Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia

Xiang, K; Wang, B; Liu, DL; Chen, C; Waters, C; Huete, A; Yu, Q

Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia

Xiang, K Wang, B Liu, DL Chen, C Waters, C Huete, A

Yu, Q

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Agricultural Water Management, 2023, 284
Issue Date:: 2023-06-30

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Field	Value	Language
dc.contributor.author	Xiang, K
dc.contributor.author	Wang, B
dc.contributor.author	Liu, DL
dc.contributor.author	Chen, C
dc.contributor.author	Waters, C
dc.contributor.author	Huete, A https://orcid.org/0000-0003-2809-2376
dc.contributor.author	Yu, Q
dc.date.accessioned	2024-01-30T05:19:33Z
dc.date.available	2024-01-30T05:19:33Z
dc.date.issued	2023-06-30
dc.identifier.citation	Agricultural Water Management, 2023, 284
dc.identifier.issn	0378-3774
dc.identifier.issn	1873-2283
dc.identifier.uri	http://hdl.handle.net/10453/175080
dc.description.abstract	A risk-based approach is more meaningful to quantify the effects of drought on crop yield given the randomness nature of past drought events, compared to the deterministic approach. However, the majority of these probabilistic studies are conducted at national or global scale to assess the yield loss probability under given drought conditions. There is still a lack of research combining droughts and crop yields in a probabilistic way at a local scale. Moreover, it is unclear how drought threshold triggering yield loss at a given conditional probability will vary in dryland cropping regions. Here, we used wheat yield data from 66 shires in New South Wales (NSW) wheat belt and meteorological data from 986 weather stations. A copula-based probabilistic method was developed to explore the yield loss probability to various drought conditions. We investigated the drought threshold under a given yield loss probability using the constructed copula function. We found that SPEI-6 in October was the optimal drought index to represent detrended wheat yield variation as this period covered the main growth stages of winter wheat in the study region. Our results show that as the severity of drought increased, the wheat yield loss probability also increased. Yield loss probability varied among the study shires, mainly due to the various climate conditions of each region. The drought threshold in subregion 1 (the northwest) was highest, followed by subregion 2 (the southwest) and subregion 3 (the eastern), indicating that wheat yield in subregion 1 was more sensitive to drought. The findings could provide important direction and benchmarks for stakeholders in evaluating the agricultural impact of drought, especially in those drought prone areas. We expect that the methodological framework developed here can be extended to other dryland areas to provide helpful information to growers, risk management policy makers and agricultural insurance evaluators.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Agricultural Water Management
dc.relation.isbasedon	10.1016/j.agwat.2023.108359
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0701 Agriculture, Land and Farm Management, 0799 Other Agricultural and Veterinary Sciences, 0905 Civil Engineering
dc.subject.classification	Agronomy & Agriculture
dc.subject.classification	3002 Agriculture, land and farm management
dc.subject.classification	3004 Crop and pasture production
dc.subject.classification	4005 Civil engineering
dc.title	Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia
dc.type	Journal Article
utslib.citation.volume	284
utslib.for	0701 Agriculture, Land and Farm Management
utslib.for	0799 Other Agricultural and Veterinary Sciences
utslib.for	0905 Civil Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-30T05:19:31Z
pubs.publication-status	Published
pubs.volume	284

Abstract:

A risk-based approach is more meaningful to quantify the effects of drought on crop yield given the randomness nature of past drought events, compared to the deterministic approach. However, the majority of these probabilistic studies are conducted at national or global scale to assess the yield loss probability under given drought conditions. There is still a lack of research combining droughts and crop yields in a probabilistic way at a local scale. Moreover, it is unclear how drought threshold triggering yield loss at a given conditional probability will vary in dryland cropping regions. Here, we used wheat yield data from 66 shires in New South Wales (NSW) wheat belt and meteorological data from 986 weather stations. A copula-based probabilistic method was developed to explore the yield loss probability to various drought conditions. We investigated the drought threshold under a given yield loss probability using the constructed copula function. We found that SPEI-6 in October was the optimal drought index to represent detrended wheat yield variation as this period covered the main growth stages of winter wheat in the study region. Our results show that as the severity of drought increased, the wheat yield loss probability also increased. Yield loss probability varied among the study shires, mainly due to the various climate conditions of each region. The drought threshold in subregion 1 (the northwest) was highest, followed by subregion 2 (the southwest) and subregion 3 (the eastern), indicating that wheat yield in subregion 1 was more sensitive to drought. The findings could provide important direction and benchmarks for stakeholders in evaluating the agricultural impact of drought, especially in those drought prone areas. We expect that the methodological framework developed here can be extended to other dryland areas to provide helpful information to growers, risk management policy makers and agricultural insurance evaluators.

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