Characterisation of chickpea cropping systems in Australia for major abiotic production constraints

Chauhan, Y. S.; Allard, Samantha; Williams, Rex; Williams, Brett; Chenu, Karine; Rachaputi, Rao C. N.

doi:10.1016/j.fcr.2017.01.008

Cited by 30 publications

(25 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The frost risk was much greater in the Southern Downs and Darling Downs sub-regions in Queensland, followed in decreasing order by northern New South Wales, New South Wales, Dawson Callide, and Central Queensland Highlands regions. The location membership of sub-regions is only slightly different to membership defined on the basis of drought [16]. For sowings on 15-April, the estimated annual losses in yield of chickpea averaged 31.3% in the Southern Downs and 26.1 % in the Darling Downs compared to much smaller losses in other sub-regions including northern New South Wales-Western Downs (10.5%), Dawson Callide (9.0%) and the lowest in Central Queensland Highlands (2.0%).…”

Section: Spatial and Temporal Trends In Frost Riskmentioning

confidence: 87%

“…This weather data is referred to as SILO weather data from here on. The coordinates of the 45 locations, description of their soil types, and water holding capacities have been reported by Chauhan, et al [16]. The 46th additional location was Oakey with a latitude of −24.4 • S and longitude of 151.7 E, respectively.…”

Section: Spatial and Temporal Frost Riskmentioning

confidence: 99%

“…The other temperature thresholds in the ≤−3 to <2 • C range were also considered relevant in order to account for consistent discrepancies in minimum temperature that could occur between the local and the SILO weather data due to elevation or spatial differences. The frost environments were classified according to a procedure developed by Chauhan, Allard, Williams, Williams, Mundree, Chenu and Rachaputi [16] for the 15-April sowing which had the highest frequency of post-flowering frost events. The procedure converts temporal trends into spatial sub-regional groups of locations.…”

Section: Spatial and Temporal Frost Riskmentioning

confidence: 99%

See 2 more Smart Citations

Frost Risk Management in Chickpea Using a Modelling Approach

Chauhan¹,

Ryan²

2020

Agronomy

View full text Add to dashboard Cite

Post-flowering frosts cause appreciable losses to the Australian chickpea industry. The Northern Grains Region (NGR) of Australia, which accounts for nearly 95% of chickpea production in Australia, is frequently subjected to such events. The objective of this study was to map frost risk in chickpea in the NGR and develop strategies to minimise the impacts of such risk. The Agricultural Production System Simulator (APSIM) modelling framework was used to determine spatial and temporal trends in post-flowering frost risk. The NGR could be divided into six broad sub-regions, each delineating locations with similar frost risk. The risk was nearly two to three times greater in the Southern Downs and Darling Downs sub-regions as compared to the Central Queensland Highlands, Dawson Callide, New South Wales, and Northern New South Wales–Western Downs sub-regions. There was an increasing trend in the frequency of frost events in the Southern Downs and New South Wales sub-regions, and a decreasing trend in the Central Queensland Highlands and Dawson Callide sub-regions, consistent with the changing climate of the NGR. In each sub-region, frost risk declined with delayed sowings, but such sowings resulted in simulation of reduced water limited yield potential (unfrosted) as well. The model output was also used to compute 10, 30, 50, and 70% probabilities of the last day of experiencing −3 to 2 °C minimum temperatures and identify the earliest possible sowings that would avoid such temperatures after flowering. Choosing the earliest sowing times with a 30% frost risk could help increase overall yields in environments with high frost risk. Simulations involving genotype x environment x management interactions suggested additional opportunities to minimise frost losses through the adoption of particular cultivars of differing phenology and the use of different agronomy in various environments of the NGR. The study indicates that there is considerable variation in frost risk across the NGR and that manipulating flowering times either through time of sowing or cultivar choice could assist in minimising yield losses in chickpea due to frost.

show abstract

Section: Spatial and Temporal Trends In Frost Riskmentioning

confidence: 87%

Section: Spatial and Temporal Frost Riskmentioning

confidence: 99%

Section: Spatial and Temporal Frost Riskmentioning

confidence: 99%

See 1 more Smart Citation

Frost Risk Management in Chickpea Using a Modelling Approach

Chauhan¹,

Ryan²

2020

Agronomy

View full text Add to dashboard Cite

show abstract

“…Crop models can help to identify locations and sowing windows for efficient screening of heat tolerance in particular target environments (e.g. Chauhan et al, 2017;Chenu et al, 2017;.…”

Section: A New Methods To Screen For Heat Tolerance At Matched Development Stagesmentioning

confidence: 99%

A robust field-based method to screen heat tolerance in wheat

Ullah

Christopher

Frederiks³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Wheat crops are highly sensitive to elevated temperatures, particularly during pollen meiosis and early grain filling. As the impact of heat stress greatly depends on the developmental stage of a crop, wheat germplasm ranking for heat tolerance in field experiments may be confounded by variation in developmental phase between genotypes at the time of heat events. Deploying an artificial-photoperiod-extension method (PEM) has allowed screening of diverse genotypes at matched developnemtal phases during natural heat events despite phenological varations. Irrigated experiments with 32 wheat genotypes were conducted in south-east Queensand, Australia with either (i) the PEM or (ii) conventional field plots. The Ppaired PEM and conventional field plot trials were sown at differentwith serial sowing dates from June to September.. In the PEM, plants were sown in single rows or in small plots and artificial supplemental lighting was installed at one end of each row/plot, extending day length to 20 h close to the lights. The intensity of supplementary lighting diminished as the distance from the lights increased, and induced a gradient of flowering times along each row/plot. Spikes of each genotype were tagged when they flowered. Late-sown crops received more heat shocks during early and/or mid-grainfilling than earlier sowings, and suffered significant yield losses. Significant genotypic differences in heat tolerance ranking were observed between PEM versus conventional plot screening. Individual grain weight of the tested wheat genotypes was strongy correlated in the PEM plots experiencing a similar degree of heat, but the correlation was either poor or negative in conventional plot trials. With PEM, we successfully quantified the impact post-flowering heat on individual grain weight of wheat genotypes with the heat events occurring precisely at a specific developmental stage. The PEM results produced robust field based rankings of genotypes for heat tolerance within trials experiencing similar heat events. This method promises to improve the efficiency of heat tolerance field screening, particularly when comparing genotypes of different maturity types.

show abstract

“…Over the last 15 years, crop models have been used to characterize stress such as drought, heat, and nitrogen stress in many crops (e.g. wheat, barley, sorghum, maize, rice, field pea, and chickpea) in various regions including Australia, Europe, Brazil, China, and India ( Chapman et al , 2000 ; Chapman, 2008 ; Heinemann et al, 2008 , 2011 , 2015 ; Chenu et al , 2009 b , 2011 , 2013 , Sadras et al , 2012 ; Chauhan et al , 2013 , 2014 , 2017 ; Kholova et al , 2013 ; Challinor et al , 2014 ; Harrison et al , 2014 ; Chenu, 2015 ; Lake et al , 2016 ; Ly et al , 2017 ). While these studies focused on characterizing local, regional, or national stress for short- or long-term climates, there is a need to better understand projected stress for future climate scenarios, to assist the development of adaptation policies and solutions.…”

Section: Introductionmentioning

confidence: 99%