|GUO, TINGTIN - Iowa State University|
|ZHANG, ZHIWU - Washington State University|
|MARSHALL, J - University Of Idaho|
|YU, JIANMING - Iowa State University|
Submitted to: Molecular Plant
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/28/2021
Publication Date: 2/7/2022
Citation: Li, X., Guo, T., Bai, G., Zhang, Z., See, D.R., Marshall, J., Garland Campbell, K.A., Yu, J. 2022. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. Molecular Plant. 15(2):203-206. https://doi.org/10.1016/j.molp.2022.01.001.
Interpretive Summary: Agricultural product is heavily impact by climate conditions. This study characterized the climate contributions to wheat yield and grain-quality trends observed from a 16-year soft white spring wheat variety testing trial conducted by the South Central and Southeast Idaho Cereals program. Each year, a set of elite wheat variety were tested in five locations. Grain yield, two grain-quality traits (test weight and protein content), and two agronomic traits (heading date and plant height) have been measured consecutively from 2005 to 2020, and falling numbers (a grain-quality trait) has been evaluated since 2013. By connecting publicly accessible climate profile database, the significant climate patterns for yield and grain-quality fluctuations is uncovered with the newly developed CERIS algorithm. The combinatio of heavy rainfall and narrow diurnal temperature range in late-season reduced test weight and falling numbers. The overall framework reported could be widely applied to explain and predict performance trend from other variety testing trials for different crops
Technical Abstract: Profound climatic contributions to spring wheat performance trends spanning 16 years in South Central and Southeast Idaho were dissected into explicit climatic patterns. A cool early-season but warm late-season increased yield. Less temperature fluctuation in late-season increased grain protein content, while the combination of heavy rainfall and narrow diurnal temperature range in late-season reduced test weight and falling numbers. Environomic prediction with whole-season climatic variants could forecast performance under new environments.