ORCID Profile
0000-0003-2447-3892
Current Organisation
James Cook University
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Publisher: Springer Science and Business Media LLC
Date: 10-12-2013
Publisher: Wiley
Date: 18-04-2013
DOI: 10.1002/IRD.1740
Publisher: Wiley
Date: 12-08-2021
DOI: 10.1111/JAC.12545
Abstract: There is high confidence that climate change has increased the probability of concurrent temperature‐precipitation extremes, changed their spatial‐temporal variations and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily data spanning the period 1889‐2019 (131 years) were extracted from SILO gridded dataset at 700 grid cells (1◦ × 1◦) across Australia to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot‐dry (CHD) events based on an ‘And’ hazard scenario (hotter than a threshold ‘And’ drier than a threshold). CHD extremes were defined as years with joint return periods of longer than 25 years calculated over the period 1889‐2019. Mann‐Kendall nonparametric tests were used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. Results showed a general cooling‐wetting trend over 1889‐1989. Significant increasing trends were detected over 1990‐2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). A significant increase in the association between temperature and precipitation was identified at various temporal scales. While the frequency of CHD extremes was mostly stable over 1889‐1989, it significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south‐east and southwest.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 02-2018
Publisher: Wiley
Date: 05-12-2022
DOI: 10.1111/JAC.12575
Abstract: We used SUFI‐2 for the first time to calibrate the phenology module of the APSIM‐wheat model for 10 spring wheat cultivars cultivated in northeast Australia (south‐eastern Queensland). Calibration resulted in an average root mean square error (RMSE) of 5.5 days for developmental stages from stem elongation up to flowering. Projections from 33 climate models under the representative concentration pathway 8.5 were used for simulations at 17 sites. Using adapted sowing times, we simulated significantly shorter crop cycles and grain yield improvements for the period 2036–2065 relative to 1990–2019 for three selected cultivars (Hartog, Scout and Gregory). Photoperiod and vernalisation sensitivities were shown to be the largest and smallest contributors to total uncertainties in the simulated flowering day and grain yield, respectively. Uncertainties in climate models had a relatively minor contribution to the total uncertainties in the simulated values of target traits. This contribution significantly increased when climate change impact on the target traits was quantified.
Publisher: Wiley
Date: 12-01-2020
DOI: 10.1111/JAC.12388
Publisher: Elsevier BV
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 28-01-2021
DOI: 10.1101/2021.01.28.428676
Abstract: We used SUFI-2 for the first time to calibrate the phenology module of the APSIM-wheat model for 10 spring wheat cultivars cultivated in northeast Australia (south-eastern Queensland). Calibration resulted in an average RMSE of 5.5 d for developmental stages from stem elongation up to flowering. Projections from 33 climate models under the representative concentration pathway 8.5 were used for simulations at 17 sites. Using adapted sowing times, we simulated significantly shorter crop cycles and grain yield improvements for the period 2036-2065 relative to 1990-2019 for three selected cultivars (Hartog, Scout and Gregory). Photoperiod and vernalisation sensitivities were shown to be the largest and smallest contributors to total uncertainties in the simulated flowering day and grain yield. Uncertainties in climate models had a relatively minor contribution to the total uncertainties in the simulated values of target traits. This contribution significantly increased when climate change impact on the target traits was estimated.
Publisher: Wiley
Date: 22-07-2020
DOI: 10.1002/IRD.2487
Publisher: Wiley
Date: 21-01-2020
DOI: 10.1002/IRD.2399
Publisher: Elsevier BV
Date: 07-2023
Publisher: Oxford University Press (OUP)
Date: 2021
DOI: 10.1093/INSILICOPLANTS/DIAB006
Abstract: Limited-transpiration rate at high evaporative demand (‘LTR’ trait) has potential to improve drought adaptation, crop water productivity and food security. The quantification of the implications of LTR for water consumption, biomass accumulation and yield formation requires the use of dynamic crop modelling to simulate physiological and environmental processes and interactions in target environments. Here, a new transpiration module was developed for the Agricultural Production Systems sIMulator (APSIM NextGen) and used to simulate atmospheric and edaphic water stress on wheat crops. This module was parameterized with (i) data from a lysimeter experiment assessing genotypic variability in the LTR trait for four genotypes contrasting in transpiration efficiency, and with (ii) a more pronounced response to high evaporative demand. The potential of the LTR trait for improving crop productivity was investigated across the Australian wheatbelt over 1989–2018. The LTR trait was simulated to allow an increase in national yield by up to 2.6 %, mostly due to shift in water use pattern, alleviation of water deficit during grain filling period and a higher harvest index. Greatest productivity gains were found in the north-east (4.9 %, on average) where heavy soils allow the conserved water with the LTR trait to be available later at more critical stages. The effect of the LTR trait on yield was enhanced under the future climate scenario, particularly in the north-east. Limiting transpiration at high evaporative demands appears to be a promising trait for selection by breeders, especially in drought-prone environments where crops heavily rely on stored soil moisture.
Publisher: Springer Science and Business Media LLC
Date: 08-2021
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 30-01-2014
Publisher: Elsevier BV
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 29-04-2014
Publisher: Springer Science and Business Media LLC
Date: 12-10-2018
DOI: 10.1038/S41467-018-06525-2
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO 2 offering no yield benefit in these years.
Publisher: IWA Publishing
Date: 02-12-2013
DOI: 10.2166/WCC.2013.168
Abstract: Six different weather generator models were compared. The first two models (M1 and M2) use a first-order autoregressive daily model and the third model (M3) uses a newly proposed semi-parametric method to reproduce the correlation and autocorrelation of the variables. Three other models (M1-2, M2-2 and M3-2) are the combinations of these models with an adjustment algorithm for the low-frequency variances (SL). The comparison revealed that M1-2 model (daily weather generator with the SL adjustment algorithm) and the M2-2 model (daily weather generator in combination with a monthly weather generator and the SL adjustment algorithm) are the best models in the study area. All the studied models have acceptable performance in relation to the shape of the probability distribution functions. Three first models have deficiencies in relation to the inter-annual standard deviations. The M2 and M3 models, in which the high-frequency standard deviation (SH) is used instead of the total standard deviation values (ST), slightly underestimated these inter-annual variations. But, the performance of the M1 model is considerably poorer than the other two models. The results revealed that the adjustment of the inter-annual standard deviations improves model performance. Moreover, the newly proposed algorithm has the potential for multi-station simulations.
Publisher: MDPI
Date: 24-12-2019
Publisher: Springer Science and Business Media LLC
Date: 02-2019
Publisher: Elsevier BV
Date: 02-2022
Publisher: MDPI
Date: 24-12-2019
Location: Iran (Islamic Republic of)
No related grants have been discovered for Brian Collins.