Optimality-based modelling of wheat sowing dates globally

Abstract

Sowing dates are currently an essential input for crop models. However, in the future, the optimal sowing time will be affected by climate changes and human adaptations to these changes. A better understanding of what determines the choice of wheat type and sowing dates is required to be able to predict future crop yields reliably. This study was conducted to understand how climate conditions affect the choice of wheat types and sowing dates globally. We develop a model integrating optimality concepts for simulating gross primary production (GPP) with climate constraints on wheat phenology to predict 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 yields. The 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 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, then predict their changes under future climate scenarios to gain insight into the impacts of climate change. Cold-season temperatures are the major determinant of sowing dates in the extra-tropics, whereas the seasonal cycle of monsoon rainfall is important in the tropics. Our model captures the timing of reported sowing dates, with differences of less than one month over much of the world; maximum errors of up to two months occur in tropical regions with large altitudinal gradients. Discrepancies between predictions and observations are larger in tropical regions than temperate and cold regions. Slight warming is shown to promote earlier sowing in wet areas but later in dry areas; larger warming leads to delayed sowing in most regions. These predictions arise due to the interactions of several influences on yield, including the effects of warming on growing-season length, the need for sufficient moisture during key phenological stages, and the temperature threshold for vernalization of winter wheat. The integration of optimality concepts for simulating GPP with climate constraints on phenology provides realistic predictions of wheat type and sowing dates. The model thus provides a basis for predicting how crop calendars might change under future climate change. It can also be used to investigate potential changes in management to mitigate the negative impacts of climate change.

Topics & Concepts