Jordi Salvia

IRTA Institute of Agrifood Research and Technology, Barcino, Catalonia, Spain

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Publications (5)15.73 Total impact

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    ABSTRACT: This paper analyses the viability of Brassica napus as an energy crop cultivated for producing biodiesel in southern Europe. The proposed methodology assessment combines physical variables such as grain production and agroclimate conditions with environmental analysis (LCA) in order to determine the Mediterranean agroclimates areas that could be cultivated for non-food purposes. The results obtained in a local production and distribution scenario (25 km) demonstrate that the biodiesel systems analysed have a better energy balance than diesel. Biodiesel obtained a net energy benefit of 16.25 MJ kg−1 of biodiesel or 35.10 MJ kg−1 of biodiesel when the avoided impacts from coproducts (glycerine and rapemeal) are considered in comparison with conventional diesel. In terms of environmental performance, the biodiesel system also has less impact compared with diesel in three categories Abiotic Depletion (AD), Photochemical Oxidation (PO) and Global Warming Potential (GWP). The estimated impact reduction in the GWP category when is compared with diesel reached a minimum of 1.76 kg CO2 eq. per kg of biodiesel when emissions of the use phase are included.The paper also demonstrates that agroclimates called “e”, “b” and “d” that ensure grain productions higher than 2000 kg ha−1 the biodiesel commercialization from B. napus energy crop is suitable in environmental terms for an energy local and regional production and distribution strategy.
    Biomass and Bioenergy 05/2012; 40:71–81. · 3.41 Impact Factor
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    ABSTRACT: One of the factors that may influence the rate of cross-fertilization is the relative size of the pollen donor and receptor fields. We designed a spatial distribution with four varieties of genetically-modified (GM) yellow maize to generate different sized fields while maintaining a constant distance to neighbouring fields of conventional white kernel maize. Samples of cross-fertilized, yellow kernels in white cobs were collected from all of the adjacent fields at different distances. A special series of samples was collected at distances of 0, 2, 5, 10, 20, 40, 80 and 120 m following a transect traced in the dominant down-wind direction in order to identify the origin of the pollen through SSR analysis. The size of the receptor fields should be taken into account, especially when they extend in the same direction than the GM pollen flow is coming. From collected data, we then validated a function that takes into account the gene flow found in the field border and that is very useful for estimating the % of GM that can be found in any point of the field. It also serves to predict the total GM content of the field due to cross fertilization. Using SSR analysis to identify the origin of pollen showed that while changes in the size of the donor field clearly influence the percentage of GMO detected, this effect is moderate. This study demonstrates that doubling the donor field size resulted in an approximate increase of GM content in the receptor field of 7%. This indicates that variations in the size of the donor field have a smaller influence on GM content than variations in the size of the receptor field.
    Transgenic Research 09/2011; 21(3):471-83. · 2.28 Impact Factor
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    ABSTRACT: Regulatory approvals for deliberate release of GM maize events into the environment have lead to real situations of coexistence between GM and non-GM, with some fields being cultivated with GM and conventional varieties in successive seasons. Given the common presence of volunteer plants in maize fields in temperate areas, we investigated the real impact of GM volunteers on the yield of 12 non-GM agricultural fields. Volunteer density varied from residual to around 10% of plants in the field and was largely reduced using certain cultural practices. Plant vigour was low, they rarely had cobs and produced pollen that cross-fertilized neighbour plants only at low--but variable--levels. In the worst-case scenario, the estimated content of GMO was 0.16%. The influence of GM volunteers was not enough to reach the 0.9% adventitious GM threshold but it could potentially contribute to adventitious GM levels, especially at high initial densities (i.e. above 1,000 volunteers/ha).
    Transgenic Research 03/2009; 18(4):583-94. · 2.28 Impact Factor
  • Crop Science 01/2008; 48(6). · 1.48 Impact Factor
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    ABSTRACT: We present the first study on cross-fertilization between Bt and conventional maize in real situations of coexistence in two regions in which Bt and conventional maize were cultivated. A map was designed and the different crops were identified, as were the sowing and flowering dates, in Bt and conventional maize fields. These data were used to choose the non-transgenic fields for sampling and analysis by the real-time quantification system-polymerase chain reaction (RTQ-PCR) technique. In general, the rate of cross-fertilization was higher in the borders and, in most of the fields, decreased towards the centre of the field. Nine fields had values of genetically modified organism DNA to total DNA of much lower than 0.9%, whereas in three the rate was higher. Some differences were found when comparing our results with those of common field trials. In real conditions of coexistence and in cropping areas with smaller fields, the main factors that determined cross-pollination were the synchronicity of flowering and the distances between the donor and receptor fields. By establishing an index based on these two variables, the rate of the adventitious presence of genetically modified maize could be predicted, as well as the influence of other factors. By applying this index, and in the case of a fully synchronous flowering time, a security distance between transgenic and conventional fields of about 20 m should be sufficient to maintain the adventitious presence of genetically modified organisms as a result of pollen flow below the 0.9% threshold in the total yield of the field.
    Plant Biotechnology Journal 12/2006; 4(6):633-45. · 6.28 Impact Factor