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Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection

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  • Klem, Karel
  • Záhora, Jaroslav
  • Zemek, František
  • Trunda, Petr
  • Tůma, Ivan
  • Novotná, Kateřina
  • Hodaňová, Petra
  • Rapantová, Barbora
  • Hanuš, Jan
  • Vavříková, Jana
  • Holub, Petr

Abstract

Water and nitrogen (N) deficit are globally the most frequently limiting factors for agricultural crops, while both often occur together. To evaluate the interactive effects of water deficit and N nutrition on remote sensing data, rainout shelter field experiments were conducted in winter wheat during 2013–2014. Canopy spectral reflectance and infrared thermal imaging parameters were correlated to biochemical, physiological, morphological and production characteristics. Correlation analysis revealed that stomatal response to water deficit is best estimated using the NPCI (Normalized Pigment Chlorophyll Index) vegetation index and also by the CWSI (Crop Water Stress Index) thermal index. A variety of vegetation indices can be used to estimate grain yield, among which the best-performing is the Normalized Red Edge-Red Index (NRERI). That index shows the highest correlation irrespective of water deficit and N nutrition. Although none of the indices provided good detection of N content in plants, the total N uptake in wheat grain was reliably estimated by the TCARI/OSAVI (Transformed Chlorophyll Absorption Reflectance Index/Optimized Soil-Adjusted Vegetation Index). The results demonstrated that utilization of N for yield or grain protein formation was largely determined by water availability, and the relationships between vegetation indices and grain protein content thus have a distinct slope under water deficit. In summary, the spectral and thermal indices can provide satisfactory estimation, irrespective of interactions between water and N deficit, for grain yield, N uptake, and stomatal responses. However, when estimating the grain protein content, the water availability should be considered.

Suggested Citation

  • Klem, Karel & Záhora, Jaroslav & Zemek, František & Trunda, Petr & Tůma, Ivan & Novotná, Kateřina & Hodaňová, Petra & Rapantová, Barbora & Hanuš, Jan & Vavříková, Jana & Holub, Petr, 2018. "Interactive effects of water deficit and nitrogen nutrition on winter wheat. Remote sensing methods for their detection," Agricultural Water Management, Elsevier, vol. 210(C), pages 171-184.
    • Handle: RePEc:eee:agiwat:v:210:y:2018:i:c:p:171-184
    DOI: 10.1016/j.agwat.2018.08.004
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    1. Bandyopadhyay, K.K. & Pradhan, S. & Sahoo, R.N. & Singh, Ravender & Gupta, V.K. & Joshi, D.K. & Sutradhar, A.K., 2014. "Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices," Agricultural Water Management, Elsevier, vol. 146(C), pages 115-123.
    2. Mathobo, Rudzani & Marais, Diana & Steyn, Joachim Martin, 2017. "The effect of drought stress on yield, leaf gaseous exchange and chlorophyll fluorescence of dry beans (Phaseolus vulgaris L.)," Agricultural Water Management, Elsevier, vol. 180(PA), pages 118-125.
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    1. Mohamed A. Darwish & Ahmed F. Elkot & Ahmed M. S. Elfanah & Adel I. Selim & Mohamed M. M. Yassin & Elsayed A. Abomarzoka & Maher A. El-Maghraby & Nazih Y. Rebouh & Abdelraouf M. Ali, 2023. "Evaluation of Wheat Genotypes under Water Regimes Using Hyperspectral Reflectance and Agro-Physiological Parameters via Genotype by Yield*Trait Approaches in Sakha Station, Delta, Egypt," Agriculture, MDPI, vol. 13(7), pages 1-20, June.
    2. He Liu & Qinghui Zhu & Xiaomeng Xia & Mingwei Li & Dongyan Huang, 2021. "Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System," Agriculture, MDPI, vol. 12(1), pages 1-17, December.
    3. Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
    4. Mwinuka, Paul Reuben & Mourice, Sixbert K. & Mbungu, Winfred B. & Mbilinyi, Boniphace P. & Tumbo, Siza D. & Schmitter, Petra, 2022. "UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: A case of Africa," Agricultural Water Management, Elsevier, vol. 266(C).
    5. Coelho, Anderson Prates & Faria, Rogério Teixeira de & Leal, Fábio Tiraboschi & Barbosa, José de Arruda & Dalri, Alexandre Barcellos & Rosalen, David Luciano, 2019. "Estimation of irrigated oats yield using spectral indices," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.

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