assessment of varieties of tomato … · 2/10/2017 · journal of progressive agriculture, vol. 8...

Journal of Progressive Agriculture, Vol. 8 No. 2: Oct. 2017 ---------------------------------------------------------------------------------------------------------------------------------------------------------------

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ASSESSMENT OF VARIETIES OF TOMATO (Lycopersicon esculentum Mill.) FOR YIELD, QUALITY AND ECONOMICS UNDER DIFFERENT GROWING

CONDITIONS D. S. Murkute1, P. Verma2, Yogesh Pawar*, J.R. Vadodaria3, and L. R.Varma4

Department of Horticulture, C.P. College of Agriculture, S. D. Agricultural University, Sardarkrushinagar- 385 506 (Gujarat)

*Email: [email protected] Received: 14.02.15 Accepted: 06.04.17

ABSTRACT The present investigation was planned and executed at Horticulture Instructional Farm, Department of Horticulture, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar (Gujarat) during 2010. The experiment was laid out in Factorial Randomised Block Design with eight tomato varieties and two growing condition. Results of study inferred that, naturally ventilated polyhouse grown plants was recorded maximum individual fruit weight (38.24 g), fruit volume (46.74 cc), fruit diameter (4.46 cm), fruit length (3.80 cm) and acidity (0.43 per cent). However, maximum ascorbic acid content was found in open field grown fruits which were 11.70 mg. Among different varieties Pusa Hybrid-2 recorded maximum fruit length (4.35 cm) and individual fruit weight (41.44 gm), while Arka Saurabh showed maximum diameter (4.93 cm) and fruit volume (62.33 cc). It was further exhibited that variety Arka Alok recorded highest TSS per cent (4.75 per cent) and ascorbic acid (13.20 mg/ 100 g), whereas maximum acidity per cent was recorded by fruits of variety Arka Vikas (0.47 per cent). Pusa Hybrid-2 under NVP recorded maximum gross return ( 9, 88,480), net return (

8, 62,124) and benefit:cost ratio (6.82:1).

Key words: Naturally ventilated polyhouse, open field, quality, tomato genotypes.

Tomato (Lycopersicon esculentum Mill.) is important vegetable for canning industry and has good demand in market. Tomato grown in almost all the corners of the country belongs to family Solanaceae. Tomato is highly regarded for its nutritional importance also. It is rich source of vitamin A (320 IU) and C (31 mg). Apart from these, it is good source of minerals like potassium (114 mg), phosphorus (36 mg), magnesium (15 mg) and others in smaller amounts (Chaudhary, 2006). Now-a-days due to increased awareness about environment impact and consumers demand in terms of good quality and safe produce, growers are adopting greenhouse production system. Rapid increase of population increase demand for agricultural produce and it will fulfill

only by growing promising genotype under suitable condition which helps to boosting tomato production. Protected cultivation or controlled environment agriculture (CEA) is a total concept of modifying the natural environment for optimum plant growth (Sirohi, 2002). It comprises of manipulation of abiotic factors like air and root zone temperature, relative humidity, light, air velocity, atmospheric concentration of carbon dioxide (CO2), root zone oxygen and nutrient concentration and moisture supply to control the crop growth. Greenhouse vegetable production is initially capital intensive, highly productive, conserves water and land and can be less harmful to environment compared with field production (Resh, 1997). Under open field condition it is

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very difficult to grow vegetables successfully in the rainy season due to very high incidence of viruses, insects/pests, fungal and bacterial diseases. Similarly, several abiotic stresses caused by extreme temperature, high humidity and high radiation also do not permit successful vegetable cultivation under open environment during the rainy and post rainy season. But some low-cost protected technologies like insect-proof net house, shade net house and naturally ventilated greenhouses are highly suitable for successful cultivation of common and high-value vegetables both during rainy and post rainy season (Singh et al. 2010). Also in the present scenario the cultivable land area is decreasing day by day due to rapid urbanization, industrialization and shrinking land holdings. India acquiring second position in production of vegetables after China but the productivity scenario clears that Indian farmers unable to reach top in productivity due to lack of advanced agro-techniques. Growing of crops under protection has many advantages but biggest advantage lies with off-seasonality and superior quality of the produce (Kumar et al. 2007). Therefore, vegetable production under low cost greenhouse technology is the best alternative to use the land and other resources more efficiently.

