Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381681566> ?p ?o ?g. }
- W4381681566 endingPage "1280" @default.
- W4381681566 startingPage "1280" @default.
- W4381681566 abstract "The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention." @default.
- W4381681566 created "2023-06-23" @default.
- W4381681566 creator A5001237699 @default.
- W4381681566 creator A5018704873 @default.
- W4381681566 creator A5022891807 @default.
- W4381681566 creator A5025690046 @default.
- W4381681566 creator A5027916619 @default.
- W4381681566 creator A5031703460 @default.
- W4381681566 creator A5034717034 @default.
- W4381681566 creator A5051744189 @default.
- W4381681566 creator A5081189592 @default.
- W4381681566 date "2023-06-21" @default.
- W4381681566 modified "2023-09-29" @default.
- W4381681566 title "Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis" @default.
- W4381681566 cites W1991976434 @default.
- W4381681566 cites W1996820225 @default.
- W4381681566 cites W2001837659 @default.
- W4381681566 cites W2027564027 @default.
- W4381681566 cites W2032804512 @default.
- W4381681566 cites W2042148766 @default.
- W4381681566 cites W2052611179 @default.
- W4381681566 cites W2071452287 @default.
- W4381681566 cites W2082818970 @default.
- W4381681566 cites W2083029259 @default.
- W4381681566 cites W2117015954 @default.
- W4381681566 cites W2118898434 @default.
- W4381681566 cites W2118921617 @default.
- W4381681566 cites W2177938084 @default.
- W4381681566 cites W2318680928 @default.
- W4381681566 cites W2395785611 @default.
- W4381681566 cites W2465787652 @default.
- W4381681566 cites W2624521219 @default.
- W4381681566 cites W2740600364 @default.
- W4381681566 cites W2746876326 @default.
- W4381681566 cites W2788598761 @default.
- W4381681566 cites W2792618910 @default.
- W4381681566 cites W2793781551 @default.
- W4381681566 cites W2793804361 @default.
- W4381681566 cites W2897966647 @default.
- W4381681566 cites W2909881334 @default.
- W4381681566 cites W2946294176 @default.
- W4381681566 cites W2964259088 @default.
- W4381681566 cites W2966652211 @default.
- W4381681566 cites W3021479982 @default.
- W4381681566 cites W3100398103 @default.
- W4381681566 cites W3110289803 @default.
- W4381681566 cites W3163444733 @default.
- W4381681566 cites W3217161402 @default.
- W4381681566 cites W388990147 @default.
- W4381681566 cites W4205109119 @default.
- W4381681566 cites W424957422 @default.
- W4381681566 cites W4306965918 @default.
- W4381681566 doi "https://doi.org/10.3390/agriculture13071280" @default.
- W4381681566 hasPublicationYear "2023" @default.
- W4381681566 type Work @default.
- W4381681566 citedByCount "0" @default.
- W4381681566 crossrefType "journal-article" @default.
- W4381681566 hasAuthorship W4381681566A5001237699 @default.
- W4381681566 hasAuthorship W4381681566A5018704873 @default.
- W4381681566 hasAuthorship W4381681566A5022891807 @default.
- W4381681566 hasAuthorship W4381681566A5025690046 @default.
- W4381681566 hasAuthorship W4381681566A5027916619 @default.
- W4381681566 hasAuthorship W4381681566A5031703460 @default.
- W4381681566 hasAuthorship W4381681566A5034717034 @default.
- W4381681566 hasAuthorship W4381681566A5051744189 @default.
- W4381681566 hasAuthorship W4381681566A5081189592 @default.
- W4381681566 hasBestOaLocation W43816815661 @default.
- W4381681566 hasConcept C10138342 @default.
- W4381681566 hasConcept C118518473 @default.
- W4381681566 hasConcept C12174686 @default.
- W4381681566 hasConcept C134121241 @default.
- W4381681566 hasConcept C139719470 @default.
- W4381681566 hasConcept C144133560 @default.
- W4381681566 hasConcept C162324750 @default.
- W4381681566 hasConcept C166957645 @default.
- W4381681566 hasConcept C191897082 @default.
- W4381681566 hasConcept C192562407 @default.
- W4381681566 hasConcept C205649164 @default.
- W4381681566 hasConcept C2778348673 @default.
- W4381681566 hasConcept C2778755073 @default.
- W4381681566 hasConcept C32896092 @default.
- W4381681566 hasConcept C38652104 @default.
- W4381681566 hasConcept C39432304 @default.
- W4381681566 hasConcept C41008148 @default.
- W4381681566 hasConcept C58640448 @default.
- W4381681566 hasConceptScore W4381681566C10138342 @default.
- W4381681566 hasConceptScore W4381681566C118518473 @default.
- W4381681566 hasConceptScore W4381681566C12174686 @default.
- W4381681566 hasConceptScore W4381681566C134121241 @default.
- W4381681566 hasConceptScore W4381681566C139719470 @default.
- W4381681566 hasConceptScore W4381681566C144133560 @default.
- W4381681566 hasConceptScore W4381681566C162324750 @default.
- W4381681566 hasConceptScore W4381681566C166957645 @default.
- W4381681566 hasConceptScore W4381681566C191897082 @default.
- W4381681566 hasConceptScore W4381681566C192562407 @default.
- W4381681566 hasConceptScore W4381681566C205649164 @default.
- W4381681566 hasConceptScore W4381681566C2778348673 @default.
- W4381681566 hasConceptScore W4381681566C2778755073 @default.