How can the barley module of Flower Power be used?
The barley module of the popular Flower Power tool is available now. This is a decision support tool to assist growers understand the relative difference in maturity between barley varieties (as determined by duration to awn emergence, Z49) when sown at different seeding dates for specific sites.
This module enables the user to compare date of awn emergence (a surrogate for flowering) predictions for a number of barley varieties over a range of sowing dates and to compare the predictions for normal, very cold or very warm seasons.
Development is a complex phenomenon. Early or late break of season, generally colder or warmer seasons, a northern or a southern location, freezing or high temperatures are all conditions that can influence the day when a given variety will flower.
How does barley module of Flower Power benefit profitability?
Opportunities exist to maximise yield benefits if the timing of barley flowering can be identified by growers. For this reason, growers and consultants value tools that can predict flowering dates of varieties at different sowing dates at a specific location.
Variety choice and sowing date are key to maximising yield potential. Earlier sowing should mean better water use efficiency and higher yields. Matching variety to sowing time aims to maximise growth, minimise the likelihood of frost after flowering and avoid the incidence of high temperature events during grain fill.
The barley module of Flower Power has been developed by modifying the statistical model (DM) developed by Sharma and D’Antuono (2011), which provides the predictions for the wheat module of the Flower Power tool. The barley module of Flower Power has been developed for a wide range of sowing dates (10 April-10 June) and the most commonly grown barley varieties in Western Australia. The model was created and is updated with data collected from field trials measuring date of awn emergence of a number of varieties at a number of locations and times of sowing over a number of seasons.
The below image shows an example of the outputs when comparing multiple varieties sown on the same date at Kulin. The predicted date of Z49 is separated into colder seasons (deciles 1-3), normal seasons (deciles 4-7) and warmer seasons (deciles 8-10). The median predicted date of Z49 is presented (50%), as is the 10th and 90th percentile. The model can also be used to compare the date of Z49 for the same variety sown on multiple dates.
The Graphic-predictions tab shows a graphical representation of the same information, with the dots showing the median predicted date of awn emergence (50th percentile), and the tails extending to the dates of the 10th and 90th percentile for each decile.
The Graphic-frost/heat risk tab is a graphical representation of the risk of frost and heat stress for this location. The graph presents the likelihood (based on historical data) of the temperature exceeding 30°C or 33°C prior to a certain date, and of dropping below 0°C, 2°C or 5°C at some point after a certain date. The purpose of this tool is to allow growers to understand the risk of extremes in temperature occurring in their location and allow better planning of flowering time to minimise frost or heat risk.
How to use this graph:
- The optimum flowering period for a given location is generally defined by the time in which frost and heat events around flowering can be avoided, assuming sufficient biomass accumulation and allowing sufficient grain filling period (without drought stress). Crops that are flowering are particularly sensitive to frost and heat stress, which can greatly reduce grain number and yield.
- This graph presents the frost risk AFTER, and heat risk BEFORE, a given date at a given location.
- While frost and heat events can occur throughout the season, targeting flowering for the period that is late enough to minimise frost risk, but early enough to minimise heat risk is important to minimise the likelihood of these stresses.
- Regional knowledge is also important to allow sufficient biomass production and sufficient time for grain filling before terminal drought
Sharma DL and D’Antuono MF (2011) Predicting flowering dates in wheat with a new statistical phenology model. Agronomy Journal 103: 221-229.