Soil quality assessment using erosion-sensitive indices and fuzzy membership under different cropping systems on a Ferralsol in Brazil

The objective of this study was to evaluate soil quality under different cropping systems through two erosion-sensitive indexing methodologies applying fuzzy membership functions to a minimum data set (MDS) of soil properties. The experiment was conducted in a Ferralsol, in annual growing cycles from 2007 to 2014. The evaluated cropping systems included individual and intercropped treatments using: sunn hemp ( Crotalaria juncea L .), pearl millet ( Pennisetum glaucum L. ), jack bean ( Canavalia ensiformis L. ), pigeon pea ( Cajanus cajan L. Huth) , and maize ( Zea mays L. ). Sampling in an adjacent area under native forest was also performed as a reference ecosystem. Soil properties such as bulk density, micro-, macro- and total porosity, aggregates stability, laboratorial soil fertility properties, and soil organic matter content were selected as MDS of physical and chemical properties, being used to compute two soil quality indices: 1) Integrated Quality Index (IQI), and 2) Nemoro Quality Index (NQI). Soil quality results showed the lowest values of soil and water losses corresponded to the largest soil quality indices evidencing that the usage of water-erosion-sensitive indices can be useful for the prediction of soil quality status. The selected MDS of soil properties were adequate indicators of soil quality as reduced water erosion was associated with large soil quality indices, e.g. negative correlation between soil macroporosity and soil erosion processes. The fuzzy methodology was effective in predicting soil quality using a MDS of soil property indicators, which can benefit various stakeholders in their decision-making process to ensure continuing and sustainable crop production. • Plant cover controls long-term soil erosion. • Cropping systems with cover crops were assessed via soil quality indices. • Soil quality indices based on a minimum dataset of soil properties were computed. • Results proved the effectiveness of fuzzy logic analyses to predict soil quality. • Cover crops can reduce soil and water losses and increase soil quality indices.