Flowering dates comparison across different cultivars

The table presents the most probable flowering dates, computed from a statistical model, under different weather conditions: warm, usual, and cold. These predictions should not be interpreted as the definitive date on which the cereal will flower. Additionally, alongside the tabular format, the predictions under the usual weather condition is graphically depicted against the sowing dates.

The statistical model requires an estimate of the heat accumulation rate to make predictions. The designations ‘warmer,’ ‘usual,’ and ‘colder’ correspond to the 0.15, 0.5, and 0.85 quantiles, respectively, of the heat accumulation rate from 2014 to 2023.

Helpful tips

• The table can be sorted by clicking on the column names.
• The lines for each cultivar can be toggled on/off by clicking on the cultivar name in the legend.
• Hover to the top right of the plot for a range of useful tools.

Example

A Scepter wheat crop sown on 8^th May in Borden is most likely to flower on 6^th Sept in a warm condition, 8^th Sept in a usual condition, and 11^th Sept in a cold condition.

Predictions based on the weather data of the past 10 years

This graph illustrates the predicted flowering dates of the first cultivar on the selection list. Each of the ten coloured curves represents the predicted flowering dates based on historical weather data for the past ten years.

The underlying statistical model requires an estimate of the heat accumulation rates to make predictions. The purple and yellow curves represent the predictions based on the heat accumulation rates of the coldest and warmest years in the past ten years, respectively. The black curve shows the prediction based on the median heat accumulation rate of the past ten years.

Weather index

The heat accumulation rate roughly indicates the ‘warmth’ of a year. The weather index shows the heat accumulation rates standardized across the past ten years.

For example, the ‘warmest’ year has the highest heat accumulation rate in the past ten years and thus has a weather index of 1 (yellow). Conversely, the ‘coldest’ year has the lowest heat accumulation rate in the past ten years and has a weather index of 0 (purple).

Helpful tips

• Hover over the top right of the plot for a range of useful tools.

Technical details

The flowering response curves were modelled using semiparametric models and fitted with a penalised splines estimator provided in the mgcv R package (Wood 2011).

The model employs ‘days to flowering’ as the response variable, with the sowing day of the year as the time covariate. This simplifies the two-step process as used in the previous versions of FlowerPower which predicts the ‘day-degrees’ required for flowering and back-transforms the ‘day-degrees’ to a time scale (Sharma and D’Antuono, 2011).

A key assumption in the model is that the environment is only characterised by the heat accumulation rate between 1 April and 31 October. The ‘sowing day’ interacts with ‘cultivar’ and the heat accumulation rate — this implies that the shapes of flowering response curves differ among cultivars and environments. Additionally, the latitude and longitude of the site are also among the model covariates, but they will only affect the vertical placement of the flowering response curves.

Disclaimer

This model aims to give an indication of how different cultivars compare for flowering dates based upon historical data. However, as with all statistical models, this app is not 100% accurate but is expected to give a reasonable approximation.

The Chief Executive Officer of the Department of Primary Industries and Regional Development and the State of Western Australia and their employees (collectively and individually referred to as DPIRD) accepts no liability whatsoever, by reason of negligence or otherwise, arising from any use or release of information in, or referred to in or linked to, this website, or any error, inaccuracy or omission in the information.

Reference

Sharma, D.L. and D’Antuono, M.F., 2011. Predicting flowering dates in wheat with a new statistical phenology model. Agronomy Journal, 103(1), pp.221-229.

Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), pp.3-36.

Acknowledgement

FlowerPower has been developed from a Microsoft Excel program into a robust web-based system by Mario D’Antuono (until 2018) with guidance from Dr Darshan Sharma. Since 2019, Kenyon Ng has been maintaining and upgrading FlowerPower with support from Dr Kawsar Salam and Dr Dean Diepeveen. Many thanks to DPIRD agronomy researchers who provided trial data (some co-funded by GRDC) and feedback on the user interface.

The weather data were obtained from the SILO database hosted by the Queensland Department of Environment and Science (https://www.longpaddock.qld.gov.au/silo/point-data/) under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.