Fig. 3.1 Adaptation to climate change by adjusting the sowing dates under different GCMs scenarios of the rice variety IR 36 grown during the kharif season at Cuttack (A) and at Jorhat (B) using ORYZA1 model (Source: Krishnan et al. (2007))
3.10 Increased Tolerance of Rice Spikelet Fertility to Temperature
Hypothesis that high temperature induces spikelet injury was evaluated by Krishnan et al. (2007) by enhancing the tolerance level of the rice cultivar IR 36 in the crop model. The equation used in the ORYZA1 model to describe the response of spikelet fertility to temperature is
Where 8 is the fertility percentage (%), Tmax is the average daily maximum temperature during the flowering period (° C), and Tmp is the average daily maximum temperature (°C) at which 50% of the spikelets are fertile. For the indica variety, Tmp had a value of 36.5. To simulate the possible effect of an increase in tolerance of spikelet to high temperatures, it was assumed that this response was shifted by 2° C by increasing the value of Tmp to 38.5°C. This adaptation in the spikelet trait was examined in Cuttack site. With the available weather data for this site, and with a constant sowing date of June 15th, a comparative study using the ORYZA1 model was made for the current climate and other GCM scenarios as obtained by the GFDL, GISS and UKMO (Fig. 3.2). Under the GCMs scenarios, temperature at the time of flowering for the main season was already high. Without any temperature tolerance of the variety by not adjusting the value of Tmp, large decreases in yield due to spikelet sterility were predicted. But, with the adaptation of variety by improved temperature tolerance of the spikelet, the yield increased higher than that of the current scenario level, at about +10.7, +13.6 and -8.4 respectively, under the GFDL, GISS and UKMO model scenarios (Fig. 3.2).
Fig. 3.2 Adaptation to climate change by improving the temperature tolerance of the rice cultivar IR 36 under the different GCMs scenarios grown during the kharif season at Cuttack using ORYZA1 model (Source: Krishnan et al. (2007))
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