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Modeling

Modeling Spikelet Sterility Induced by Low Temperature in Rice

^a Dep. of Biology and Environment Sciences, National Agric. Res. Center for Tohoku Region, Shimokuriyagawa, Iwate, 020-0198, Japan and the Japan Society for the Promotion of Science, Tokyo, 102-8577, Japan
^b Dep. of Global Resources, National Inst. for Agro-Environmental Sciences, Tsukuba, Ibaraki, 305-8604, Japan
^c Dep. of Biology and Environment Sciences, National Agric. Res. Center for Tohoku Region, Shimokuriyagawa, Iwate, 020-0198, Japan
^d Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan

^* Corresponding author (shimn{at}affrc.go.jp)

Received for publication February 3, 2005. Accurate prediction of spikelet sterility in rice (Oryza sativa L.) is a prerequisite for accurately predicting grain yield in cool climates, since severe yield losses frequently occur when spikelet sterility is induced by cool temperatures during reproductive growth. Both cool air temperature (T_a) and cool water temperature (T_w) are detrimental factors that can cause spikelet sterility, but a large discrepancy between T_a and T_w is often observed in paddy fields. The depth of water can also affect spikelet sterility. We proposed a model that accounted for the effects of T_a, T_w, and water depth on spikelet sterility, and was based on panicle temperature (T_p), then tested the model using 23 independent sets of field data from northern Japan. We also quantified the role of daily amplitude (the difference between maximum and minimum temperatures) and differences in plant sensitivity to temperature in determining spikelet sterility. A cool-irrigation experiment revealed that spikelet sterility depended more strongly on T_p than on T_w or T_a. We also developed six models using "cooling degree-day" concept. The model based on T_p had higher accuracy than models based solely on T_w or T_a. In addition, average temperature was a better predictor than minimum temperature. Accounting for the difference in temperature sensitivity also improved the model's accuracy. A model that considers these factors would thus improve prediction accuracy for spikelet sterility due to cool weather.

Abbreviations: CDD, cooling degree-day • CL, culm length • DAP, days after transplanting • DVI, developmental index • DVR, developmental rate • RMSD, root-mean-square deviation • STR, spikelet sterility • T_a, air temperature • T_w, water temperature • T_p, panicle temperature

This article has been cited by other articles:

				H. Shimono, T. Hasegawa, and K. Iwama Modeling the Effects of Water Temperature on Rice Growth and Yield under a Cool Climate: I. Model Development Agron. J., September 10, 2007; 99(5): 1327 - 1337. [Abstract] [Full Text] [PDF]