MATERIALS AND METHODS The experiment was conducted in 2010 the naturally ventilated polyhouse and open field of Horticulture Instructional Farm, Chimanbhai Patel College of Agriculture, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar. The experiment was arranged in factorial randomized design with eight treatments (Varieties) viz. Anand Tomato- 3, Pusa Ruby, Pusa Hybrid-2, Arka Saurabh, Arka Vikas,

Arka Meghali, Arka Abha and Arka Alok under naturally ventilated polyhouse and open field condition, therefore making total sixteen treatment combination. The treatments were three replicated thrice. The paired row system of planting was followed with 75cm x 45cm x 45cm spacing. Sowing was done in the month of May and transplanting was done after one month of sowing on flat bed. Recommended package of practices were followed to grow crop successfully. Fruit length and fruit diameter of five fruits from each tagged plants was measured with the help of digital vernier caliper. Fruit volume was measured by measuring cylinder method, i.e. Fruits were dipped in measuring cylinder which has water and the difference between final and initial volumes of water was noted. Total soluble solids content of tomato fruit was determined using Erma Hand Refractometer (0-32 Brix). The mean data were subjected to statistical analysis following standard procedure (Gomez and Gomez, 1984).

RESULTS AND DISCUSSION Yield character: Data on yield per hectare revealed that plants grown under polyhouse recorded maximum yield per hectare (76.20 t) over the plants grown under open field. Main effect of genotypes found statistically significant on yield per hectare. Among eight genotypes Pusa Hybrid-2 produced highest yield per hectare (87.47 t). In interaction effect of growing condition with different genotypes noticed that, among grown genotypes, Pusa Hybrid-2 recorded higher yield per hectare under greenhouse as well as open field which was 123.56 tonnes and 51.33 t, respectively. Highest number of fruits per plant under greenhouse was due to prolonged harvesting span which resulted in maximum yield per hectare. Similar results were observed by, Pandey et al.


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(2000), Cheema et al.( 2003) and Parvej et al. (2010). Quality attributes: Data on quality parameters of different variety fruits found significant between two growing conditions. Fruits of naturally ventilated polyhouse grown plants were recorded maximum individual fruit weight (38.24 g), fruit volume (46.74 cc), fruit diameter (4.46 cm), fruit length (3.80 cm) and acidity (0.43 per cent). However, maximum ascorbic acid content was found in open field grown fruits which were 11.70 mg. Earlier in 2005a Singh et al. have proved the role of micro-climate in improving quality parameters of tomato. Naturally ventilated polyhouse grown have better quality of fruits than open field except ascorbic acid. Results on ascorbic acid were found parallel with findings of Pant et al. 1996. Data of different varieties revealed that variety Pusa Hybrid-2 recorded significantly maximum fruit length (4.35 cm) and individual fruit weight (41.44 g) while Arka Saurabh showed maximum diameter (4.93 cm) and fruit volume (62.33 cc). The results further exhibited that variety Arka Alok recorded highest TSS per cent (4.75 per cent) and ascorbic acid (13.20 mg per 100 g). There was a marked variation in the acidity content of tomato fruits and it was observed that maximum acidity was recorded by fruits of variety Arka Vikas (0.47 per cent). Quality characters governed by heredity or genetic constitution of plant, hence, variation in different varieties are obvious. The results of these quality characters are in confirmity with results of Pant et al. (1996), Cheema et al. (2003), Hazarika and Phookan, (2005), Singh et al. (2005b). Under polyhouse and open field environments variety Pusa Hybrid-2 recorded maximum fruit length of 4.45

cm and 4.25 cm, respectively, while Pusa Ruby recorded minimum i.e. 3.13 cm and 2.33 cm, respectively. Diameter of fruit recorded maximum in Arka Saurabh (5.43 cm). Under open field, maximum fruit diameter was recorded in Arka Alok (4.58 cm), however, Arka Saurabh (4.44 cm), Arka Abha (4.35 cm) and Pusa Hybrid-2 (4.24 cm) were found at par with Arka Alok. Among greenhouse grown varieties, Arka Saurabh recorded highest weight of fruit (52.25 gm) and fruit volume (73.33 cc). In case of open field maximum fruit weight and volume was found in Pusa Hybrid-2 (41.01 gm) and Arka Saurabh (51.33 cc), respectively. Variation in fruit size parameters was due to difference in shape of fruits. Similar results on fruit diameter and length of fruits under different growing conditions was also reported by Hazarika and Phookan (2005), and Parvej et al. (2010) in tomato and Pandey et al. (2005) in capsicum. However, fruit weight and volume differed due to difference in fruit length and diameter of fruits of different varieties grown in different environment. Results on fruit weight and volume are agreed with findings of Ganesan (2002) and Cheema et al. (2003). Under naturally ventilated polyhouse variety Arka Alok recorded maximum TSS per cent (5.31 per cent) and ascorbic acid (12.35 mg), whereas, in open field Arka Saurabh and Arka Alok found maximum in TSS per cent (5.10 per cent) and ascorbic acid (14.04 mg) content, respectively. Maximum acidity per cent under naturally ventilated polyhouse grown varieties was found in Pusa Hybrid-2 (0.54 per cent) followed by Arka Meghali (0.52 per cent). Among open field grown varieties, Arka Abha recorded highest acidity per cent of 0.59 per cent. The results obtained on these characters are in the line with the findings of Pant et al.


