Ribal Sanchis, FJ.; Fenollosa Ribera, ML.; García Segovia, P.; Clemente Polo, G.; Escobar Lanzuela, N.; Sanjuán Pellicer, MN. (2016) Methods: An optimizing technique, specifically integer goal programming, is used as a means of designing diets which take into account the aforementioned aspects. Goal programming (GP) is used to design those menus that meet, or nearly meet, all the requirements with respect to caloric content, caloric share among macronutrients, nutrients to encourage and nutrients to limit, while reducing the carbon footprint (CFP) and the lunch budget. In order to have real, acceptable dishes, a school catering company provided information about the typical dishes they serve. The CFP of each dish was assessed, based on literature about life cycle assessment and CFP studies on food products. The nutritional value of each dish was obtained from databases, whereas prices were gathered from a wholesaler. Results and discussion:After solving the goal programming model for several CFP and budget goals, the results show reductions with respect to the average CFP of between -13% and -24%, and reductions with respect to the average budget between -10% and -15% while maintaining the nutritional aspects similar to the average of the proposed menus. The results show that a wide range of budget is available, maintaining an almost constant CFP and meeting nutritional requirements to a similar degree; therefore, it is possible to avoid trade-offs between the CFP and the budget. The analysis of the dishes selected shows how the optimization model, in general, avoids the dishes which have a high CFP and high price and which are low in iron content, but high in protein and cholesterol. Conclusions:Goal programming constitutes a suitable tool for designing diets which are economically, environmentally and nutritionally sustainable. Its flexibility enables specific issues to be studied, such as the existence of possible trade-offs between budget and CFP, attained by changing the budget and the CFP goals. By means of an iterative process, new dishes could be introduced or the existing ones could be improved, thus providing catering companies with useful information.

The eco-efficiency can be defined by using the “economic value/environmental impacts” ratio. In this study the eco-efficiency of orange production in the Comunidad Valenciana was assessed. 24 scenarios of orange production were built regarding their agricultural practices. For every scenario the environmental impacts were assessed by means of Life Cycle Assessment (LCA) as well as the economic value added. The results have been referred to 1 kg oranges. The integration of the economic and environmental assessments was made through Data Envelopment Analysis (DEA). Among the scenarios scored as eco-efficient, those with organic production prevailed.

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PURPOSE: This study aims to analyse the variability in the carbon footprint (CF) of organically and conventionally produced Valencian oranges (Spain), including both farming and post-harvest (PH) stages. At the same time, two issues regarding sample representativeness are addressed: how to determine confidence intervals from small samples and how to calculate the aggregated mean CF (and its variability) when the inventory is derived from different sources. METHODS: The functional unit was 1 kg of oranges at a European distribution centre. Farming data come from a survey of two samples of organic and conventional farms; PH data come from one PH centre; and data on exportation to the main European markets were obtained from official secondary sources. To assess the variability of the farming subsystem, a bootstrap of the mean CF was performed. The variability of the PH subsystem was assessed through a Monte Carlo simulation and a subsequent subsampling bootstrap. A weighted discrete distribution of the CF of transport and end-of-life (EoL) was built, which was also bootstrapped. The empirical distribution of the overall CF was obtained by summing all iterations of the three bootstrap procedures of the subsystems. RESULTS AND DISCUSSION: The CF of the baseline scenarios for conventional and organic production were 0.82 and 0.67 kg CO2 equivalent•kg orange-1 , respectively; the difference between their values was due mainly to differences in the farming subsystem. Distribution and EoL was the subsystem contributing the most to the CF (59.3% and 75.7% of the total CF for conventional and organic oranges, respectively), followed by the farming subsystem (34.1% and 19.8% for conventional and organic oranges, respectively). The confidence intervals for the CF of oranges were 0.72-0.92 and 0.61-0.82 kg CO2 equivalent•kg orange-1 for conventional and organic oranges, respectively, and a significant difference was found between them. If organic production were to reach 50% of the total exported production, the CF would be reduced by 5.4-8.4%. CONCLUSIONS: The case study and the methods used show that bootstrap techniques can help to test for the existence of significant differences and estimate confidence intervals of the mean CF. Furthermore, these techniques allow several CF sources to be combined so as to estimate the uncertainty in the mean CF estimate. Assessing the variability in the mean CF (or in other environmental impacts) gives a more reliable measure of the mean impact.

Influence of management practices on economic and environmental performance of crops.A case study in Spanish horticulture. AbstractThis paper assesses the effect of management practices on the environmental and economic performance of tigernut production in Spain. Tigernut is a horticultural crop grown in a very limited area with homogenous climate and soil; thus the influence of these surrounding factors on the agricultural practices and their subsequent impact can be overlooked. From an environmental perspective, the variability among farms was much greater than the one of the costs. A principal component analysis showed that keeping some impacts low would also decrease the costs. Results highlight how proper management leads to both relatively low environmental impacts and costs.

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