Stabilising the flowering time of wheat

Reduced autumn rainfall has been attributed to anthropogenic climate change (Cai et al., 2012; Murphy and Timbal, 2008) and is likely to persist. In addition to declining autumn rainfall, the average farm size is increasing, highlighting the demand for new combinations of management and genetics to stabilise flowering time during the optimal period in order to overcome the observed yield decline (Kirkegaard and Hunt, 2010), and to maintain the viability of SEA wheat farms and their contribution to global food security. 

An opportunity exists to overcome these weather and management challenges  by using summer and autumn rain to establish wheat crops earlier than is currently practised (Hunt and Kirkegaard, 2011). The benefits of early sowing are increased frequency of planting opportunities (allows more area to be sown and flower on time) (Hunt et al., 2012), increased yield due to reduced water loss to evaporation (Batten and Khan, 1987; Eastham et al., 1999), deeper roots (Incerti and Oleary, 1990), a longer yield formation phase (Hunt et al., 2012), higher water-use efficiency (Gomez-Macpherson and Richards, 1995; Richards et al., 2014) and greater interception of solar radiance (Stapper and Harris, 1989). Commonly grown cultivars, if planted in early autumn, incur a large frost risk due to earlier flowering, therefore cultivars that allow early establishment need to be slower developing (Hunt et al., 2013). These cultivars exist though are rarely used by growers and fail to express their value in national variety trials as they are generally planted too late in the season. 

This study uses field and model simulation to identify the optimal flowering period for sites across SEA , and identifies a genotype x management (G x M) strategy for stabilising yield and flowering at a field site.

Method

Pre-experimental modelling 

In this study, the widely validated cropping systems model Agricultural Production Systems simulator (APSIM), version 7.6 was used to simulate wheat yield and flowering date, with reductions in yield applied for frost and heat damage based on air temperatures during sensitive periods. Simulated crops were sown at weekly intervals from April 1 to July 15 of each year. The relationship between flowering date and grain yield was established for 28 locations using 51years (1963-2013) of climate records. We defined optimal flowering periods (OFP) as the flowering period which was associated with a mean yield of ≥ 95% of maximum yield from the combination of 51 seasons and 16 sowing dates.

Field trial

In the 2015 and 2016 growing seasons, a field trial was established in Temora, NSW. The trial consisted of 18 cultivars of varying release dates and development types (results from only four genotypes are presented in this paper, Table 1), and four times of sowing (17 April, 27 April, 7 May and 15 May in 2015, and 14 April, 26 April, 6 May, and 15 May in 2016). Chemical fertilisers and pesticides were applied such that nutrient limitations, weeds, pests or diseases did not limit yield. Flowering time was recorded as the time when 50% of the spikes in each plot had visible anthers. A mean flowering stability index was calculated by dividing the thermal time range in flowering for each cultivar by the thermal time range in sowing dates for each year. Yields were measured by machine harvest of middle rows of plots only. 

Table 1. Alleles of the major genes which govern response to vernalisation (Vrn; v=sensitive, a=insensitive) and photoperiod (Ppd; b=sensitive, a=insensitive) in four of the cultivars used in the Temora field experiment in 2015 and 2016.

Results and discussion

Through APSIM simulation, it was determined that the optimal flowering time at Temora, NSW, is from the 25 September to 10 October (Figure 1). Flowering times and thus yields of cultivars that possessed winter habit (RAC2341 and Wedgetail) were more stable than spring cultivars (Gregory and Condo, Figures 2 and 3, Table 2). 

In 2016, the fast spring cultivar Condo yielded most when sown on 15 May at 6.8t/ha, but this was the only time of sowing to flower during the optimal period (Figure 2). In 2015, autumn and winter were much cooler which slowed development and Condo flowered during the optimal period from three times of sowing (Figure 3). In 2015, extreme high temperatures in the first week of October gave a large advantage to flowering at the start of the optimal window, which Condo was able to do from the 26 April sowing date. In 2016, mid-spring cultivar Gregory also yielded highest when sown on 15 May with 6.6t/ha, but two out of four time of sowings flowered at the optimal time. In 2015, three time of sowings of Gregory flowered in OFP. In 2016, the fast winter cultivar RAC2341 achieved the highest yield out of the four cultivars at 7t/ha sown on 6 May, and three time of sowings flowered within the optimal period. In 2015, RAC2341 yielded highest when sown on 14 April with 5.5t/ha and three time of sowings flowering within the OFP, however the last time of sowing (15- May) only flowered just outside the OFP (11 October). In both seasons, the fast winter cultivar RAC2341 outyielded mid-winter wheat Wedgetail at all time of sowings. RAC2341’s flowering time is similar to that of Wedgetail, but it was able to more consistently flower during the optimum period, meaning it is better adapted to the Temora environment.

Figure 1. The optimal flowering period of wheat determined by APSIM simulation for Temora, NSW. Grey lines represent the standard deviation of the frost and heat reduced mean yield (kg/ha), black line represents the mean yield (kg/ha). Grey columns are the estimated optimal flowering period defined as 95% of the mean yield for the 51 year simulation (1963-2013).

Figure 2. The yield and flowering dates of four cultivars sown on four times of sowing (14 April, 26 April, 6 May, and 15 May) in the 2016 growing season. Condo (round), Gregory (square), RAC2341 (triangle) and Wedgetail (broken line), LSD (black line), grey column is the optimal flowering period as determined by APSIM in Figure 1. Cultivar. Time of sowing, P-value <0.001.

Figure 3. The yield and flowering dates of four cultivars sown on four times of sowing (17 April, 27 April, 7 May, and 15 May) in the 2015 growing season. Condo (round) Gregory (square), RAC2341 (triangle) and Wedgetail (broken line), LSD (black line), grey column is the optimal flowering period as determined by APSIM in Figure 1. Cultivar. Time of sowing, P-value <0.001.

The results suggest that when the opportunity arises on farm, for example, an early season break or high summer rainfall allowing early sowing, growers can use cultivars with slow development, such as RAC2341, which will flower within the optimal period from a broad range of sowing dates, thus minimising the combined damage from frost, heat and water stress and maximising yield. 

Table 2. The mean flowering date stability, mean yield across the four times and standard deviation of yield for each cultivar for the two year experiment in the 2015 and 2016 seasons. Higher number indicates a cultivar has a less stable flowering date over a range of sowing dates. 

Conclusion

New G x M strategies which avoid water and temperature stress need to be identified to ensure that flowering of wheat occurs at the optimal period to avoid current and future yield losses associated with autumn rainfall decline and extreme spring events. Here we have described one G x M strategy which stabilises both flowering time and yield, that is, cultivars that are sensitive to vernalisation which can be sown early or over a wide sowing window to achieve the optimal flowering periods and maximise yield. 

Acknowledgements

The scholarship for this work was provided through the GRDC and their support is gratefully acknowledged.
The authors also gratefully acknowledge field support from CSIRO staff Tony Swan, Brad Rheinhiemer, Melanie Bullock and Laura Goward. 

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Contact details 

Bonnie Flohr
CSIRO Agriculture, PO Box 1600, Canberra ACT 2600
0475 982 678
bonnie.flohr@csiro.au
@BonnieFlohr