number and spatial extent of MZs changed through the growing season. A network of soil moisture sensors was used to interpret spatiotemporal changes of the NDVI. Soil water content was a significant contributor to changes in crop vigor across MZs through the growing season. Real-time cluster validity function analysis provided in-season evaluation of the MZ design. For example, the total within-MZ daily soil moisture relative variance decreased from 85% (early vegetative stages) to below 25% (late reproductive stages). Finally, using the Hydrus-1D model, a workflow for in-season optimization of irrigation scheduling and water delivery management was tested. Data simulations indicated that crop transpiration could be optimized while reducing water applications between 11 and 28.5% across the dynamic MZs. The proposed integration of spatiotemporal crop and soil moisture data can be used to support management decisions to effectively control outputs of crop × environment × management interactions.

Suggested Citation

  • Fontanet, Mireia & Scudiero, Elia & Skaggs, Todd H. & Fernàndez-Garcia, Daniel & Ferrer, Francesc & Rodrigo, Gema & Bellvert, Joaquim, 2020. "Dynamic Management Zones for Irrigation Scheduling," Agricultural Water Management, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:agiwat:v:238:y:2020:i:c:s0378377419319821
    DOI: 10.1016/j.agwat.2020.106207
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    References listed on IDEAS

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    1. Autovino, Dario & Rallo, Giovanni & Provenzano, Giuseppe, 2018. "Predicting soil and plant water status dynamic in olive orchards under different irrigation systems with Hydrus-2D: Model performance and scenario analysis," Agricultural Water Management, Elsevier, vol. 203(C), pages 225-235.
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    1. Hongbo Wang & Hui Cao & Fuchang Jiang & Xingpeng Wang & Yang Gao, 2022. "Analysis of Soil Moisture, Temperature, and Salinity in Cotton Field under Non-Mulched Drip Irrigation in South Xinjiang," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    2. Kumar, Hemendra & Srivastava, Puneet & Lamba, Jasmeet & Diamantopoulos, Efstathios & Ortiz, Brenda & Morata, Guilherme & Takhellambam, Bijoychandra & Bondesan, Luca, 2022. "Site-specific irrigation scheduling using one-layer soil hydraulic properties and inverse modeling," Agricultural Water Management, Elsevier, vol. 273(C).
    3. Kumar, Hemendra & Srivastava, Puneet & Lamba, Jasmeet & Lena, Bruno & Diamantopoulos, Efstathios & Ortiz, Brenda & Takhellambam, Bijoychandra & Morata, Guilherme & Bondesan, Luca, 2023. "A methodology to optimize site-specific field capacity and irrigation thresholds," Agricultural Water Management, Elsevier, vol. 286(C).

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