Evaluating C trends in clayey Cerrado Oxisols using a four-quadrant model based on specific arylsulfatase and β-glucosidase activities

Abstract

Management practices that promote the stabilization of extracellular enzymes in soil matrix, increasing their potential activity, favor the formation and stabilization of soil organic carbon (SOC). In this study, we present a four-quadrant model to evaluate C trends in clayey Cerrado Oxisols, based on the relationship between SOC and the average activities of arylsulfatase and β-glucosidase per unit of SOC (average specific enzyme activity, ASEA). This model assumes that areas under the same long-term soil use and management practices achieve a balance between ASEA and SOC. However, due to the greater sensitivity of soil enzymes, this balance is temporarily altered after the adoption of agricultural management practices that improve or degrade the overall soil quality (SQ). To calibrate the model, relationships were established between SOC and ASEA (0 to 10 cm soil samples) and the relative cumulative grain yield of soybean and corn in long-term field experiments. Threshold values for SOC and ASEA were defined as the equivalent of 50% of the maximum cumulative corn and soybean yields. These thresholds were used to divide an ASEA vs. SOC scatter plot into four quadrants. Quadrants 1 (high SOC/high ASEA) and 3 (low SOC/low ASEA) represent stable patterns of high- and low-quality soils, respectively. Quadrants 2 (low ASEA/high SOC) and 4 (high ASEA/low SOC) represent transitional patterns of soils undergoing biological degradation (C loss) and regenerative processes (C gain), respectively. A dataset containing 102 soil samples from commercial farms with contrasting management histories was used to validate the four-quadrant model, whereas a dataset of 1212 soil samples from representative Cerrado agricultural areas was used to characterize trends in soil C. The ASEA/SOC four-quadrant model provides a simple graphical representation that allows farmers to identify locations where soil management is following SOC gain or loss trajectories quickly and easily. This information can be used to make sound management decisions.