Pipette Method: Errors Resulting From Aliquot Collection Depth in Soil Clay Quantification

  •  Jaedson Cláudio Anunciato Mota    
  •  Carlos Eduardo Linhares Feitosa    
  •  Lucas de Sousa Oliveira    
  •  José Israel Pinheiro    
  •  Alfredo Mendonça de Sousa    
  •  Thiago Leite de Alencar    
  •  Márcio Godofrêdo Rocha Lobato    
  •  Alexandre dos Santos Queiroz    
  •  Ícaro Vasconcelos do Nascimento    


Granulometry represents the relative proportions of the fractions that compose the soil, being an important agronomic tool to infer mean values of density, water availability and cation exchange capacity, besides being useful in soil classification. Among the methods employed to determine the fractions composing the soil, those which consider the separation by sedimentation for the clay fraction still have problems in the analytical protocol, which are directly responsible of errors in the results obtained. Given the above, this study aimed to evaluate the best pipette immersion depth to collect the aliquot containing only clay, to calculate and discuss the errors associated with collection of the aliquot containing clay fraction in soil granulometric analysis. Samples for granulometric analysis were collected in the superficial layer and top of the B horizon of an Argissolo Amarelo, corresponding to the textural classes sandy loam and sandy clay. Regardless of soil textural class, the depth h = 5 cm established in the calculation using the Stokes’s equation leads to overestimation and underestimation of clay and silt fractions in the soil. The collection should be performed with the pipette tip positioned at h/2 = 2.5 cm.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1916-9752
  • Issn(Onlne): 1916-9760
  • Started: 2009
  • Frequency: monthly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

  • Google-based Impact Factor (2016): 2.28
  • h-index (December 2017): 31
  • i10-index (December 2017): 304
  • h5-index (December 2017): 22
  • h5-median (December 2017): 27