Decision support for agricultural water managementView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000689</doi_batch_id><timestamp>1713266651</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 3</title></titles><publication_date><month>09</month><year>2024</year></publication_date><journal_volume><volume>14</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Decision support for agricultural water management</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>Irrigation water use is the major pressure limiting the availability of fresh water resources in the Mediterranean. Efficient irrigation scheduling programs (IRSPs) are able to reduce water consumption; however, their selection and placement in large agricultural landscapes depend on location specific characteristics and economic indicators. Towards this end, a novel and efficient Decision Support Tool (DST) is developed in MATLAB-programming, able to assess the effectiveness of different IRSPs in reducing total agricultural water use at the catchment scale along with their impact on crop yields. The DST integrates a look-up table with data on irrigation water amounts and crop yields at different locations within a catchment, populated by a hydrological and crop growth estimator: the process-based SWAT model, into a multi-objective Genetic Algorithm, which serves as the optimization engine for the allocation of measures across the agricultural land. The optimization scheme leads rapidly to the optimal trade-off frontier between the conflicting objectives providing spatial allocations of IRSPs. The tool was implemented in the Ali Efenti catchment demonstrating optimal solutions that could save more than 10% of water by reducing cotton yields less than 5% from the baseline. The study highlights the potential of the tool to assist in the development of cost-effective water saving plans at the catchment level in order to reduce the risk of desertification in intensively cultivated areas. </p></abstract><publication_date><month>04</month><day>30</day><year>2013</year></publication_date><pages><first_page>255</first_page><last_page>263</last_page></pages><publisher_item><identifier id_type="pii">887</identifier></publisher_item><doi_data><doi>10.30955/gnj.000887</doi><timestamp>1713266651</timestamp><resource>https://journal.gnest.org/publication/887</resource></doi_data></journal_article></journal></body></doi_batch> </div>
