Simulating wheat sowing dates based on optimality principle

Sowing dates are essential input for crop models that simulate wheat production. Currently,many crop models use a pre-defined wheat calendar based on historical observations as input (Minoli et al., 2019), whereby the dates of sowing and harvest are fixed and do not respond to changes in climate, and this means that model projections are highly uncertain (Asseng et al., 2013). In the future, the optimal sowing time will be affected by climate changes and human adaptations to these changes. Although some crop models include crop specific parameters for sowing (Waha et al., 2012) or a relationship between sowing dates and climate, which could simulate sowing dates, (van Bussel et al., 2015), the use of pre-defined crop calendars or empirical relationships stems from an imperfect understanding of the role of climate in determining sowing dates for optimal production and thus limited ability to model this dynamically (Dobor et al., 2016). Therefore, a better understanding of what determines the choice of wheat type and wheat sowing dates globally is required to be able to predict future crop yields reliably.

With the help from Dr. Wang, Prof. Prentice and Prof. Harrison, we have been developing a new wheat model (named PC, productivity for crop), which integrating optimality concepts for simulating gross primary production (Wang et al., 2017), mass-balance equations for predicting leaf area index and empirical functions relating carbon allocation (Qiao et al., 2020; Qiao et al., 2021). In the study, we coupled original version of PC model (Qiao et al., 2021) with additional climate constraints on wheat phenology to predict sowing dates globally (Fig. 1). Since the time between sowing and harvest reflects the accumulated temperature during the growing season, temperature constraints on sowing depend on whether wheat is grown in a climate where it must experience a prolonged cold period before sprouting to minimise the risk of losses through frost damage. In regions with highly seasonal rainfall, the need to avoid crop damage because of intense rains imposes an additional constraint on sowing dates. We assume that wheat could be sown at any time with suitable climate conditions and farmers would select a sowing date that maximises grain yields. The PC model is run starting on every possible climatically suitable day, determined by climate constraints associated with low temperature and intense precipitation. The optimal sowing date is determined by the day which gives the highest yield in each location.

We evaluate the simulated optimal sowing dates with data on observed sowing dates created by merging census-based datasets and local agronomic information. The validation shows (Fig. 1) that our model captures the timing of reported sowing dates, with differences between estimated and observed sowing dates of less than one month over much of the world. This agreement between prediction and observation indicates that the PC model provides realistic predictions of wheat type and sowing dates, furtherly, implies that the optimality-based approach used in the study could be used to predict optimal wheat sowing dates under different future climate scenarios and thus provide a more secure basis for assessments of the need and potential of changing management practices to mitigate the negative impacts of climate change on wheat growth.

Figure 1 The figure of modelling scheme (left panel) and sowing dates prediction (right panel).

This manuscript related to simulating sowing dates is under review. We will provide the DOI when it is accepted.


Asseng, S. et al., 2013. Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3(9): 827-832.

Dobor, L. et al., 2016. Crop planting date matters: Estimation methods and effect on future yields. Agr Forest Meteorol, 223: 103-115.

Minoli, S., Egli, D.B., Rolinski, S. and Muller, C., 2019. Modelling cropping periods of grain crops at the global scale. Global and Planetary Change, 174: 35-46.

Qiao, S.C., Wang, H., Prentice, I.C. and Harrison, S.P., 2020. Extending a first-principles primary production model to predict wheat yields. Agr Forest Meteorol, 287: 107932.

Qiao, S.C., Wang, H., Prentice, I.C. and Harrison, S.P., 2021. Optimality-based modelling of climate impacts on global potential wheat yield. Environmental Research Letters, 16(11): 114013.

van Bussel, L.G.J., Stehfest, E., Siebert, S., Müller, C. and Ewert, F., 2015. Simulation of the phenological development of wheat and maize at the global scale. Global Ecology and Biogeography, 24(9): 1018-1029.

Waha, K., van Bussel, L.G.J., Muller, C. and Bondeau, A., 2012. Climate-driven simulation of global crop sowing dates. Global Ecology and Biogeography, 21(2): 247-259.

Wang, H. et al., 2017. Towards a universal model for carbon dioxide uptake by plants. Nat Plants, 3(9): 734-741.


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