Sirius: a mechanistic model of wheat response to environmental variation

Jamieson, P. D.; Semenov, Mikhail A.; Brooking, I.R.; Francis, G. S.

doi:10.1016/s1161-0301(98)00020-3

Cited by 307 publications

(207 citation statements)

References 39 publications

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“…The impact statistics included the probability of heat stress around flowering, which can substantially reduce final grain yield, and the drought stress index. To compute these statistics, the Sirius wheat simulation model (Jamieson et al 1998, Jamieson & Semenov 2000 was used to predict wheat growth for a selection of local-scale climate scenarios based on the output from the Hadley Centre regional climate model, the socalled UKCIP02 climate predictions (Hulme et al 2002, Semenov 2007. It was shown that, despite higher temperature and lower summer precipitation predicted in the UK for the 2050s, the reduction in simulated grain yield due to drought stress is predicted to be smaller than that at present, because wheat will mature earlier in a warmer climate and avoid severe summer drought.…”

Section: Example Of Impact Assessmentmentioning

confidence: 99%

Use of multi-model ensembles from global climate models for assessment of climate change impacts

2010

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Multi-model ensembles of climate predictions constructed by running several global climate models for a common set of experiments are available for impact assessment of climate change. Multi-model ensembles emphasize the uncertainty in climate predictions resulting from structural differences in the global climate models as well as uncertainty due to variations in initial conditions or model parameterisations. This paper describes a methodology of using multi-model ensembles from global climate models for impact assessments which require local-scale climate scenarios. The approach is based on the use of a weather generator capable of generating the localscale daily climate scenarios used as an input by many process-based impact models. A new version of the LARS-WG weather generator, described in the paper, incorporates climate predictions from 15 climate models from the multi-model ensemble used in the IPCC Fourth Assessment Report (AR4). The use of the AR4 multi-model ensemble allows assessment of the range of uncertainty in the impacts of climate change resulting from the uncertainty in predications of climate. As an example, the impact of climate change on the probability of heat stress during flowering of wheat, which can result in significant yield losses, was assessed using local-scale climate scenarios in conjunction with a wheat simulation model at 4 European locations. The exploitation of much larger perturbed physics ensembles is also discussed.

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Section: Example Of Impact Assessmentmentioning

confidence: 99%

Use of multi-model ensembles from global climate models for assessment of climate change impacts

2010

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“…SIRIUS is a wheat simulation model that calculates biomass production from intercepted photosynthetically active radiation and grain growth from simple partitioning rules (Jamieson et al, 1998). The model accounts for the enhanced CO 2 effect by linearly increasing radiation use efficiency (RUE), so that for a doubling of CO 2 air concentration the RUE is increased by 30% (Jamieson et al, 2000).…”

Section: Durum Wheat Impact Model Calibration and Validationmentioning

confidence: 99%

Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region

Ferrise

Moriondo

Bindi

2011

Nat. Hazards Earth Syst. Sci.

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Abstract.Recently, the availability of multi-model ensemble prediction methods has permitted a shift from a scenariobased approach to a risk-based approach in assessing the effects of climate change. This provides more useful information to decision-makers who need probability estimates to assess the seriousness of the projected impacts.In this study, a probabilistic framework for evaluating the risk of durum wheat yield shortfall over the Mediterranean Basin has been exploited. An artificial neural network, trained to emulate the outputs of a process-based crop growth model, has been adopted to create yield response surfaces which are then overlaid with probabilistic projections of future temperature and precipitation changes in order to estimate probabilistic projections of future yields. The risk is calculated as the relative frequency of projected yields below a selected threshold.In contrast to previous studies, which suggest that the beneficial effects of elevated atmospheric CO 2 concentration over the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying, the results of this study are of greater concern.

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“…However, we have developed more complete DSSs [15] based on simulation models such as Sirius [16] that deal with the crop in mode detail, keep a proper water balance including an estimate of the actual soil moisture deficit (ASMD) through time.…”

Section: Arable Cropsmentioning

confidence: 99%

Improving irrigation practice in New Zealand

Martin¹,

Thomas²,

Bloomer³

et al. 2006

WIT Transactions on Ecology and the Environment, 96

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The Canterbury Plains are the major cropping area, and an increasingly important dairying area, in New Zealand. Irrigation is necessary on most of the Plains to achieve high productivity. However, competition for limited water resources means that farmers increasingly have to justify their demands for water. Farmers need to know when to irrigate, how much water to apply, and the yield penalty if the crop is not irrigated. For annual arable crops, we have used the maximum potential soil moisture deficit (MPSMD) model and a large rainshelter to answer these questions. Total or economic yield of nearly all the crops tested decreased linearly as MPSMD increased, regardless of the timing of drought. The slope of the regression line is the yield loss with increasing MPSMD. For ryegrass dairy pastures, which grow throughout the year, the model has been adapted to simulate actual soil moisture changes. As most farmers do not measure pasture production directly, we have developed a model for potential pasture production, and a simple relationship between grass growth restrictions and actual soil moisture deficits, that fits experimental farm data. Farmers can also reduce the amount of water they apply by improving the water application efficiency of their irrigation systems. Protocols had been developed to identify causes of poor performance in the field, and how these may be addressed. This information is being used by farmers to improve their irrigation decisions, and irrigation system performance, and has been included in decision support systems for arable and vegetable farmers, and is being developed for dairy farmers.

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Sirius: a mechanistic model of wheat response to environmental variation

Cited by 307 publications

References 39 publications

Use of multi-model ensembles from global climate models for assessment of climate change impacts

Use of multi-model ensembles from global climate models for assessment of climate change impacts

Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region

Improving irrigation practice in New Zealand

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