Fabio Vincenzi

Linking dissolved organic carbon, acetate and denitrification in agricultural soils

This study focuses on the factors affecting nitrate removal via microbial denitrification in agricultural soils, and particularly on the quantity and quality of dissolved organic carbon. To assess the relationship among dissolved organic carbon, nitrate and low molecular weight organic acids (acetate and formate), grids of ceramic suction cups were established in the four most representative soil types of the lower Po River floodplain, cropped with maize. Results highlighted a direct relation between acetate and dissolved organic carbon in all sites. The best fit was obtained in soils were the main source of organic carbon was the maize residues. By comparing dissolved organic carbon and acetate versus nitrate concentration revealed that acetate can be used as a better proxy for denitrification in the field with respect to dissolved organic carbon.

Effects of Moisture and Particle Size on Quantitative Determination of Total Organic Carbon (TOC) in Soils Using Near-Infrared Spectroscopy

Near-Infrared Spectroscopy is a cost-effective and environmentally friendly technique that could represent an alternative to conventional soil analysis methods, including total organic carbon (TOC). Soil fertility and quality are usually measured by traditional methods that involve the use of hazardous and strong chemicals. The effects of physical soil characteristics, such as moisture content and particle size, on spectral signals could be of great interest in order to understand and optimize prediction capability and set up a robust and reliable calibration model, with the future perspective of being applied in the field. Spectra of 46 soil samples were collected. Soil samples were divided into three data sets: unprocessed, only dried and dried, ground and sieved, in order to evaluate the effects of moisture and particle size on spectral signals. Both separate and combined normalization methods including standard normal variate (SNV), multiplicative scatter correction (MSC) and normalization by closure (NCL), as well as smoothing using first and second derivatives (DV1 and DV2), were applied to a total of seven cases. Pretreatments for model optimization were designed and compared for each data set. The best combination of pretreatments was achieved by applying SNV and DV2 on partial least squares (PLS) modelling. There were no significant differences between the predictions using the three different data sets (p < 0.05). Finally, a unique database including all three data sets was built to include all the sources of sample variability that were tested and used for final prediction. External validation of TOC was carried out on 16 unknown soil samples to evaluate the predictive ability of the final combined calibration model. Hence, we demonstrate that sample preprocessing has minor influence on the quality of near infrared spectroscopy (NIR) predictions, laying the ground for a direct and fast in situ application of the method. Data can be acquired outside the laboratory since the method is simple and does not need more than a simple band ratio of the spectra.