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"USGS Publications Warehouse": { "@context": "https://schema.org", "@type": "Article", "additionalType": "Journal Article", "name": "A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "70206445", "url": "https://pubs.usgs.gov/publication/70206445" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70206445 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.1080/17538947.2019.1651912", "url": "https://doi.org/10.1080/17538947.2019.1651912" } ], "journal": { "@type": "Periodical", "name": "International Journal of Digital Earth", "volumeNumber": "13", "issueNumber": "8" }, "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "International Journal of Digital Earth" } ], "datePublished": "2020", "dateModified": "2020-08-05", "abstract": "The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world\u2019s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40\u201350\u00b0 had much higher mean CWP (2.45\u2005kg/m\u00b3) compared to mean CWP of corn grown in other latitudes such as 30\u201340\u00b0 (1.67\u2005kg/m\u00b3) or 20\u201330\u00b0 (0.94\u2005kg/m\u00b3). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (\u22640.75\u2005kg/m\u00b3), medium (>0.75 to <1.10\u2005kg/m\u00b3), and high CWP (\u22651.10\u2005kg/m\u00b3). For corn the global CWP categories were low (\u22641.25\u2005kg/m\u00b3), medium (>1.25 to \u22641.75\u2005kg/m\u00b3), and high (>1.75\u2005kg/m\u00b3). For rice the global CWP categories were low (\u22640.70\u2005kg/m\u00b3), medium (>0.70 to \u22641.25\u2005kg/m\u00b3), and high (>1.25\u2005kg/m\u00b3). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India\u2019s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100\u2005m\u00b3 in volume that leads to many benefits such as acting as \u2018water banks\u2019 for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.", "description": "37 p.", "publisher": { "@type": "Organization", "name": "Taylor & Francis" }, "author": [ { "@type": "Person", "name": "Thenkabail, Prasad", "givenName": "Prasad", "familyName": "Thenkabail", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2182-8822", "url": "https://orcid.org/0000-0002-2182-8822" }, "affiliation": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ] }, { "@type": "Person", "name": "Daniel J. Foley", "familyName": "Daniel J. Foley", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2051-6325", "url": "https://orcid.org/0000-0002-2051-6325" }, "affiliation": [ { "@type": "Organization", "name": "Northern Arizona University" } ] }, { "@type": "Person", "name": "Aneece, Itiya", "givenName": "Itiya", "familyName": "Aneece", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-1201-5459", "url": "https://orcid.org/0000-0002-1201-5459" }, "affiliation": [ { "@type": "Organization", "name": "Western Geographic Science Center", "url": "https://www.usgs.gov/centers/western-geographic-science-center" } ] }, { "@type": "Person", "name": "Pardhasaradhi Teluguntla", "familyName": "Pardhasaradhi Teluguntla", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-8060-9841", "url": "https://orcid.org/0000-0001-8060-9841" }, "affiliation": [ { "@type": "Organization", "name": "Bay Area Environmental Research Institute at USGS" 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