气候变化领域集成服务门户(CCPortal): Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing

CCPortal

DOI	10.1016/j.cliser.2024.100455
	Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing
	Ponti, Luigi; Gutierrez, Andrew Paul; Giannakopoulos, Christos; Varotsos, Konstantinos V.; Nevado, Javier Lopez; Feria, Silvia Lopez; Gonzalez, Freddy Wilmer Rivas; Caboni, Federico; Stocchino, Federica; Rosati, Adolfo; Marchionni, Damiano; Cure, Jose Ricardo; Rodriguez, Daniel; Terrado, Marta; De Felice, Matteo; Dell'Aquila, Alessandro; Calmanti, Sandro; Arjona, Ricardo; Sanderson, Michael
发表日期	2024
ISSN	2405-8807
起始页码	34
卷号	34
英文摘要	The Spanish region of Andalusia is the world-leading olive oil producer. Its olive-dominated landscapes are among the most biodiverse drylands of the globe and prospectively among the areas most affected by climate change. This analysis used physiologically based demographic modeling (PBDM) to assess the impact of climate change on the olive/olive fly system of Andalusia. The analysis was implemented on cloud computing, allowing PBDM models to be run from any computer connected to the internet, to interface with state-of-the-art climatic drivers, and to scale efficiently with increasing computational loads and user requests. Findings include that chilling required for olive blooming will decrease in large areas of the Andalusian provinces of Jaen, Cordoba, and Sevilla, with some areas not meeting the minimum chilling threshold and some accumulating no chilling by the end of the century under the high greenhouse gas (GHG) emission scenario. Olive blooming will occur up to five weeks earlier in the Jaen, Cordoba, Sevilla, and Granada provinces, but olive yield is expected to increase or remain stable. Olive fly infestation will decrease with climate change, with infestations below the reference economic threshold of 4 % towards the end of the century in some areas under high GHG emission scenario. Measures to adapt Andalusian olive systems to climate change include: selecting olive cultivars with lower chilling requirements; implementing cover crops to enhance water use efficiency under increased CO2 concentration and uncertain precipitation projections; and targeting the spring generation of the fly and diversifying the olive landscape to reduce infestation levels.
英文关键词	Crop management strategy; Multi-trophic agroecosystem modeling; Weather -driven biology; Species interactions; Climate services; Application programming interface (API)
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Environmental Studies ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001204993100001
来源期刊	CLIMATE SERVICES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/307361
作者单位	Italian National Agency New Technical Energy & Sustainable Economics Development; University of California System; University of California Berkeley; National Observatory of Athens; Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA); Universidad Militar Nueva Granada; Universitat Politecnica de Catalunya; Barcelona Supercomputer Center (BSC-CNS); Met Office - UK
推荐引用方式 GB/T 7714	Ponti, Luigi,Gutierrez, Andrew Paul,Giannakopoulos, Christos,et al. Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing[J],2024,34.
APA	Ponti, Luigi.,Gutierrez, Andrew Paul.,Giannakopoulos, Christos.,Varotsos, Konstantinos V..,Nevado, Javier Lopez.,...&Sanderson, Michael.(2024).Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing.CLIMATE SERVICES,34.
MLA	Ponti, Luigi,et al."Prospective regional analysis of olive and olive fly in Andalusia under climate change using physiologically based demographic modeling powered by cloud computing".CLIMATE SERVICES 34(2024).