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Dynamic prescription maps for site-specific variable rate irrigation of cotton

Author

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  • O’Shaughnessy, Susan A.
  • Evett, Steven R.
  • Colaizzi, Paul D.

Abstract

A prescription map is a set of instructions that controls a variable rate irrigation (VRI) system. These maps, which may be based on prior yield, soil texture, topography, or soil electrical conductivity data, are often manually applied at the beginning of an irrigation season and remain static. The problem with static prescription maps is that they ignore spatiotemporal changes in crop water status. In a two-year study (2012 and 2013), a plant feedback system, including a wireless sensor network of infrared thermometers (IRTs), was used to develop dynamic prescription maps to accomplish adaptive irrigation scheduling for cotton (Gossypium hirsutum L.). One-half of a center pivot field was divided into manually and plant feedback-controlled irrigation treatment plots. Irrigation treatments were at three levels, 75, 50 and 25 percent of full as defined by either replenishment of crop water use to field capacity or by the equivalent threshold of the IRT sensed crop water stress. The system accepted user input to control irrigation for the manual treatment plots (I75M, I50M, and I25M), and calculated and compared a thermal stress index for each plant feedback-controlled treatment plot (I75C, I50C and I25C) with a pre-determined threshold for automated irrigation scheduling. The effectiveness of the plant feedback irrigation scheduling system was evaluated by comparing measured lint yield, crop water use (ETc), and water use efficiency (WUE) with the manually scheduled treatment plots. Results for both years indicated that average lint yields were similar between the manual and plant feedback-control plots at the I75 level (181 and 182gm−2, respectively, in 2012; 115 and 103gm−2, respectively, in 2013) and I50 level (146 and 164gm−2, respectively, in 2012; 95 and 117gm−2, respectively, in 2013). At the I25 level, average lint yield was significantly greater for the plant feedback-compared with the manual-control treatment plots (142gm−2 and 92gm−2, respectively), but the mean amount of irrigation was twice that of the manual-control plots. Mean water use efficiencies (WUE) within the same irrigation treatment levels were similar between methods. Importantly, the automatic plant feedback system did not require the time consuming and expensive manual reading of neutron probe access tubes that was required to schedule the manual treatments. These results demonstrate that the integration of a plant feedback system with a commercial VRI system could be used to control site-specific irrigation management for cotton at higher irrigation treatment levels, i.e., I75 percent and I50 percent of full. Such a system can facilitate the use of a VRI system by automating prescription map coding and providing dynamic irrigation control instructions to meet variable crop water needs throughout the irrigation season. As of yet, further research is required to maintain automatic deficit irrigation at a level equivalent to 25 percent replenishment of crop water use relative to field capacity.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:159:y:2015:i:c:p:123-138
    DOI: 10.1016/j.agwat.2015.06.001
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    References listed on IDEAS

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    4. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    5. Li, Xiumei & Zhao, Weixia & Li, Jiusheng & Li, Yanfeng, 2019. "Maximizing water productivity of winter wheat by managing zones of variable rate irrigation at different deficit levels," Agricultural Water Management, Elsevier, vol. 216(C), pages 153-163.
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    7. 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.
    8. 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).
    9. O’Shaughnessy, Susan A. & Kim, Minyoung & Andrade, Manuel A. & Colaizzi, Paul D. & Evett, Steven R., 2020. "Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains," Agricultural Water Management, Elsevier, vol. 240(C).
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    11. Xiaoping Chen & Shaoyuan Feng & Zhiming Qi & Matthew W. Sima & Fanjiang Zeng & Lanhai Li & Haomiao Cheng & Hao Wu, 2022. "Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2," Agriculture, MDPI, vol. 12(3), pages 1-15, March.
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