Vol. 6, Issue 4 (2018)
Assessment of yield and yield attributes gap under irrigated and rainfed condition in rice crop for different agroclimatic zones of Chhattisgarh using DSSAT Simulation model
Author(s): Surbhi Jain, ASRAS Sastri, N Manikandan, JL Chaudhary and Bhirendra Kumar
Abstract: Validation of the simulated value under DSSAT models experiment was conducted during kharif season for different rice varieties viz., Swarna, Mahamaya, MTU-1010 and Karma Mahsuri to assess the yield and yield attributes gap. The DSSAT simulation model was validated for the different agroclimatic zones of Chhattisgarh. Crop simulation models are essential tools to design management practices to mitigate such adverse conditions. An experiment was carried out during 2014 in Department of Agrometeorology, IGKV, Raipur. DSSAT simulation model was used for validating soil, crop and weather management data from three agroclimatic regions and five cultivars. The production potentials of the five varieties were higher in irrigated condition as compared to rainfed condition. From the yield gap analysis, it was found that the yield gap between no fertilizer stress and with fertilizer 100:60:40 kg/ha was highest in all three zones both under irrigated and rainfed conditions. It was also found that the highest yield gap was found in Karma Mahsuri with 7.5, 9.7 and 9.4 t/ha at Raipur, Ambikapur and Jagdalpur respectively under irrigated condition. Under rainfed condition yield gap was 4.5, 4.7 and 4.6 t/ha in Karma Mahsuri under Raipur, Ambikapur and Jagdalpur respectively. It was also found that the yield gap varied from variety to variety with highest yield gap in Karma Mahsuri in both irrigated and rainfed conditions in all the three stations.
Pages: 23-28 | 1071 Views 51 Downloads
How to cite this article:
Surbhi Jain, ASRAS Sastri, N Manikandan, JL Chaudhary, Bhirendra Kumar. Assessment of yield and yield attributes gap under irrigated and rainfed condition in rice crop for different agroclimatic zones of Chhattisgarh using DSSAT Simulation model. Int J Chem Stud 2018;6(4):23-28.