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(1996), Dhaliwal et al. (2000), Pandey et al. (2000). Economics Results regarding in economics i.e. cost of cultivation, gross return, net return and benefit:cost ratio are presented in table 2 and they revealed that, among sixteen treatments, T3 (Pusa Hybrid-2 under NVP) recorded maximum gross return (

9, 88,480), net return ( 8, 62,124) and benefit:cost ratio (6.82:1). While, under the open field condition variety Pusa Hybrid-2 was found to most remunerative as maximum gross return,

net return and benefit:cost ratio of ( 4,10,640), ( 3,19,247) and (3.49:1), respectively was in this variety.

CONCLUSION Between two growing conditions the naturally ventilated polyhouse proved superior over open field condition. Among eight varieties the variety Pusa Hybrid-2 found superior over other varieties with respect to yield, quality characters and economics under both the conditions.

Table 1: Performance of different tomato varieties on yield and quality attributes under NVP and open field

Treatment Yield hectare-1

Fruit Length (cm)

Fruit Diameter (cm)

Fruit Volume (cc)

Individual Fruit Weight (g)

TSS %

Ascorbic Acid (mg/100gm)

Acidity %

Growing Condition NVP 76.20 3.80 4.46 46.74 38.69 4.06 10.62 0.43 Open field 33.88 3.47 4.09 37.52 29.36 4.18 11.70 0.42 CD @ 5% 2.57 0.15 0.20 4.03 2.08 NS 0.28 0.01 Variety Anand Tomato-3 36.20 3.80 4.02 34.41 27.69 4.45 10.47 0.33

Pusa Ruby 36.16 3.00 3.55 24.08 19.36 3.57 10.00 0.33 Pusa Hybrid-2 87.45 4.35 4.32 42.25 41.44 3.46 11.05 0.45 Arka Saurabh 68.12 3.91 4.93 62.33 40.19 4.36 11.32 0.46 Arka Vikas 61.60 3.35 4.06 35.86 32.44 4.07 11.00 0.47 Arka Meghali 58.63 3.32 4.11 39.17 36.58 4.38 9.68 0.44 Arka Abha 53.53 3.74 4.57 45.37 35.53 3.92 12.58 0.52 Arka Alok 38.63 3.62 4.65 53.58 38.94 4.75 13.20 0.38 CD @ 5% 5.15 0.30 0.39 8.07 4.15 0.68 0.57 0.02 G x V T1 48.58 4.19 3.93 32.17 31.91 4.44 10.42 0.32 T2 56.68 3.13 3.71 27.33 24.98 3.28 9.38 0.37 T3 123.56 4.45 4.38 44.83 41.87 3.32 9.90 0.54 T4 96.86 4.21 5.43 73.33 52.25 3.62 10.75 0.48 T5 76.71 3.48 4.25 41.33 35.08 4.04 11.53 0.42 T6 85.78 3.49 4.53 51.34 46.33 4.31 8.81 0.52 T7 72.77 3.80 4.79 47.23 37.07 4.17 11.84 0.44 T8 48.62 3.66 4.72 56.33 40.09 5.31 12.35 0.32 T9 23.81 3.40 4.10 36.67 23.48 4.47 10.51 0.34 T10 15.63 2.88 3.40 20.83 13.75 3.85 10.62 0.29 T11 51.33 4.25 4.27 39.67 41.01 3.60 12.21 0.36 T12 39.38 3.60 4.44 51.33 28.14 5.10 11.89 0.43 T13 46.50 3.22 3.87 30.40 29.79 4.10 10.47 0.50 T14 31.49 3.15 3.70 27.00 26.84 4.45 10.55 0.35 T15 34.28 3.68 4.35 43.50 34.01 3.67 13.31 0.59 T16 28.65 3.58 4.58 50.83 37.80 4.20 14.04 0.45 CD @ 5% 7.28 0.42 0.55 11.40 5.88 0.96 0.81 0.03


