Performance of Stress Indices in Assessing High Yield Potential of Rice Genotypes in Sulfur Deficient Soil
M.V Sriramachandrasekharan, R. Manivannan, M Ravichandran
International Journal of Plant & Soil Science · pp. 1009–1017 · Published 26 May 2014
10.9734/IJPSS/2014/10145Abstract
Aim: To recognize the rice genotypes based on stress indices which would give higher yield both under sulfur stress and non-stress conditions. Design Factorial Randomized design with two factors Place and Duration of Study: It was conducted in the farmers field( Chidambaram and Kuthalam in Tamilnadu, India and during Kharif season ( June to September , 2009) Methodology: Field experiments were conducted in two soils Kondal series (Typic Haplusterts- Vertisol) and Padugai series (Typic Ustifluvents- Entisol). The treatment details are factor A–S levels ( 0 & 10 mg S kg-1) applied through gypsum and Factor B- Rice genotypes (ADT 43,CO 47,ADT 39,CO 43,ADT 42,ASD 19,ADT 36,ADT 37,ADT 38 and CO45). The yield data from two soils was used to calculate for each genotype 1) Stress tolerance (TOL) 2) Mean productivity (MP) 3) Geometric mean productivity (GMP) 4) Stress susceptibility index (SSI) and 5) Stress tolerance index (STI). Results: Grain yield varied significantly (P=0.05) among rice genotypes regardless of S treatments. Without added S treatment CO 43 (4865 kg ha-1) and CO 47 (5025 kg ha-1) produced highest grain yield and ADT 36 (3437,3775 kg ha-1) produced lowest yield in Kondal and Padugai series, respectively. Stress susceptibility index (SSI) and stress tolerance index (TOL) was associated with low yield under S sufficient condition. But stress tolerance index, mean productivity (MP) and geometric mean productivity (GMP) was able to identify top yielders both under S stress and non stress situations because of very strong correlation with yield. Accordingly best performers based on STI under both S stress and non- stress were ADT 43, CO 47, ADT 39 and CO 43.
Cited by 1
A. W. Bowsher, K. C. Shelby, I. Ahmed · Journal of Agronomy and Crop Science · 2016
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