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Soil Apparent Electrical Conductivity (ECa) as a Means of Monitoring Changesin Soil Inorganic N on Heterogeneous Morainic Soils in SE Norway During Two Growing Seasons

Korsaeth, Audun

Nutrient cycling in agroecosystems, 2005-07, Vol.72 (3), p.213-227 [Periódico revisado por pares]

Dordrecht: Springer Nature B.V

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  • Título:
    Soil Apparent Electrical Conductivity (ECa) as a Means of Monitoring Changesin Soil Inorganic N on Heterogeneous Morainic Soils in SE Norway During Two Growing Seasons
  • Autor: Korsaeth, Audun
  • Assuntos: Barley ; Cereal crops ; Crop growth ; Electrical conductivity ; Electrical resistivity ; Environmental monitoring ; Fertilization ; Growing season ; Heterogeneity ; Loam ; Ranking ; Regression analysis ; Regression models ; Soils ; Temporal variations ; Topsoil
  • É parte de: Nutrient cycling in agroecosystems, 2005-07, Vol.72 (3), p.213-227
  • Descrição: An efficient method to monitor changes in soil inorganic N content during crop growth would be a useful means to guide N fertilization to ensure high yields and low N losses to the environment. In this study, soil apparent electrical conductivity (ECa) measured by the widely used conductivity meter EM38 was tested as an indirect measurement of available N in spring barley during two cropping seasons at two sites with morainic loam in SE Norway. The experiment was constructed to maximize soil variation. In spite of the ȁ8noiseȁ9 caused by the soil heterogeneity, concentrations of inorganic N (cNinorg) or NO3-N were most strongly correlated with ECa in both years and at both locations (with one exception). The measurements of ECa reflected well the temporal variation in inorganic N content (Ninorg), and a ranking of the treatments based on ECa fitted very well with a ranking based on Ninorg at the first three sampling times after fertilizing. The best subset of sensor variables (i.e. variables which can be measured ȁ8on-the-goȁ9 by sensor techniques in the field) described 27–69% (average 47%) of the variation in topsoil cNinorg. When expanding the regression models to include pH as well, the degree of explanation increased significantly. In conclusion, the method of using ECa appears to be quite robust in terms of detecting relative differences in cNinorg, whereas a determination of absolute levels of cNinorg with the method is unreliable.
  • Editor: Dordrecht: Springer Nature B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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