FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2884771298> ?p ?o ?g. }

• W2884771298 endingPage "430" @default.
• W2884771298 startingPage "422" @default.
• W2884771298 abstract "To quantitatively analyze the main driving factors of agricultural water use in different stages in the middle reaches of the Heihe River basin, the Logarithmic Mean Divisia Index (LMDI) decomposition method was employed to calculate the contribution of each driving factor to agricultural water use. The crop-planting scale, cropping pattern, irrigation quota, and irrigation efficiency of different crops were chosen as representative factors of agricultural water use. The study revealed that (1) from 1991 to 2015, agricultural water use exhibited a fluctuating growth trend that resulted in a 0.031 billion m3 increase in use. From 1991 to 2001, agricultural water use increased by 0.069 billion m3, and from 2002 to 2015, it decreased by 0.038 billion m3. (2) In each research period, the expansion of the crop-planting scale and unreasonable cropping patterns increased agricultural water use. However, decreases in irrigation quotas and improvements in irrigation efficiency decreased agricultural water use. The contributions of these changes were 1.138 billion m3, 0.109 billion m3, -1.08 billion m3, and -0.136 billion m3, respectively, from 1991 to 2015. Comparing the period 1991 to 2001 with 2002 to 2015, the increase associated with the crop-planting scale and the decrease related to irrigation quotas were prominent and dramatically changed agricultural water use. (3) The effects of crops varied in different research periods. From 1991 to 2001, the contribution of cash crops’ increase was 0.446 billion m3, which was more prominent than that of food crops’ decrease (-3.78 billion m3), and from 2002 to 2015, the agricultural water use was decreased for all crops except maize. In conclusion, the best measures to decrease agricultural water use in the middle reaches of the Heihe River basin are to control the crop-planting scale and optimize the cropping pattern. The results of this study indicate how diverse determinants affect agricultural water use and provide insight for local agricultural water savings." @default.
• W2884771298 created "2018-08-03" @default.
• W2884771298 creator A5001730991 @default.
• W2884771298 creator A5014462733 @default.
• W2884771298 creator A5033888528 @default.
• W2884771298 creator A5065442873 @default.
• W2884771298 creator A5081632776 @default.
• W2884771298 date "2018-09-01" @default.
• W2884771298 modified "2023-10-17" @default.
• W2884771298 title "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China" @default.
• W2884771298 cites W1522204718 @default.
• W2884771298 cites W1608671743 @default.
• W2884771298 cites W165154121 @default.
• W2884771298 cites W1855570824 @default.
• W2884771298 cites W1979131945 @default.
• W2884771298 cites W2006428852 @default.
• W2884771298 cites W2011206232 @default.
• W2884771298 cites W2011228107 @default.
• W2884771298 cites W2011852807 @default.
• W2884771298 cites W2019721667 @default.
• W2884771298 cites W2024027195 @default.
• W2884771298 cites W2036298099 @default.
• W2884771298 cites W2044055559 @default.
• W2884771298 cites W2054634939 @default.
• W2884771298 cites W2072400194 @default.
• W2884771298 cites W2074002081 @default.
• W2884771298 cites W2149857156 @default.
• W2884771298 cites W2150002313 @default.
• W2884771298 cites W2167030433 @default.
• W2884771298 cites W2207717133 @default.
• W2884771298 cites W2324736186 @default.
• W2884771298 cites W2328941805 @default.
• W2884771298 cites W2396654588 @default.
• W2884771298 cites W2397688665 @default.
• W2884771298 cites W2460783398 @default.
• W2884771298 cites W2474920775 @default.
• W2884771298 cites W2547378489 @default.
• W2884771298 cites W2549562448 @default.
• W2884771298 cites W2551807477 @default.
• W2884771298 cites W2735662073 @default.
• W2884771298 cites W283584222 @default.
• W2884771298 doi "https://doi.org/10.1016/j.agwat.2018.06.041" @default.
• W2884771298 hasPublicationYear "2018" @default.
• W2884771298 type Work @default.
• W2884771298 sameAs 2884771298 @default.
• W2884771298 citedByCount "38" @default.
• W2884771298 countsByYear W28847712982019 @default.
• W2884771298 countsByYear W28847712982020 @default.
• W2884771298 countsByYear W28847712982021 @default.
• W2884771298 countsByYear W28847712982022 @default.
• W2884771298 countsByYear W28847712982023 @default.
• W2884771298 crossrefType "journal-article" @default.
• W2884771298 hasAuthorship W2884771298A5001730991 @default.
• W2884771298 hasAuthorship W2884771298A5014462733 @default.
• W2884771298 hasAuthorship W2884771298A5033888528 @default.
• W2884771298 hasAuthorship W2884771298A5065442873 @default.
• W2884771298 hasAuthorship W2884771298A5081632776 @default.
• W2884771298 hasConcept C105795698 @default.
• W2884771298 hasConcept C110158866 @default.
• W2884771298 hasConcept C118518473 @default.
• W2884771298 hasConcept C129225989 @default.
• W2884771298 hasConcept C13558536 @default.
• W2884771298 hasConcept C149207113 @default.
• W2884771298 hasConcept C153823671 @default.
• W2884771298 hasConcept C162324750 @default.
• W2884771298 hasConcept C166957645 @default.
• W2884771298 hasConcept C176205827 @default.
• W2884771298 hasConcept C186370098 @default.
• W2884771298 hasConcept C18903297 @default.
• W2884771298 hasConcept C205649164 @default.
• W2884771298 hasConcept C33923547 @default.
• W2884771298 hasConcept C39432304 @default.
• W2884771298 hasConcept C48824518 @default.
• W2884771298 hasConcept C50551742 @default.
• W2884771298 hasConcept C524765639 @default.
• W2884771298 hasConcept C6557445 @default.
• W2884771298 hasConcept C86803240 @default.
• W2884771298 hasConcept C88862950 @default.
• W2884771298 hasConcept C97615858 @default.
• W2884771298 hasConceptScore W2884771298C105795698 @default.
• W2884771298 hasConceptScore W2884771298C110158866 @default.
• W2884771298 hasConceptScore W2884771298C118518473 @default.
• W2884771298 hasConceptScore W2884771298C129225989 @default.
• W2884771298 hasConceptScore W2884771298C13558536 @default.
• W2884771298 hasConceptScore W2884771298C149207113 @default.
• W2884771298 hasConceptScore W2884771298C153823671 @default.
• W2884771298 hasConceptScore W2884771298C162324750 @default.
• W2884771298 hasConceptScore W2884771298C166957645 @default.
• W2884771298 hasConceptScore W2884771298C176205827 @default.
• W2884771298 hasConceptScore W2884771298C186370098 @default.
• W2884771298 hasConceptScore W2884771298C18903297 @default.
• W2884771298 hasConceptScore W2884771298C205649164 @default.
• W2884771298 hasConceptScore W2884771298C33923547 @default.
• W2884771298 hasConceptScore W2884771298C39432304 @default.
• W2884771298 hasConceptScore W2884771298C48824518 @default.
• W2884771298 hasConceptScore W2884771298C50551742 @default.
• W2884771298 hasConceptScore W2884771298C524765639 @default.
• W2884771298 hasConceptScore W2884771298C6557445 @default.

• next