|BURROUGHS, CHARLES - University Of Illinois|
|ALFORD, AVA - Southern Illinois University|
|PENG, BIN - University Of Illinois|
|KUMAGAI, ETSUSHI - University Of Illinois|
|GUAN, KAIYU - University Of Illinois|
|Ainsworth, Elizabeth - Lisa|
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 10/30/2018
Publication Date: 10/30/2018
Citation: Burroughs, C., Alford, A., Peng, B., Kumagai, E., Bernacchi, C.J., Guan, K., Ainsworth, E.A. 2018. Soybean physiological and agronomic responses to heat stress: a dose response experiment [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. Paper No. 1114.
Technical Abstract: Climate stressors such as rising temperatures and heat waves place additional strain on crop yields and reduce food supply. In order to accurately model future soybean yields, it is necessary to understand the mechanisms by which heat stress reduces soybean productivity. Under heat stress, seed yield could be reduced as result of reduced photosynthetic metabolism, accelerated development, and/or increased flower and pod abortion. Commercial varieties of soybean were grown under ambient and four elevated temperature treatments (+1.5, +3, +4.5 and +6 °C) at an open-air study site at the University of Illinois-Urbana Champaign. There was a linear decrease of seed yield of 225.7 kg/ha per 1°C increase in temperature. Photosynthetic capacity was not affected by the heating treatments, until late in the growing season, when photosynthetic capacity was reduced at the highest temperature treatment. In contrast to expectation, development was not accelerated in the heating treatments. Flower and pod numbers were counted on nodes 5 to 13, and the number of flower and pod abortions was estimated. There were no clear effects of heating on flower and pod production or abortion. Data collected from these experiments will be used to calibrate parameters in CLM-AgSys, a crop model that embeds in the Community Earth System Model (CESM). CLM-AgSys combines the strengths of both the Community Land Model (CLM) and the Agricultural Production Systems sIMulator (APSIM). With this unique model-data fusion effort, we hope to more accurately project soybean yield of US under future climate scenarios.