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Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates

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  • Leo, Stephen
  • De Antoni Migliorati, Massimiliano
  • Nguyen, Trung H.
  • Grace, Peter R.

Abstract

Cotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agisys:v:205:y:2023:i:c:s0308521x22001950
    DOI: 10.1016/j.agsy.2022.103559
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    References listed on IDEAS

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    1. Welsh, Jon & Powell, Janine & Scott, Fiona, 2015. "Optimising nitrogen fertiliser in high yielding irrigated cotton: A benefit-cost analysis and the feasibility of participation in the ERF," AFBM Journal, Australasian Farm Business Management Network, vol. 12, December.
    2. Wang, Xingpeng & Wang, Hongbo & Si, Zhuanyun & Gao, Yang & Duan, Aiwang, 2020. "Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin," Agricultural Water Management, Elsevier, vol. 241(C).
    3. Amin, Asad & Nasim, Wajid & Mubeen, Muhammad & Ahmad, Ashfaq & Nadeem, Muhammad & Urich, Peter & Fahad, Shah & Ahmad, Shakeel & Wajid, Aftab & Tabassum, Fareeha & Hammad, Hafiz Mohkum & Sultana, Syeda, 2018. "Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan," Agricultural Systems, Elsevier, vol. 167(C), pages 213-222.
    4. Basso, B. & Ritchie, J. T. & Pierce, F. J. & Braga, R. P. & Jones, J. W., 2001. "Spatial validation of crop models for precision agriculture," Agricultural Systems, Elsevier, vol. 68(2), pages 97-112, May.
    5. 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.
    6. Adhikari, Pradip & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Modala, Naga R. & Rajan, Nithya & Barnes, Edward M., 2016. "Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model," Agricultural Water Management, Elsevier, vol. 164(P2), pages 317-330.
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