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Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin

To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temp...

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Detalles Bibliográficos
Autores principales: Zhang, Yan, Wang, Feiyu, Du, Zhenjie, Dou, Ming, Liang, Zhijie, Gao, Yun, Li, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919263/
https://www.ncbi.nlm.nih.gov/pubmed/36771663
http://dx.doi.org/10.3390/plants12030580
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author Zhang, Yan
Wang, Feiyu
Du, Zhenjie
Dou, Ming
Liang, Zhijie
Gao, Yun
Li, Ping
author_facet Zhang, Yan
Wang, Feiyu
Du, Zhenjie
Dou, Ming
Liang, Zhijie
Gao, Yun
Li, Ping
author_sort Zhang, Yan
collection PubMed
description To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m(3) in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m(3) in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m(3) in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.
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spelling pubmed-99192632023-02-12 Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin Zhang, Yan Wang, Feiyu Du, Zhenjie Dou, Ming Liang, Zhijie Gao, Yun Li, Ping Plants (Basel) Article To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m(3) in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m(3) in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m(3) in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin. MDPI 2023-01-28 /pmc/articles/PMC9919263/ /pubmed/36771663 http://dx.doi.org/10.3390/plants12030580 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Yan
Wang, Feiyu
Du, Zhenjie
Dou, Ming
Liang, Zhijie
Gao, Yun
Li, Ping
Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title_full Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title_fullStr Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title_full_unstemmed Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title_short Spatio-Temporal Distribution Characteristics and Driving Factors of Main Grain Crop Water Productivity in the Yellow River Basin
title_sort spatio-temporal distribution characteristics and driving factors of main grain crop water productivity in the yellow river basin
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919263/
https://www.ncbi.nlm.nih.gov/pubmed/36771663
http://dx.doi.org/10.3390/plants12030580
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