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Table 2: Performance of different tomato varieties on economics under NVP and open field Treatment Cost of

cultivation ( ) Gross returns ( )

Net returns ( )

B:C ratio

(T1) Anand Tomato-3 under NVP 1,25,903 3,88,640 2,62,737 2.08:1 (T2) Pusa Ruby under NVP 1,26,194 4,53,440 3,27,246 2.59:1 (T3) Pusa Hybrid-2 under NVP 1,26,356 9,88,480 8,62,124 6.82:1 (T4) Arka Saurabh under NVP 1,26,234 7,74,880 6,48,646 5.13:1 (T5) Arka Vikas under NVP 1,26,234 6,13,680 4,87,446 3.86:1 (T6) Arka Meghali under NVP 1,26,234 6,86,240 5,00,006 4.43:1 (T7) Arka Abha under NVP 1,26,234 5,82,160 4,55,926 3.61:1 (T8) Arka Alok under NVP 1,26,234 3,88,960 2,62,726 2.08:1 (T9) Anand Tomato-3 under open field 90,942 1,90,480 99,538 1.09:1 (T10) Pusa Ruby under open field 91,233 1,25,040 33,807 0.37:1 (T11) Pusa Hybrid-2 under open field 91,393 4,10,640 3,19,247 3.49:1 (T12) Arka Saurabh under open field 91,273 3,15,040 2,23,767 2.45:1 (T13) Arka Vikas under open field 91,273 3,72,000 2,80,727 3.07:1 (T14) Arka Meghali under open field 91,273 2,51,920 1,60,647 1.76:1 (T15) Arka Abha under open field 91,273 2,74,240 1,82,297 2.00:1 (T16) Arka Alok under open field 91,273 2,29,200 1,37,927 1.51:1 The sale price of tomato was Rs 8/kg.

REFERENCES Chaoudhary, B. 2006. Vegetables. National book

trust, India. pp- 43-54 Cheema, D.S., Kaur, P., Dhunna, O.S., Singh, P.,

Kaur, S., Kaur, Sandeep. and Dhaliwal, M.S. 2003. Off season production of tomato under net house conditions. Haryana J. hort. Sci. 32 (3&4): 288-289.

Dhaliwal, M.S., Singh, S. and Cheema, D.S. 2000. Evaluation of tomato F1 hybrids. Haryana J. Hort. Sci. 29 (1&2): 128

Ganesan, M. 2002. Comparative evaluation of low cost greenhouse and its effect on the yield and quality of two varieties of tomato (Lycopersicon esculentum Mill.). Indian Agriculturist. 46 (3/4): 161-168.

Gomez, K.A. and A.A. Gomez, 1984. Statistical procedures for agricultural research, 2nd edition. John Wiley and Sons, New York. pp 84-89.

Hazarika, T.K., and Phookan, D.B. 2005. Performance of tomato cultivars for polyhouse cultivation during spring summer in Assam. Indian Journal of Horticulture. 62(3): 268-271.

Kumar, M., Kohli, S. K., Gupta, and Vikaram, A. 2007. Effect of growing media, irrigation regime, fertigation and mulching on productivity of tomato in naturally ventilated polyhouses in Hills. Indian Journal of Agriculture Sciences. 77 (5): 302-304.

Pandey, S., Dixit, J., Dwivedi, S.V. and Dubey, R. 2000. Stability analysis for yield and its

components in tomato (Lycopersicon esculentum MILL.) Haryana J. Hort. Sci. 29 (3&4): 207-208.

Pandey, V., Ahmed, Z., Tewari, H.C, and Narendra Kumar. 2005. Effect of greenhouse models on plant growth and yield of capsicum in North West Himalaya. Indian J. Hort. 62 (3), September 2005: 312-313.

Pant, P. C., Pande, H. K., Verma, G. S., Biswas, V. R., Pruthi, T. D., Kumar, N. 1996. Physical parameters and biochemical constituents of tomato grown under various protected conditions. New Agriculturist., 7 (1): 33-36.

Parvej, M.R., Khan, M.A.H., and Awal, M.A. 2010. Phenological development and production potentials of Tomato under polyhouse climate. The Journal of Agricultural Sciences. 5 (1): 19-31.

Singh, B., Mahesh, K., Hasan, M. 2005b. Performance of tomato cultivars under greenhouse conditions in northern India. Journal of Vegetable Science.11 (4):73-80.

