Australian wheat production expected to decrease by the late 21st century

Wang, Bin; Liu, De Li; O’Leary, G.; Asseng, Senthold; Macadam, Ian; Lines-Kelly, Rebecca; Yang, Xihua; Clark, Anthony; Crean, Jason; Sides, Timothy; Xing, Hongtao; Mi, Chunrong; Yu, Qiang

doi:10.1111/gcb.14034

Cited by 67 publications

(34 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher AUC or TSS scores represent better model performance. Following previous studies (Lasram et al, 2010;Wang, Liu, et al, 2018), AUC scores between 0.7-0.8 were classified as "fair" and 0.8-1 as "good," TSS scores between 0.4-0.6 were classified as "fair" and 0.6-1 as "good." Only models with AUC scores higher than 0.8 and TSS scores higher than 0.45 were selected and used to predict potential distribution for given species.…”

Section: Calibration and Evaluation Of Species Distribution Modelsmentioning

confidence: 99%

“…Species distribution model (SDM) is based on the concept of ecological niche and can identify the statistical relationships between species occurrence data and environmental variables (Peterson et al, ). SDM has been widely used in the prediction of environmental suitability and potential distribution range for species (Dyderski, Paz, Frelich, & Jagodzinski, ; Noce, Collalti, & Santini, ; Wang, Liu, et al, ). In this study, we used comprehensive field investigation data and an ensemble SDM method to predict the potential distribution (i.e., suitable habitat) of four representative woody plant species in the Junggar Basin under the current climate and future climate scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China

Eziz

Zhang

et al. 2019

Ecology and Evolution

View full text Add to dashboard Cite

AimDryland ecosystems are exceedingly sensitive to climate change. Desertification induced by both climate changes and human activities seriously threatens dryland vegetation. However, the impact of climate change on distribution of dryland plant species has not been well documented. Here, we studied the potential distribution of four representative dryland plant species (Haloxylon ammodendron, Anabasis aphylla, Calligonum mongolicum, and Populus euphratica) under current and future climate scenarios in a temperate desert region, aiming to improve our understanding of the responses of dryland plant species to climate change and provide guidance for dryland conservation and afforestation.LocationJunggar Basin, a large desert region in northwestern China.MethodsOccurrence data of the studied species were collected from an extensive field investigation of 2,516 sampling sites in the Junggar Basin. Ensemble species distribution models using 10 algorithms were developed and used to predict the potential distribution of each studied species under current and future climate scenarios.Result Haloxylon ammodendron and A. aphylla were likely to lose most of their current suitable habitats under future climate scenarios, while C. mongolicum and P. euphratica were likely to expand their ranges or remain relatively stationary. Variable importance evaluation showed that the most important climate variables influencing species distribution differed across the studied species. These results may be explained by the different ecophysiological characteristics and adaptation strategies to the environment of the four studied species.Main conclusionsWe explored the responses of the representative dryland plant species to climate change in the Junggar Basin in northwestern China. The different changes in suitability of different species imply that policymakers may need to reconsider the selection and combination of the afforestation species used in this area. This study can provide valuable reference for the management and conservation of dryland ecosystems under future climate change scenarios.

show abstract

Section: Calibration and Evaluation Of Species Distribution Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China

Eziz

Zhang

et al. 2019

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…We used APSIM because it has been widely used in wheat in climate change (e.g. Hochman, Gobbett, & Horan, 2017; Wang et al, 2018) and crop trait evaluation (e.g. Zhao et al, 2019) studies, and has recently been successfully tested against other AGFACE data (O'Leary et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

A reduced‐tillering trait shows small but important yield gains in dryland wheat production

Houshmandfar

Ota

O’Leary

et al. 2020

Global Change Biology

Self Cite

View full text Add to dashboard Cite

Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time‐step coupled radiation and transpiration efficiency model to simulate the impact of the reduced‐tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced‐tillering trait in the most water‐limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2] (e[CO2]) alone might limit the area where the reduced‐tillering trait is advantageous, the most likely climate scenario of e[CO2] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long‐term average yield advantages were small (ranged from 31 to 51 kg ha−1 year−1), across large dryland areas the value is large (potential cost‐benefits ranged from Australian dollar 23 to 60 MIL/year). It seems therefore worthwhile to further explore this reduced‐tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world.

show abstract

“…Agricultural Production Systems sIMulator has been widely tested in Australia (e.g., Asseng, Turner, & Keating, 2001;Chenu et al, 2011;Christopher, Christopher, Borrell, Fletcher, & Chenu, 2016;Hochman, Holzworth, & Hunt, 2009;Lilley & Kirkegaard, 2007;Wang et al, 2018). A modified version of the APSIM-wheat model (Holzworth et al, 2014;Keating et al, 2003), version 7.9, was used to simulate wheat crop growth and development at 60 selected sites across the Australian wheatbelt ( Figure 1).…”

Section: Simulation Setupmentioning

confidence: 99%

Detection of major weather patterns reduces number of simulations in climate impact studies

Ababaei

Ullah

2020

J Agronomy Crop Science

View full text Add to dashboard Cite

With climate change posing a serious threat to food security, there has been an increased interest in simulating its impact on cropping systems. Crop models are useful tools to evaluate strategies for adaptation to future climate; however, the simulation process may be infeasible when dealing with a large number of G × E × M combinations. We proposed that the number of simulations could significantly be reduced by clustering weather data and detecting major weather patterns. Using 5, 10 and 15 clusters (i.e., years representative of each weather pattern), we simulated phenology, cumulative transpiration, heat-shock-induced yield loss (heat loss) and grain yield of four Australian cultivars across the Australian wheatbelt over a 30-year period under current and future climates. A strong correlation (r 2 ≈1) between the proposed method and the benchmark (i.e., simulation of all 30 years without clustering) for phenology suggested that average duration of crop growth phases could be predicted with substantially fewer simulations as accurately as when simulating all 30 seasons. With mean absolute error of 0.64 days for phenology when only five clusters were identified, this method had a deviation considerably lower than the reported deviations of calibrated crop models. Although the proposed method showed higher deviations for traits highly sensitive to temporal climatic variability such as cumulative transpiration, heat loss and grain yield when five clusters were used, significantly strong correlations were achieved when 10 or 15 clusters were identified. Furthermore, this method was highly accurate in reproducing site-level impact of climate change. Less than 7% of site × general circulation model (GCM) combinations (zero for phenology) showed incorrect predication of the direction (+/−) of climate change impact when only five clusters were identified while the accuracy further increased at the regional level and with more clusters. The proposed method proved promising in predicting selected traits of wheat crops and can reduce number of simulations required to predict crop responses to climate/management scenarios in model-aided ideotyping and climate impact studies. K E Y W O R D S climate change, clustering, crop modelling, ideotyping, phenology, weather patterns | 377 ABABAEI And nAJEEB S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Ababaei B, Najeeb U. Detection of major weather patterns reduces number of simulations in climate impact studies.

show abstract

Australian wheat production expected to decrease by the late 21st century

Cited by 67 publications

References 55 publications

Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China

Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China

A reduced‐tillering trait shows small but important yield gains in dryland wheat production

Detection of major weather patterns reduces number of simulations in climate impact studies

Contact Info

Product

Resources

About