Lodging in rice can be alleviated by atmospheric CO sub(2) enrichment

• Published in: Agriculture, ecosystems & environment ecosystems & environment, 2007-01, Vol.118 (1-4), p.223-230
• Main Authors: Shimono, Hiroyuki, Okada, Masumi, Yamakawa, Yasuhiro, Nakamura, Hirofumi, Kobayashi, Kazuhiko, Hasegawa, Toshihiro
• Format: Article
• Language: English
• Subjects: Oryza sativa
• ISSN: 0167-8809
• DOI: 10.1016/j.agee.2006.05.015

The projected increase of atmospheric CO sub(2) concentration [CO sub(2)] is expected to increase yield of agricultural C sub(3) crops, but little is known about effects of [CO sub(2)] on lodging that can reduce yield. This study examined the interaction between [CO sub(2)] and nitrogen (N) fertilization on the lodging of rice (Oryza sativa L.) using free-air CO sub(2) enrichment (FACE) systems installed in paddy fields at Shizukuishi, Iwate, Japan (39 reversible reaction 8N, 140 one fourth 7E). Rice plants were grown under two levels of [CO sub(2)] (ambient = 365 mu mol mol super(-1); elevated [CO sub(2)] = 548 mu mol mol super(-1)) and three N fertilization regimes: a single initial basal application of controlled-release urea (8 g N m super(-2), CRN), split fertilization with a standard amount of ammonium sulfate (9 g N m super(-2), MN), and ample N (15 g N m super(-2), HN). Lodging score (six ranks at 18intervals, with larger scores indicating greater bending), yield, and yield components were measured at maturity. The lodging score was significantly higher under HN than under CRN and MN, but lodging was alleviated by elevated [CO sub(2)] under HN. This alleviation was associated with the shortened and thickened lower internodes, but was not associated with a change in the plants mass moment around the culm base. A positively significant correlation between lodging score and ripening percentage indicated that ripening percentage decreased by 4.5% per one-unit increase in lodging score. These findings will be useful to develop functional algorithm that can be incorporated into mechanistic crop models to predict rice production more accurately in a changing climate and with different cultural practices.