Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia

Feng, Ping; Wang, Bin; Liu, De Li; Xing, Hongtao; Ji, Fei; Macadam, Ian; Ruan, Hongyan; Yu, Qiang

doi:10.1007/s10584-018-2170-x

Cited by 71 publications

(37 citation statements)

References 41 publications

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“…The results suggested that the wheat in the NCP may still suffer from drought stress. Selection for drought‐tolerant cultivars and improvement of soil water retention capacity could be possible strategies to minimize drought damage (Feng et al ., 2018).…”

Section: Discussionmentioning

confidence: 99%

Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain

Bai

Xiao

Wang³

et al. 2020

Intl Journal of Climatology

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Future extreme climate events will become more intense and frequent with global warming, which is a great threat to wheat productivity in the North China Plain (NCP). Projecting future changes in extreme climate events is an important prerequisite for exploring crop adaptation measures to climate variation. In this study, we calculated 11 extreme climate indices at different wheat growth stages that are sensitive to wheat yield across NCP. The future climate projections were sourced from thirteen Global Climate Models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6) under two emission scenarios of future societal development pathway (SSP) 245 and SSP585. Daily climate data of thirteen GCMs were generated by using a statistically downscaled method. Two multi-model ensemble methods, that is, arithmetic mean and independence weighted mean (IWM), were used to assess the performance of thirteen GCMs in reproducing historical changes of extreme climate indices. The IWM ensemble results could better reproduce historical changes of extreme climate indices than multi-model arithmetic mean and any individual GCM. We found that the frequency and intensity of heat extremes were projected to increase over the 21st century for both scenarios, but those of cold extremes will decrease. Heat stress intensity (HSI) increases around 1.0 C for SSP245 scenario and 1.6 C for SSP585 scenario by the end of the 21st century. There was no significant change in extreme precipitation indices across the NCP, but the changes of extreme precipitation had strong spatial heterogeneity. Overall, wheat production in the NCP might have a higher frequency of exposure to extreme climate, especially under heat stress. Therefore, adopting effective adaptation measures to mitigate heat stress for wheat is the first priority in the NCP.

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Section: Discussionmentioning

confidence: 99%

Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain

Bai

Xiao

Wang³

et al. 2020

Intl Journal of Climatology

Self Cite

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“…Climate variables (e.g., temperature, precipitation) are important drivers for crop production [43][44][45]. Extreme temperatures and droughts show adverse impacts on crops in the context of global climate change [35,46,47].…”

Section: Climate Datamentioning

confidence: 99%

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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“…Various studies, conducted in England, Europe, and North America indicate that drainage can effectively lower the water table and improve crop yields (Cannell and Jackson, 1981; Evans and Fausey, 1999; Bullock and Acreman, 2003; Blann et al, 2009; Smedema et al, 2014; Gramlich et al, 2018). It was also reported to greatly reduce wheat yield losses due to waterlogging in south-western Victoria (Gardner and Flood, 1993; Christy et al, 2015; Feng et al, 2018). Despite the yield losses associated with waterlogging on prone Australian texture-contrast soils, large scale adoption of drainage is still limited in the HRZ (Cox et al, 2005; Rengasamy, 2006; Christy et al, 2015).…”

Section: Soil Managementmentioning

confidence: 99%

Soil and Crop Management Practices to Minimize the Impact of Waterlogging on Crop Productivity

et al. 2019

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Waterlogging remains a significant constraint to cereal production across the globe in areas with high rainfall and/or poor drainage. Improving tolerance of plants to waterlogging is the most economical way of tackling the problem. However, under severe waterlogging combined agronomic, engineering and genetic solutions will be more effective. A wide range of agronomic and engineering solutions are currently being used by grain growers to reduce losses from waterlogging. In this scoping study, we reviewed the effects of waterlogging on plant growth, and advantages and disadvantages of various agronomic and engineering solutions which are used to mitigate waterlogging damage. Further research should be focused on: cost/benefit analyses of different drainage strategies; understanding the mechanisms of nutrient loss during waterlogging and quantifying the benefits of nutrient application; increasing soil profile de-watering through soil improvement and agronomic strategies; revealing specificity of the interaction between different management practices and environment as well as among management practices; and more importantly, combined genetic, agronomic and engineering strategies for varying environments.

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Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia

Cited by 71 publications

References 41 publications

Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain

Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China plain

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Soil and Crop Management Practices to Minimize the Impact of Waterlogging on Crop Productivity

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