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Estimation of maize properties and differentiating moisture and nitrogen deficiency stress via ground – Based remotely sensed data

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  • Elmetwalli, Adel H.
  • Tyler, Andrew N.

Abstract

Moisture and nitrogen deficiency are major determinant factors for cereal production in arid and semi arid environments. The ability to detect stress in crops at an early stage is crucially important if significant reductions in yield are to be averted. In this context, remotely sensed data has the possibility of providing a rapid and accurate tool for site specific management in cereal crop production. This research examined the potential of hyperspectral and broad band remote sensing for predicting maize properties under nitrogen and moisture induced stress. Spectra were collected from drip irrigated maize subjected to various rates of irrigation regimes and nitrogen fertilization. 60 spectral vegetation indices were derived and examined to predict maize yield and other properties. Highly significant correlations between maize crop properties and various vegetation indices were noticed. RVI and NDVI were found to be sensitive to maize grain yield in both tested seasons. Cred edge demonstrated the strongest significant correlations with maize yield. The correlations with grain yield were found to be strongest at the flowering stage. Penalized linear discriminant analysis (PLDA) showed the possibility to distinguish moisture and nitrogen deficiency stress spectrally. The implications of this work for the use of satellite based remote sensing in arid zone precision agriculture are discussed.

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  • Elmetwalli, Adel H. & Tyler, Andrew N., 2020. "Estimation of maize properties and differentiating moisture and nitrogen deficiency stress via ground – Based remotely sensed data," Agricultural Water Management, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:agiwat:v:242:y:2020:i:c:s0378377420307277
    DOI: 10.1016/j.agwat.2020.106413
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    References listed on IDEAS

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    1. Namara, Regassa E. & Hanjra, Munir A. & Castillo, Gina E. & Ravnborg, Helle Munk & Smith, Lawrence & Van Koppen, Barbara, 2010. "Agricultural water management and poverty linkages," Agricultural Water Management, Elsevier, vol. 97(4), pages 520-527, April.
    2. Ajdary, Khalil & Singh, D.K. & Singh, A.K. & Khanna, Manoj, 2007. "Modelling of nitrogen leaching from experimental onion field under drip fertigation," Agricultural Water Management, Elsevier, vol. 89(1-2), pages 15-28, April.
    3. Mansouri-Far, Cyrus & Modarres Sanavy, Seyed Ali Mohammad & Saberali, Seyed Farhad, 2010. "Maize yield response to deficit irrigation during low-sensitive growth stages and nitrogen rate under semi-arid climatic conditions," Agricultural Water Management, Elsevier, vol. 97(1), pages 12-22, January.
    4. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
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    1. Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.

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