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Optimizing cotton irrigation and nitrogen management using a soil water balance model and in-season nitrogen applications

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  • Zurweller, B.A.
  • Rowland, D.L.
  • Mulvaney, M.J.
  • Tillman, B.L.
  • Migliaccio, K.
  • Wright, D.
  • Erickson, J.
  • Payton, P.
  • Vellidis, G.

Abstract

Nitrogen (N) and irrigation can be two of the costliest management inputs in United States (U.S.) cotton (Gossypium hirsutum L.) production systems. Furthermore, input amounts are often dependent on yearly environmental conditions, making it challenging to optimize N and irrigation management. The objectives of this research were to determine optimal N and irrigation management by: (i) evaluating equally split in-season N rates applied at first square and bloom in combination with varying levels of plant available water replacement (PAWR) estimated by an ET-based soil water balance model for cotton; and (ii) evaluate the whole-plant responses to the interaction between N and water management levels. Field experiments were conducted during 2015 and 2016 at two locations in Florida (Jay and Citra), southeastern U.S. with differing soil textures consisting of a deep sand and sandy loam. Irrigation treatments consisted of: (i) 100% of PAWR (100%); (ii) a primed acclimation (PA) treatment consisting of 50% of PAWR until first bloom and then 100% of PAWR (50%PA); (iii) 50% PAWR for the entire season (50%); (iv) a rain-fed control (RF). Nitrogen treatments consisted of even application splits at first square and bloom applied at a rate of 0 (N0), 22 (N22), 34 (N34), and 45 kg N ha−1 (N45). Lint yield assessments were conducted at both locations in both years. To link yield responses to possible physiological responses, in depth crop measurements consisting of SPAD chlorophyll content, leaf area index (LAI), N uptake, and harvest index (HI) were conducted at the Citra site. Contrasting soil textures and weather conditions between research locations and years allowed for a comprehensive assessment of both N and irrigation management across varying environmental conditions. Lint yield was either increased or at least maintained during the two years of this research at the Citra location by making two split N applications at first square and bloom of N22; while the optimal N treatment at the Jay location was N34 at first square and bloom in both years of this research. Additionally, a yield reduction occurred in the dry year of 2016 at the Citra location when N45 was applied at first square and bloom. The most efficient irrigation strategy at the Citra location was the primed acclimation treatment. At the Jay location, the RF control had similar lint yields as the irrigated treatments in both years, indicating that water application was not limiting at this site. These management strategies offer ways to optimize costly inputs when growing cotton in the southeastern U.S.

Suggested Citation

  • Zurweller, B.A. & Rowland, D.L. & Mulvaney, M.J. & Tillman, B.L. & Migliaccio, K. & Wright, D. & Erickson, J. & Payton, P. & Vellidis, G., 2019. "Optimizing cotton irrigation and nitrogen management using a soil water balance model and in-season nitrogen applications," Agricultural Water Management, Elsevier, vol. 216(C), pages 306-314.
  • Handle: RePEc:eee:agiwat:v:216:y:2019:i:c:p:306-314
    DOI: 10.1016/j.agwat.2019.01.011
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    References listed on IDEAS

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    1. Singh, Yudhveer & Rao, Sajjan Singh & Regar, Panna Lal, 2010. "Deficit irrigation and nitrogen effects on seed cotton yield, water productivity and yield response factor in shallow soils of semi-arid environment," Agricultural Water Management, Elsevier, vol. 97(7), pages 965-970, July.
    2. Wanjura, Donald F. & Upchurch, Dan R. & Mahan, James R. & Burke, John J., 2002. "Cotton yield and applied water relationships under drip irrigation," Agricultural Water Management, Elsevier, vol. 55(3), pages 217-237, June.
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    Cited by:

    1. Leo, Stephen & De Antoni Migliorati, Massimiliano & Nguyen, Trung H. & Grace, Peter R., 2023. "Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates," Agricultural Systems, Elsevier, vol. 205(C).
    2. DeLaune, P.B & Mubvumba, P. & Ale, S. & Kimura, E., 2020. "Impact of no-till, cover crop, and irrigation on Cotton yield," Agricultural Water Management, Elsevier, vol. 232(C).
    3. Wang, Hongbo & Li, Guohui & Huang, Weixiong & Li, Zhaoyang & Wang, Xingpeng & Gao, Yang, 2024. "Compensation of cotton yield by nitrogen fertilizer in non-mulched fields with deficit drip irrigation," Agricultural Water Management, Elsevier, vol. 298(C).
    4. Desheng Wang & Chengkun Wang & Lichao Xu & Tiecheng Bai & Guozheng Yang, 2022. "Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang," Agriculture, MDPI, vol. 12(7), pages 1-20, June.
    5. Sangha, Laljeet & Shortridge, Julie & Frame, William, 2023. "The impact of nitrogen treatment and short-term weather forecast data in irrigation scheduling of corn and cotton on water and nutrient use efficiency in humid climates," Agricultural Water Management, Elsevier, vol. 283(C).
    6. 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).

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