TITLE:
Evaluation of a Penetration Resistance Model in Oxisol under No-Till and Texture Variation
AUTHORS:
João Tavares Filho, Stephanie dos Santos Locatelli, Tamires Firmino, Lucas Augusto De Assis Moraes, Thadeu Rodrigues De Melo
KEYWORDS:
Penetration Resistance Model, Oxisol, No-Till, Compaction, Physical Properties
JOURNAL NAME:
Open Access Library Journal,
Vol.9 No.11,
November
30,
2022
ABSTRACT: The no-tillage system influences the structure of the soil, as the absence of plows combined with the intensive use of heavy machinery contributes to variations in soil density and soil compaction. On the other hand, a no-tillage system tends to increase soil organic matter, which probably increases soil elasticity acting on the soil structure and possibly helping to increase the soil’s mechanical resistance to compaction. In a 2012 study, to evaluate the fit of the resistance curve to soil penetration and study the compaction of a very clayey Oxisol (clay content ~ 665 g·kg-1) under no-till, a model was proposed with very good results. Good for data collected in the field. However, only one type of texture was considered and, therefore, the objective of this work was to evaluate this model for Latosols under no-tillage conditions, but with varying clay contents. Here, 355 soil penetration resistance points were made with a dynamic impact penetrometer, and samples were collected to determine soil density, moisture, and organic matter in Oxisols with varying clay content. The results confirmed that the non-linear model which includes density, moisture, and soil organic matter content, proved to be efficient for the adjustment of the penetration resistance curve in the studied Latosols with significant variation in clay content and under no-tillage. The inclusion of organic matter allowed, in relation to the control model, a more excellent approximation of the resistance values obtained in the field of the 1:1 line and improved the coefficient of determination by 27% and the correlation coefficient by 13% and the relative error absolute was reduced by 5.26 times compared to a model that used only soil density and moisture.