The SLIM (Social learning for the integrated management and sustainable use of water at catchment scale) Final Report

Background: SLIM stands for 'Socuak Learning for the Integrated Management and Sustainable Use of Water at Catchment Scale'. It is a multi-country research project funded by the European Commission (DG RESEARCH - 5th Framework Programme for research and technological development, 1998-2002). Its main theme is the investigation of the socio-economic aspects of the sustainable use of water. Within this theme, its main focus of interest lies in understanding the application of social learning as a conceptual framework, an operational principle, a policy instrument and a process of systemic change

Assessment of climate change impacts on SOC dynamic in rainfed cereal cropping systems managed with contrasting tillage practices using a multi model approach

Conservation tillage (i.e., reduced- RT and no till - NT) is frequently proposed as mitigation practices as it can contribute to increase soil organic carbon (SOC) compared to conventional mouldboard ploughing (CT). In this study, we assessed the long-term effects of different tillage management practices on crop yield and SOC stock dynamics in Mediterranean rainfed cereal cropping systems at current and future climate scenarios. We relied on data obtained from long term experiments (LTEs) coming from ICFAR network coupled with four simulation models (APSIM, DSSAT, EPIC, SALUS). Two LTEs dataset were used: AN (Ancona, Marche, 1994-2015) characterized by a two-year durum wheat-maize rotation (NT vs CT: 40 cm deep mouldboard ploughing) and PI2 (Pisa, Toscana) based on a maize continuous crop from 1994 to 1998 followed by a durum wheat-maize rotation (RT: 15 cm disc tillage; vs CT: 30 cm deep ploughing). Climate scenarios were generated by setting up a statistical model using predictors from ERA40 reanalysis and seasonal indices of temperature and precipitation from E-OBS gridded data for the period 1958-2010. The statistical downscaling model was applied to CMCC-CM predictors to obtain climate scenarios at local scale over the period 1971-2000 and 2021-2050 (RCP45 and RCP85 emission scenarios). The multi-model mean was able to better reproduce and with less uncertainty SOC dynamics than a single model, hence better SOC predictions are also expected to occur in the future assessment. Overall, our study showed a decrease of SOC stocks in both sites and tillage systems in future scenarios. However, even if conservation tillage was more affected by climate change losing more SOC than CT, these systems were still able to stock more soil organic carbon also under future scenarios

Nanotechnology and its applications in food and animal science

Nanotechnology has the potential to manipulate matter at the nanometre scale, creating and assembling substances at a molecular level with new and interesting properties. This has offered opportunities for applications in different sectors. Recently, nanotechnology has received particular attention due to its promising applications in animal nutrition, drugs and nutrients delivery, animal reproduction, disease diagnosis and treatment. In the food sector, nanotechnology is applied to the improvement of food packaging, processing, monitoring and the development of food with new functional properties that can respond to the needs of consumers. This review will focus on the advances of nanotechnology in food sector and animal science with particular attention to animal nutrition. The implications for safety and regulation will be also discussed

Modelling different cropping systems

Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations

Modelling nitrous oxide emissions of high input maize crop systems

Arable soils are a large source of nitrous oxide (N2O) emissions and several factors may affect the processes responsible of its production (nitrification and denitrification). In particular, forage crop systems for dairy farming are among the cropping systems with highest N input, mainly because they are based on high yielding forage grasses such as maize. A number of options have been explored to decrease the emissions but they remain site specific and are related to climatic, soil and local availability of management options. Moreover, guidelines for estimating N2O emission from agricultural soils does not take into account different crops, soils, climate and management, all of which are known to affect nitrification-denitrification and N2O production and emission.Process-based models represent a promising route to capture the spatial and temporal variability of N2O emissions, along with the effects of crop management. Nevertheless, the testing and comparison of these models have been limited to only a few works, with studies mainly based on biogeochemical models rather than process-based crop models. Furthermore, a multi-model ensemble analysis, which proved to be the best option for crop system analysis, has not been done extensively for the simulation of N2O emissions to addressing the various options for mitigations practices related to maize crop fertilization systems.Our objective is to evaluate the performances of several process-based models in simulating N2O emissions under different type, amount, rate of N fertilizer, i) quantify N2O emission, as a function of nitrogen inputs, across a wide range of soil types and environmental contexts; ii) assess the uncertainty in simulating N2O emissions, and iii) identify efficient mitigation of N-fertilized maize systems

Le misure agroambientali: applicazione nelle Marche e analisi di un caso di studio

A cura di Roggero P.P. e Toderi M

Monitoraggio dell’impatto ambientale dei sistemi colturali di collina a livello di sottobacino imbrifero. I. Analisi triennale delle perdite di nitrati e fosforo solubile nel deflusso idrico

Felcini Editor