Abstract

A field trial was carried out under irrigated situation during the fall seasons i.e. between August and February of 2003-04 and 2004-05 at Coimbatore to evaluate the performance and suitability of popular Bt and non-Bt hybrids (MECH 162 Bt and MECH 162 non Bt) on both optimum and delayed planting conditions under four dates of sow- ing (15 August, 1 and 15 September, and 1, October). Timely planting of Bt hybrid on 15 August produced signifi- cantly higher seed cotton yield (2.05 t/ha) and associated traits. The result revealed that seed cotton yields were similar under Bt (MECH 162 Bt) when sown either on 1 or 15 September and that of non-Bt (MECH 162 non Bt) sown on 15 August. The study also suggested that Bt cotton hybrid could be staggered planted from 15 August to 15 September without significant reduction in yield. Higher yield of MECH 162 Bt was also apparent under late planting condition (1 October) over its isogenic non-Bt as the former recorded significantly (42.5%) higher yield (1.47 t/ha) over the latter (1.04 t/ha). Bt cotton matured early in the season (with higher Bartletts index) and re- tained higher number of bolls, which resulted in increased sink strength and seed cotton yield. However, elevated performance in fibre yields due to sowing time differences or Bt gene per se (MECH 162 Bt versus its non-Bt) could hardly influence the more resilient quality traits except 2.5% span length. Highest net return ( 35,550/ha) and benefit cost ratio (BCR, 1.63) were also calculated with 15 August planting with the Bt hybrid. Even under late sown condition economic advantage of Bt hybrid was evident from increased net return (11.6%) and BCR (37%). Irrespective of dates of sowing, incorporation of Bt gene onto non-Bt hybrid increased gross return, net return and BCR by 15.6, 36.4 and 144.3% respectively. A Regression equation involving yield (Y), maximum air temperature (T ), rainfall (RF) and RH (Y=117.4+0.06359 T (0-45 DAS) - 0.04716 RH 07 22 (46-90 DAS) - 0.03115 RF(1-2 max max weeks) was validated for yield prediction using chi-square test.

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