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Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton

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  • O'Shaughnessy, S.A.
  • Evett, S.R.
  • Colaizzi, P.D.
  • Howell, T.A.

Abstract

The use of digital infrared thermography and thermometry to investigate early crop water stress offers a producer improved management tools to avoid yield declines or to deal with variability in crop water status. This study used canopy temperature data to investigate whether an empirical crop water stress index could be used to monitor spatial and temporal crop water stress. Different irrigation treatment amounts (100%, 67%, 33%, and 0% of full replenishment of soil water to field capacity to a depth of 1.5Â m) were applied by a center pivot system to soybean (Glycine max L.) in 2004 and 2005, and to cotton (Gossypium hirsutum L.) in 2007 and 2008. Canopy temperature data from infrared thermography were used to benchmark the relationship between an empirical crop water stress index (CWSIe) and leaf water potential ([Psi]L) across a block of eight treatment plots (of two replications). There was a significant negative linear correlation between midday [Psi]L measurements and the CWSIe after soil water differences due to irrigation treatments were well established and during the absence of heavy rainfall. Average seasonal CWSIe values calculated for each plot from temperature measurements made by infrared thermometer thermocouples mounted on a center pivot lateral were inversely related to crop water use with r2 values >0.89 and 0.55 for soybean and cotton, respectively. There was also a significant inverse relationship between the CWSIe and soybean yields in 2004 (r2Â =Â 0.88) and 2005 (r2Â =Â 0.83), and cotton in 2007 (r2Â =Â 0.78). The correlations were not significant in 2008 for cotton. Contour plots of the CWSIe may be used as maps to indicate the spatial variability of within-field crop water stress. These maps may be useful for irrigation scheduling or identifying areas within a field where water stress may impact crop water use and yield.

Suggested Citation

  • O'Shaughnessy, S.A. & Evett, S.R. & Colaizzi, P.D. & Howell, T.A., 2011. "Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton," Agricultural Water Management, Elsevier, vol. 98(10), pages 1523-1535, August.
    • Handle: RePEc:eee:agiwat:v:98:y:2011:i:10:p:1523-1535
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    References listed on IDEAS

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    1. O'Shaughnessy, S.A. & Evett, S.R., 2010. "Canopy temperature based system effectively schedules and controls center pivot irrigation of cotton," Agricultural Water Management, Elsevier, vol. 97(9), pages 1310-1316, September.
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    2. Candogan, Burak Nazmi & Sincik, Mehmet & Buyukcangaz, Hakan & Demirtas, Cigdem & Goksoy, Abdurrahim Tanju & Yazgan, Senih, 2013. "Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions," Agricultural Water Management, Elsevier, vol. 118(C), pages 113-121.
    3. Fan, Yubing & Himanshu, Sushil K. & Ale, Srinivasulu & DeLaune, Paul B. & Zhang, Tian & Park, Seong C. & Colaizzi, Paul D. & Evett, Steven R. & Baumhardt, R. Louis, 2022. "The synergy between water conservation and economic profitability of adopting alternative irrigation systems for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 262(C).
    4. King, B.A. & Tarkalson, D.D. & Sharma, V. & Bjorneberg, D.L., 2021. "Thermal Crop Water Stress Index Base Line Temperatures for Sugarbeet in Arid Western U.S," Agricultural Water Management, Elsevier, vol. 243(C).
    5. Krista C. Shellie & Bradley A. King, 2020. "Application of a Daily Crop Water Stress Index to Deficit Irrigate Malbec Grapevine under Semi-Arid Conditions," Agriculture, MDPI, vol. 10(11), pages 1-17, October.
    6. Colaizzi, Paul D. & O’Shaughnessy, Susan A. & Evett, Steve R. & Mounce, Ryan B., 2017. "Crop evapotranspiration calculation using infrared thermometers aboard center pivots," Agricultural Water Management, Elsevier, vol. 187(C), pages 173-189.
    7. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
    8. King, B.A. & Shellie, K.C., 2016. "Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index," Agricultural Water Management, Elsevier, vol. 167(C), pages 38-52.
    9. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    10. 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).
    11. Garibay, Victoria M. & Kothari, Kritika & Ale, Srinivasulu & Gitz, Dennis C. & Morgan, Gaylon D. & Munster, Clyde L., 2019. "Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    12. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.

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