Understanding and reproducing regional diversity of climate impacts on wheat yields: current approaches, challenges and data driven limitations

Uncertainties in the impact assessments of climate anomalies and extremes on crop yields arise from the complexity and the limited knowledge of the involved biophysicalprocesses, thevariableaccuracyof themeteorological and field data, and the limited information on the local agronomical practices. A recent perspective paper (Siebert et al 2017b) highlighted the complexity of this topic, the limitations of simplified statistical approaches such as the one proposed by Zampieri et al (2017), and the need for systematic comparisonwithprocess-based cropgrowth models. Here we provide a further perspective on the (dis-) advantages and main limitations of both approaches, especially for global scale applications, and explore how combining methods can improve our understanding of weather and climate impacts on yields in the diverse regions of the world. Process-based crop models simulate crop growth and phenological stages, generally at daily time intervals as a response to daily meteorological inputs (e.g. air temperature, precipitation, radiation, wind and relative humidity). These models are used at both the field scale (e.g. Ceglar et al 2011, Wallach et al 2013) and the regional/global scale over gridded domains (e.g. Rosenzweig et al 2014, Frieler et al 2017). Since the formulation of the involved biophysical processes cannot be derived from first principles, it is mostly based on empirical parameterizations that need to be calibrated with comprehensive sets of information and accurate observations of the driving variables. These variables are usually available only at the field scale and for a limited number of regions in the world (Levis 2014). Among the most important adjustable parameters in crop models are the reference temperatures conducive to plant growth and the optimal range of temperatures determining the final yields (Wang et al 2017). Wheat temperature-related critical physiological thresholds widely differ depending on the variety, even without considering the additional uncertainties related to the combined effect of water stress (e.g. Stone and Nicolas 1995a, Rane and Nagarajan 2004, Spiertz et al 2006, Farooq et al 2011, Dias et al 2011). Therefore, at the regional and global scales, several assumptions are necessary due to the lack of information on crop varieties, their spatial distribution and precise agro-management practices, such as irrigation and fertilization (Jones et al 2017). Several processes including the physiological effects of climate extremes, such as heat stress and drought and their non-linear combination, are still not reliably reproduced in crop models (Rosenzweig et al 2014, Siebert et al 2017b and references therein). Therefore, process-based models could underestimate the effect of heat stress compared to the statistical approaches (Roberts et al 2017). Indeed, including the physiological effects of heat stress can alter significantly the model results (Lobell et al 2015, Wang et al 2017). The same issue arises more prominently for water excess (Rosenzweig et al 2002) and other related factors such as pests and diseases, or leaching of nutrients in fertilizers. As a consequence, the reliability of processbased crop models highly depends on the region where they are applied, reducing the agreement with the observations (see e.g. Frieler et al 2017). Despite these limitations, process-based models of sufficient complexity can indeed be used to assess causality between yield and climate anomalies in particular regions (see for instance Lobell et al 2013, 2015, Flohr et al 2017, Jain et al 2017). Statistical approaches usually establish regressionlike links between reported yield time series (e.g. FAOSTAT data) and the main climatic variables or more complex climatic indicators (e.g. Ray et al 2015, Iizumi and Ramankutty 2016, Lesk et al 2016, Matiu et al 2017). Climate anomalies can be diagnosed based on a single indicator (Iizumi and Ramankutty 2016),