How ENSO affects maize yields in China: understanding the impact mechanisms using a process-based crop model

Abstract

The El Nino Southern Oscillation (ENSO) is one of the main factors influencing global climate variability and consequently has a major effect on crop yield variability. However, most studies have been based on statistical approaches, which make it difficult to discover the underlying impact mechanisms. Here, using process-based crop model Model to Capture the Crop-Weather relationship over a Large Area (MCWLA)-Maize, we found a consistent spatial pattern of maize yield variability in association with ENSO between MCWLA-Maize model outputs and observations. During El Nino years, most areas of China, especially in the north, experience a yield increase, whereas some areas in the south have a decrease in yields. During La Nina years, there is an obvious decline in yields, mainly in the north and northeast, and a general increase in the south. In-depth analyses suggest that precipitation P rather than temperature T and solar radiation S during the maize growing season is the main cause of ENSO-induced maize yield variability in northern and northeastern China. Although a 2 degrees C change of T can affect maize yields more than a 20% change of P, greater changes of P contribute more to maize yield variability during ENSO years. In general, maize yields in drier regions are much more sensitive to P variability than those in wetter areas. All changes in meteorological variables, including T, P, S, and vapour pressure deficit (V-PD) during ENSO years, affect yield variability mainly through their effects on water stress. Our results suggest that more effective agricultural information can be provided to government decision makers and farmers by developing a food security warning system based on the MCWLA-Maize model and ENSO forecast information.