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Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies

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  • García-Tejero, I.F.
  • Rubio, A.E.
  • Viñuela, I.
  • Hernández, A
  • Gutiérrez-Gordillo, S
  • Rodríguez-Pleguezuelo, C.R.
  • Durán-Zuazo, V.H.

Abstract

Almond (Prunnus dulcis Mill.) has been traditionally associated to marginal land cultivation and rain-fed agriculture in South Spain. However, in the last years, this crop is being progressively introduced in more productive agricultural areas within the Guadalquivir river basin, where the available water resources are not enough to satisfy the adequate crop-water requirements. Considering this limitation, a more precise irrigation scheduling to maximize the yield is required. Infrared thermal imaging emerges as alternative to other traditional methodologies to assess the crop-water status, especially when deficit irrigation (DI) strategies are being applied. The aim of this study was to define the methodology to assess the almond water status by means of thermal information. The trial was conducted during 2014, during the kernel-filling period, in an almond experimental orchard (SW Spain), with 5-year-old trees, subjected to three irrigation regimes: i) a full-irrigation treatment (C-100), which received 100% of ETC; ii) a regulated deficit irrigation (RDI-50), which received 100% of ETC except during the kernel filling period, when this treatment was irrigated with 50% of ETC; iii) and a low-frequency deficit irrigation treatment (LFDI), which received 100% of ETC except during the kernel filling period, when it was subjected to continuous periods of irrigation-restriction, defined in terms of the threshold values of shaded leaf water potential (Ψleaf). Three daily curves of canopy temperature (TC), stomatal conductance to water vapour (gs) and Ψleaf with measurements at 8:00, 11:00, 14:00, 17:00 and 20:00 were developed. Additionally, Crop Water Stress Index (CWSI), temperature difference between canopy and the surrounding air (ΔTcanopy-air), and the relative index to stomatal conductance (IG) obtained at different scales (canopy and row) were estimated. Significant correlations of infrared thermal information vs. Ψleaf and gs were obtained (p ≤ 0.05 and p ≤ 0.01), in particular, by using the thermal readings taken at 11:30, 14:30 at 17:30 h, especially robust were the relationships obtained between TC and CWSI with Ψleaf at 11:30 h; and between TC and CWSI with gs, and Ψleaf at 14:30 h. Finally, considering the infrared thermal monitoring procedure (readings at tree and row level), similar values of TC were obtained, and therefore, the images taken at row level offered a better information with a higher feasibility in terms of image processing.

Suggested Citation

  • García-Tejero, I.F. & Rubio, A.E. & Viñuela, I. & Hernández, A & Gutiérrez-Gordillo, S & Rodríguez-Pleguezuelo, C.R. & Durán-Zuazo, V.H., 2018. "Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 208(C), pages 176-186.
    • Handle: RePEc:eee:agiwat:v:208:y:2018:i:c:p:176-186
    DOI: 10.1016/j.agwat.2018.06.002
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    2. Deng, Juntao & Pan, Shijia & Zhou, Mingu & Gao, Wen & Yan, Yuncai & Niu, Zijie & Han, Wenting, 2023. "Optimum sampling window size and vegetation index selection for low-altitude multispectral estimation of root soil moisture content for Xuxiang Kiwifruit," Agricultural Water Management, Elsevier, vol. 282(C).
    3. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    4. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2022. "Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images," Agricultural Water Management, Elsevier, vol. 264(C).

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