Environmental effectiveness of GAEC cross-compliance standard 2.2 "Maintaining the level of soil organic matter through crop rotation" and economic evaluation of the competitiveness gap for farmers
AbstractWithin the Project MO.NA.CO was evaluated the Environmental effectiveness of GAEC cross-compliance standard 2.2 “Maintaining the level of soil organic matter through crop rotation” and economic evaluation of the competitiveness gap for farmers who support or not the cross-compliance regime. The monitoring was performed in nine experimental farms of the Council for Agricultural Research and Economics (CREA) distributed throughout Italy and with different soil and climatic conditions. Were also evaluated the soil organic matter and some yield parameters, in a cereal monocropping (treatment counterfactual) and a two-year rotation cereal-legume or forage (treatment factual). The two-years application of the standard “crop rotations” has produced contrasting results with regards to the storage of soil organic matter through crop rotation and these were not sufficient to demonstrate a statistically significant effect of treatment in any of the farms considered in monitoring, only in those farms subjected to more years of monitoring was recorded only a slight effect of the standard as a trend. The variations of organic matter in soils in response to changes in the culture technique or in the management of the soil may have long lag times and two years of time are not sufficient to demonstrate the dynamics of SOM associated with the treatment, also in consideration of the large inter annual variability recorded in different monitored sites.
Article - Italian: 289
Article - English: 347
Technical Report: 272
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) 2015 Lamberto Borrelli, Roberta Farina, Paolo Bazzoffi, Antonio Melchiorre Carroni, Paola Ruda, Mauro Salis, Silvia Carnevale, Andrea Rocchini, Nino Virzì, Francesco Intrigliolo, Massimo Palumbo, Michele Cambrea, Alfio Platania, Fabiola Sciacca, Stefania Licciardello, Antonio Troccoli, Mario Russo, Marisanna Speroni, Giovanni Cabassi, Luigi Degano, Roberto Fuccella, Francesco Savi, Rosa Francaviglia, Ulderico Neri, Margherita Falcucci, Giampiero Simonetti, Olimpia Masetti, Gianluca Renzi, Domenico Ventrella, Vittorio Alessandro Vonella, Luisa Giglio, Francesco Fornaro, Rita Leogrande, Carolina Vitti, Marcello Mastrangelo, Francesco Montemurro, Angelo Fiore, Mariangela Diacono, Lorenzo Furlan, Francesca Chiarini, Francesco Fracasso, Erica Sartori, Antonio Barbieri, Francesco Fagotto, Marco Fedrizzi, Giulio Sperandio, Mauro Pagano, Roberto Fanigliulo, Mirko Guerrieri, Daniele Puri, Michele Colauzzi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.