000224896 001__ 224896
000224896 005__ 20181203024527.0
000224896 0247_ $$2doi$$a10.1016/j.jclepro.2016.06.099
000224896 022__ $$a0959-6526
000224896 02470 $$2ISI$$a000388775200038
000224896 037__ $$aARTICLE
000224896 245__ $$aMulti-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain
000224896 260__ $$aOxford$$bElsevier$$c2017
000224896 269__ $$a2017
000224896 300__ $$a15
000224896 336__ $$aJournal Articles
000224896 520__ $$aMeeting the growing food demand with minimum impact on the environment is a major challenge to face for ensuring a more sustainable food production. To tackle this problem, in this article we present a novel systematic method for agriculture planning that optimally allocates rainfed and irrigated cropping areas, thereby enhancing food availability and reducing the environmental impact of agriculture. The allocation problem is mathematically formulated as a multi-objective linear programming problem that simultaneously accounts for the maximisation of the crop production and the minimisation of the environmental impact caused by water consumption. To quantify the environmental damage, life cycle assessment principles and water footprint concepts are integrated into the model. The capabilities of our tool are illustrated through its application to a real case study that considers wheat production in Spain. The results show that the current allocation of rainfed and irrigated wheat areas in Spain is sub-optimal. Our tool provides a set of alternatives for optimally reallocating these wheat areas that ultimately achieve significant reductions in environmental impact while maintaining or even increasing the production level. The analysis clearly demonstrates that the optimal allocation of rainfed and irrigated cropping areas is a potential pathway to minimise the environmental impact of water consumption in food production. Our systematic decision-support tool aims to assist famers and policy-makers in the transition towards a more sustainable agricultural sector. (C) 2016 Elsevier Ltd. All rights reserved.
000224896 6531_ $$aMulti-objective optimization
000224896 6531_ $$aAgriculture
000224896 6531_ $$aLinear programming
000224896 6531_ $$aWater footprint
000224896 6531_ $$aLife cycle assessment
000224896 6531_ $$aDecision-making tool
000224896 700__ $$aGalan-Martin, Angel$$uUniv Rovira & Virgili, Dept Engn Quim, Av Paisos Catalans 26, E-43007 Tarragona, Spain
000224896 700__ $$0248396$$aVaskan, Pavel$$g251123$$uUniv Rovira & Virgili, Dept Engn Quim, Av Paisos Catalans 26, E-43007 Tarragona, Spain
000224896 700__ $$aAnton, Assumpcio$$uUniv Rovira & Virgili, Dept Engn Quim, Av Paisos Catalans 26, E-43007 Tarragona, Spain
000224896 700__ $$aJimenez Esteller, Laureano$$uUniv Rovira & Virgili, Dept Engn Quim, Av Paisos Catalans 26, E-43007 Tarragona, Spain
000224896 700__ $$aGuillen-Gosalbez, Gonzalo$$uUniv Rovira & Virgili, Dept Engn Quim, Av Paisos Catalans 26, E-43007 Tarragona, Spain
000224896 773__ $$j140$$q816-830$$tJournal Of Cleaner Production
000224896 909C0 $$0252110$$pGR-GN$$xU12053
000224896 909CO $$ooai:infoscience.tind.io:224896$$particle$$pENAC
000224896 917Z8 $$x105271
000224896 937__ $$aEPFL-ARTICLE-224896
000224896 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000224896 980__ $$aARTICLE