Analysis of some parameters related to the hydraulic infiltration of a silty-loam soil subjected to organic and mineral fertilizer systems in Southern Italy
AbstractThis experiment was carried out to detect the most linear process to calculate the hydraulic conductivity, with the aim to classify the soil of experimental station of the Unit for Research in Cultivations Alternative to Tobacco (CAT), locate in South Italy (Scafati, Province of Salerno), subject to different types of manure: compost and mineral fertilizer. The field tests were made by a system measuring infiltration by double, inner and outer ring, inserted into the ground. Each ring was supplied with a constant level of water from external bottle (3 cm), and hydraulic conductivity is determined when the water flow rate in the inner ring is constant. Four areas, two fertilized by mineral fertilizer (areas I and III) and two amended with compost (areas II and IV) at two depths, 5 and 10 cm (H1-H2), were analysed. The parameters were recorded at the following dates: on 18th and 19th September 2009, respectively, at 5 and 10 cm of depth (H1-H2) in area I; on 7th and 8th October 2009 in area II; on 13th and 14th October 2009 in area III; on 16th and 17th October 2009 in area IV. The effect of compost, used one time only, is present in all parameters, even if with a low statistical significance (P<0.01-0.05). This biomass stores a better water reserve [g (100 g)–1)-Δθ] and causes a lower avidity for water (bibacity) and a better speed of percolation (Ks) of exceeding water. The organic matter decreased the variability of soil along field. The studied soil showed to be almost permeable and not having any serious problem concerning rain intensity.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2011 Antonietta Napolitano
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.