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A rainfed experiment was conducted at two contrasting rainfall situations of two different states of South India viz., Hyderabad (Telangana) and Tirupathi (Andhra Pradesh) during 2007-08 to standardize the optimum sowing time, plant densities and fertiliser dose for horsegram variety CRIDA 18R released and recommended for cultivation in South India. Results showed that the second fortnight of August and first fortnight of November were suitable optimum times of sowing in Hyderabad and Tirupathi regions respectively due to receipt of rainfall from South West monsoon and North East monsoon at respective locations. Plant densities of 3.33 lakh per hectare with or without 10N + 20P2O5 fertilizer dose yielded higher seed in both the regions. However when the sowing was delayed, wisest decision might be to continue the plant densities of 3.33 lakh population per hectare with or without fertilizer. The results revealed that CRIDA-18R responded to fertilizer up to 10 N +20 P2O5 kg ha-1 with 3.33 lakh plant density but for the varied optimum time of sowing at two different centers with occurrence of favourable rainfall situations, revealing a very flexible sowing time for CRHG-18R in South India.

A study was conducted in the Open Top Chambers (OTCs) to assess the influence of cool and warm season conditions on the response of sunflower (KBSH-1) to two elevated CO2 levels (550 and 700 ppm) and compare them with the response to an ambient level (390 ppm). The effect of elevated CO2 levels on biomass accumulation, seed yield and yield components were quantified in two seasons. Apart from the main effects of CO2 and different seasons, a significant interaction effect between CO2 levels and seasons was also observed. The CO2 levels differed significantly in influencing biomass accumulation, seed yield and number of seeds. Four Principal Components (PC) based on PC analysis explained about 85% of the variability in the response of traits influenced by CO2 levels in winter and summer seasons. In order to predict total dry weight, seed yield and harvest index obtained in winter and summer seasons, regression models of these variables were also calibrated and used through PC scores of different components. The analysis indicated that significant predictions could be made at ambient level with 550 ppm, compared to 700 ppm of CO2 level. The plant traits with a significantly higher loading of more than ± 0.70 on PCs were identified and have been recommended for future research in genetic improvement of sunflower, taking into account the change of climate due to elevated CO2 and temperature levels.

Nine characters contributing to seed yield were measured on 20 accessions of horse gram (Macrotyloma uniflorum (Lam.) Verd), and subjected to genetic divergence analysis using Mahalanobis statistic and mapping using DIVA-GIS. The accessions were collected from two eco-geographical regions of Andhra Pradesh (India) – North Coastal and Rayalaseema. Based on D2 values the genotypes were grouped into five clusters. Genetic diversity was not related to eco-geographical distribution. The greatest inter-cluster distance separated clusters II and V, followed by clusters IV, and V, III and IV. Entries in clusters V and II appear suitable as parents for horse gram improvement. The Rayalaseema region is the source of useful variation for days to flowering, maturity and yield.

Principal component analysis has been explored for assessing the consistency of 14 plant traits for sunflower growth using multivariate data of 6 field experiments conducted during kharif 1994 to 1999 seasons on rainfed alfisol. Seven genotypes have been used in the study viz., Guj-sun-1, MSFH-8, MSFH-17 KBSH-1, Jwala, Pac-36 and Morden. Observations were recorded on leaf nitrogen, leaf area, leaf weight, leaf number, stem nitrogen, stem weight root length, root weight, stomatal conductance, photosynthesis, total biomass on 30, 45 and 60 days after sowing (DAS) and flower head diameter and flower head weight on 45 and 60 days after sowing in each season. The first two principal components have extracted about 80% of variance in the data of different plant traits on different days after sowing. The consistency of plant traits has been assessed by examining correlations between different plant traits and the distribution of loadings of plant traits on the first two leading principal components 30, 45 and 60 days after sowing. The results have indicated that stem weight and lead number on the 30th day, leaf area and leaf weight on the 45th day and leaf area, leaf weight and total biomass on the 60th day after sowing had significantly higher loadings on the 1st principal component. Similarly, root length on the 30th day, flower head diameter and flower head weight on the 45th day and root length and photosynthesis on the 60th day after sowing had significantly higher loadings on the 2nd principal component. Based on a graphic plot of the loadings, root length (30 and 60 DAS), stomatal conductance (30, 45 and 60 DAS) and photosynthesis (30 DAS) were found to be consistent since their loadings on the 1st principal component had high mean values with low standard deviations. Similarly, stem nitrogen (45 and 60 DAS), stomatal conductance (45 DAS), photosynthesis (30 DAS), leaf nitrogen (60 DAS), root length (30 DAS), flower head diameter (45 DAS) and flower head weight (45 DAS) were also consistent since their loadings on the 2nd principal component had high mean values with low standard deviations.

SUMMARY Regression analysis is explored for the multivariate data of two field experiments conducted with 7 sunflower genotypes, GUJ-SUN-1, MSFH-8, MSFH-17, KBSH-1, JWALA, PAC-36 and MORDEN, grown during kharif 1997 and 1998 in a dryland alfisol. Observations on 8 physiological traits, stomatal conductance, photosynthesis, leaf nitrogen, stem nitrogen, leaf weight, stem weight, leaf number and leaf area, as observed on 30, 45 and 60 days after sowing were used in the analysis for selecting the best genotype which is consistent in plant growth and has an efficient performance apart from identifying the dominant traits. Regression functions for predicting physiological traits have been calibrated by regressing the observations of a trait on 60 days after sowing through its observations on 30 and 45 days after sowing. The genotypes have been assessed based on the significance of traits, predictability and estimate of error derived for each trait. The analysis indicated the dominance of photosynthesis, stomatal conductance and stem nitrogen in kharif 1997 and stomatal conductance, photosynthesis and leaf nitrogen in kharif 1998 for the growth of sunflower. The analysis also indicated the superiority of PAC-36 in kharif 1997 and JWALA in kharif 1998 having 8 and 7 significant regression coefficients of traits, respectively.