Brazilian soil scientists have increased the use of the term “soil quality” in their scientific publications in the last decade. However, it remains unclear if those publications only mention “soil quality” in a broad context, or the studies are focused on soil quality assessments, integrating soil chemical, physical and biological indicators. The objective of this systematic review was to carry out a critical analysis of the conception in using the term “soil quality” in recent publications derived from studies [...] ABSTRACT Brazilian soil scientists have increased the use of the term “soil quality” in their scientific publications in the last decade. However, it remains unclear if those publications only mention “soil quality” in a broad context, or the studies are focused on soil quality assessments, integrating soil chemical, physical and biological indicators. The objective of this systematic review was to carry out a critical analysis of the conception in using the term “soil quality” in recent publications derived from studies

Quantifying soil gas emissions is costly, since it requires specific methodologies and equipment. The objective of this study was to evaluate modeling by nonlinear regression and artificial neural networks (ANN) to estimate CO2 emissions caused by soil managements. CO2 emissions were evaluated in two different soil management systems: no-tillage and minimum tillage. Readings of CO2 flow were carried out by an automated closed system chamber; soil temperature, water content, density, and total organic carbon were also determined. The regression model and the ANN models were adjusted based on the correlation of the variables measured in the areas where the soil was managed with no-tillage and minimum tillage with data of CO2 emission. Artificial neural networks are more accurate to determine correlations between CO2 emissions and soil temperature, water content, density, and organic carbon content than linear regression.