Monitoring canopy micrometeorology in diverse climates to improve the prediction of heat-induced spikelet sterility in rice under climate change

Abstract

Rice is well adapted to a wide range of climates, but is highly susceptible to heat during flowering. However, there are uncertainties in assessing the occurrence of heat-induced spikelet sterility (HISS) and the impact of climate change. One reason is the gap between the ambient air temperature and the panicle temperature, which determines the magnitude of HISS in field studies. To improve our understanding of this gap, we established a multi-site monitoring network (MINCERnet) to measure canopy micrometeorology and heat stress in the major rice growing regions (Sub-Saharan Africa; South, Southeast, and East Asia; and the USA). MINCERnet assessed the processes that determine panicle temperature and the resulting HISS in open fields using the same cultivars (‘IR64’, ‘N22’, and ‘IR52’) and a standard system (MINCER) for micrometeorological monitoring under diverse climates. By using the MINCERnet data in the canopy heat-balance model (IM²PACT), we confirmed that the canopy and panicle transpiration and the resulting evaporative cooling strongly affected the gap between the ambient air temperature and the panicle temperature, and that the HISS rate in open fields could be predicted accurately in diverse climates by using the mean panicle temperature during the flowering hours. The “oasis effect” in the broad sense, that is, evaporative cooling and the increase of relative humidity, which is nested at the various levels along the continuum from the landscape to the panicle, formed temperature and relative humidity gradients along the continuum in response to different climatic conditions. The heat-balance characteristics (i.e., a stronger evaporative cooling under drier climate conditions) suggested that the risk of HISS caused by global warming will increase more in wetter climates, where panicle temperatures tended to increase. Thus, accurate relative humidity data as well as air temperature will be required, along with spatial downscaling, to permit accurate prediction of rice heat stress and yield. HISS prediction using an approach based on the panicle temperature as input for models and monitoring of canopy micrometeorology will reduce uncertainties in rice yield prediction and the response of yield to various climate change adaptation measures.

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