The issue of water and climate change is present in many countries. Drought stress is one of the main abiotic stresses influencing turfgrass growth and quality. Tall fescue is the most suitable cool-season turfgrass for the Mediterranean region. This species has a better heat tolerance than perennial ryegrass and Kentucky bluegrass. The analysis of radiation reflected by turfgrass can supply precious information on drought stress and nutritional status. In this study a Linear Gradient Irrigation System (LGIS) was adopted on a Festuca arundinacea turf with 9 water replenishment levels and 2 nitrogen conditions, to evaluate the proximity sensed spectral reflectance. ET 0 was estimated using the Hargreaves and Samani method. The following parameters were determined: turf quality, drought tolerance, pest problems, temperature of the surface, clippings weight and relative nitrogen content, turf growth and soil moisture. Spectral reflectance data were acquired using a LICOR 1800 spectroradiometer. Pearson correlation coefficients were studied among all parameters and vegetation indices. Nitrogen fertilization influenced significantly turf quality, clippings weight, nitrogen content and turf growth. Water replenishment influenced significantly all parameters except nitrogen content. Among all parameters the highest correlation coefficient was registered relating drought tolerance with turf quality (r = 0.88) and with surface temperature (r = -0.88). Among vegetation indices results showed that Water Index (WI) and Normalized Difference Water Index (NDWI), are the most suitable to discriminate between different levels of water replenishment. Comparing WI with NDWI, the correlation coefficients were higher for Water Index in all the parameters, in particular the highest WI value was registered for drought tolerance (r = 0.91). This preliminary research demonstrates that spectral remote sensing can be a useful diagnostic tool to detect water stress in turfgrasses.
The aim of this work is to evaluate the sustainability, in terms of greenhouse gases emission saving, of a new potential bio-ethanol production chain in comparison with the most common ones. The innovation consists of producing bio-ethanol from different types of no-food grapes, while usually bio-ethanol is obtained from matrices taken away from crop for food destination: sugar cane, corn, wheat, sugar beet. In the past, breeding programs were conducted with the aim of improving grapevine characteristics, a large number of hybrid vine varieties were produced and are nowadays present in the Viticulture Research Centre (CRA-VIT) Germplasm Collection. Some of them are potentially interesting for bio-energy production because of their high production of sugar, good resistance to diseases, and ability to grow in marginal lands. Life cycle assessment (LCA) of grape ethanol energy chain was performed following two different methods: i) using the spreadsheet <em>BioGrace</em>, developed within the <em>Intelligent Energy Europe</em> program to support and to ease the Renewable Energy Directive 2009/28/EC implementation; ii) using a dedicated LCA software. Emissions were expressed in CO<sub>2</sub> equivalent (CO<sub>2</sub>eq). These two tools gave very similar results. The overall emissions impact of ethanol production from grapes on average is about 33 g CO<sub>2</sub>eq MJ<sup>–1</sup> of ethanol if prunings are used for steam production and 53 g CO<sub>2</sub>eq MJ<sup>–1</sup> of ethanol if methane is used. The comparison with other bio-energy chains points out that the production of ethanol using grapes represents an intermediate situation in terms of general emissions among the different production chains. The results showed that the sustainability limits provided by the normative are respected to this day. On the contrary, from 2017 this production will be sustainable only if the transformation processes will be performed using renewable sources of energy.
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