It is proposed that there is an evidence-based argument that the risk due to the uncertainty component of climate change (CC) can be mitigated by building flexibility into cropping systems through strategic pre-season planning.
The hypothesis is that useful flexibility can be generated through at least three approaches:
- Growing a variety that exhibits desirable plasticity of yield components.
- Tactical population control where growers could kill a part of the crop under water deficient conditions.
- Strategically structuring plant populations to maximise crop buffering.
These hypothesised strategies are based on a review of previous agronomy research in Western Australia and relevant literature.
Plasticity of yield components underpins the yield stability of most predominant wheat varieties in WA (Sharma et al 2008). For example, Wyalkatchem and Carnamah show remarkable high plasticity for mean kernel weight. Apparently, this plasticity maximises the capture of resource-limited potential (for example, rainfall) irrespective of the sink status.
High seed rates have been advocated as an integrated weed management practice but some experimental data also indicates a disadvantage to high plant populations under drying and rapidly warming conditions. This highlights the need to optimise plant population in areas where climate is changing towards consistently drier and warmer winter growing conditions. Growing mixtures of varieties with and without herbicide tolerance genes (for example, imidazolinone tolerance) could be a tactic for a need-based reduction of plant population. The catch in this case is the ability to kill unwanted plants as soon as possible in order to save water for surviving plants.
Based on the concept of population buffering (Allard and Bradshaw 1964), varietal mixtures can reduce yield losses associated with uncertainties of seasonal weather forecasts (for example, forecasts of drought, late rains, frost and high temperature). For example, varieties differing for phenology and growth pattern may be mixed. Some new phenology models (such as, Sharma and D’Antuono 2011) can distinguish varieties for flowering date response to warmer or colder season types.
A hypothetical estimate shows that the yield benefit from post-frost water use can be up to three times for an appropriate mixture in comparison to a monoculture crop of a frost damaged variety. We recognise there will be issues with yield penalty due to late maturity but suitable varieties for such shandying need to be carefully identified. Our field trials have futher shown that there is no economic or yield disadvantage if the terminal soil moisture conditions turn out to be rather favourable. Three variety mixtures produced the highest economic returns in our trials. However, proper decision tools on determining location and season specific composition of such variety mixtures is required.
Project DAW00202 'Demonstrating adaptation to climate change in the wheatbelt of Western Australia through innovative on-farm and virtual farm approaches' was part of the National Adaptation and Mitigation Initiative (NAMI), funded by the Department of Primary Industries and Regional Development, Grains Research and Development Corporation and the Australian Government's Climate Change Research Program. Thanks are also due to Geraldine Pasqual, Caroline Peek, and Glen Riethmuller for comments and suggestions.
Allard, RW & Bradshaw, AD 1964, 'Implications of genotype-environmental interactions in applied plant breeding', Crop Science, vol.4, pp.503-508.
Fletcher, A., Ogden, G. & Sharma, D 2019, 'Mixing it up - wheat cultivar mixtures can increase yield and buffer the risk of flowering too early or too late', European Journal of Agronomy, vol.103, pp.90-97.
Sharma, DL., D'Antuono, MF., Anderson, WK., Shackley, BJ., Saicou-Kunesch, CM & Amjad, M 2008, 'Variability of optimum sowing time for wheat yield in Western Australia', Australian Journal of Agricultural Research, vol.59, pp.958-970.
Sharma, DL & D’Antuono, MF 2011, 'Predicting flowering dates in wheat with a new statistical phenology model', Agronomy Journal, vol.103, pp.221-229.