Canopy micrometeorology monitoring in diverse climates innovatively improves the evaluation heat-induced sterility of rice under climate change
M. Yoshimoto, M. Fukuoka, Y. Tsujimoto, T. Matsui, K. Kobayasi, K. Saito, P. A. J. van Oort, B. I. Y. Inusah, C. Vijayalakshmi, D. Vijayalakshmi, W. M. W. Weerakoon, S. Chandradasa, T. T. Myint Daw, Z. C. Phyo Daw, X. Tian, H.-S. Lur, C.-M. Yang, L. Tarpley, N. L. Manigbas, T. Hasegawa
Abstract
This study is the first and unique of open-field studies to assess the determination process of heat-induced spikelet sterility (HISS) of rice by using the same variety and the same monitoring system, MINCER (Micrometeorological Instrument for Near Canopy Environment of Rice), covering the major-rice growing regions from Sub-Saharan Africa, South, Southeast, and East Asia, and USA. Applying the observation data from the monitoring network, MINCERnet, to the canopy heat balance model, IM2PACT, it was quantitatively corroborated in open-field conditions worldwide that the canopy and panicle transpiration and their evaporative cooling effect played a great role on the micrometeorological gap between the ambient air temperature and the panicle temperature, and that the sterility rate due to HISS in open-fields can be evaluated accurately in diverse climates by the mean panicle temperature at flowering hours in the flowering period. The heat balance structure suggested that the risk of HISS should be higher in high humidity climates rather than in dry climates also in the future, which lead to the importance of the humidity accuracy as well as of the air temperature in climate scenarios and their spatial downscaling for future prediction of rice heat stress and production. Applying the heat-tolerant variety was suggested to be able to keep the sterility due to HISS low in all climates. It is needed of the approach using the panicle temperature as indicator of HISS by intervening sub-model and/or monitoring of micrometeorology inside the canopy to reduce uncertainties in future rice yield prediction under various adaptation measures.
02 /Specification & Classification
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