Singh, R., Nagre, D.D. and Saytedndra, K. 2005a. Effect of planting time on yield, quality and economics of tomato hybrids in heated polyhouse. International Conference on Plasticulture and Precision Farming, held at The Ashok, Chanakyapuri, New Delhi (17th Nov.to 21st Nov., 2005). Book of Abstract, Abstract No. ICPPF/145/GH/28. pp-46


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COMBING ABILITY ANALYSIS FOR NITROGEN FIXATION IN BLACKGRAM (Vigna mungo L. Hepper)

Kalyan Singh Shekhawat Senthil Seeds Private Limited, IDAR, Gujarat-383430

Received: 01.09.17 Accepted: 16.10.17

ABSTRACT The present investigation was conducted at Plant Breeding Farm, RCA, Udaipur. Experimental material comprised of ten parents and 45 hybrids (adopting half diallel) raised in RBD with three replications in two environments (first- control; second-seed treated with Rhizobium urd culture). The main objective was to estimate combining ability analysis for nitrogen fixation characters. Interaction of GCA and SCA with environment was significant for nodules fresh weight per plant, nodules dry weight per plant, plant fresh weight, plant dry weight, nitrogen content of plant, carbohydrate content, leghaemoglobin content. The ratio of 2SCA/ 2GCA was greater than one for all the characters studied, there by indicating the preponderance of non-additive gene effects in the inheritance of all the characters. Estimates of GCA effects indicated that parents RBU 36, RBU 28 and RUD 59 were good general combiner for most of the nitrogen fixing traits. These parents could be further used in breeding programme. The crosses T9 x BGU 54, 99-U-27 x RUD 59 and RBU 28 x RBU 38 were best specific combiner for various nitrogen fixing characters. However, these crosses could be used as a high nitrogen fixing capacity for the varietal development by adopting the pedigree method.

Keywords: Blackgram (Vigna mungo L.), Combing ability and Nitrogen fixation

Legumes are "Unique jewels of Indian crop husbandry" (Swaminathan, 1981), through their recognized role in restoring soil fertility and valued for protein rich food, feed and fodder. Pulse crops are grown over an area of 238.19 lakh hectares with production of 148.09 lakh tones (Anonymous, 2001). The country would need at least 23 million tons of pulses by 2005 A.D. and 30 million tonnes by 2020 AD (Anonymous, 2000). High yielding varieties developed in blackgram could not give desired productivity mainly due to their high input requirements like costly chemical fertilizers which are unaffordable by the poor farmers. Thus, nitrogen is one of the major plant nutrient and becomes a limiting factor in realizing yield potential of high yielding varieties, so to increase biological nitrogen fixation, there is a requirement to understand the genetic

behavior of nitrogen fixation to evolve high nitrogen fixing genotype(s). For breeding high yielding varieties of crop plants, combining ability analysis is one of the powerful tools available which provides information on the nature and magnitude of genetic variance for components of productivity. Further estimation of combining ability effects would also enable to discriminate efficient and productive genotypes for creating more productive resource base. The concept of combining ability was developed by Sprague and Tatum (1942). According to them general combining ability (GCA) measures the average performance of line in hybrid combinations, while specific combining ability (SCA) measures the deviation of certain expected combinations on the basis of average performance of lines involved.


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Griffing (1956) presented statistical concept of general and specific combining ability. The general combining ability involved additive and additive x additive interaction, where as, specific combining ability measures dominance, additive x dominance and dominance x dominance interactions. Hence, estimation of combining ability becomes essential in identifying superior parents as they reflet on the performance of parents and also to isolate heterotic crosses in several breeding programmes.

MATERIALS AND METHODS The present investigation was conducted at Plant Breeding Farm of Rajasthan College of Agriculture, Udaipur during Kharif, 2000. The material comprising of ten genetical and geographical diverse genotypes of blackgram selected on the basis of their nitrogen fixing ability and seed yield. The cross were attempted in half diallel at Plant Breeding Farm, Rajasthan College of Agriculture, Udaipur. Parents used in the present investigation: Dungla, T9, RBU28, RBU38, TPU4, 99-U-27, RUD79, RUD59, BGU102, BGU54. The 45 F1s along with 10 parents were grown in two environments (first (E1), control; second (E2) use of Rhizobium urd culture for seed treatment) in randomized block design with three replications during kharif, 2000. Each parent and F1 were planted in single row plot of 2 meter length with 30 and 10 cm row to row and plant to plant spacing, respectively. Irrigations were given as and when required. Recommended agronomical practices were followed to raise the successful crop, no supplement nitrogen fertilizer was given. The observation on the following traits were recorded on five representative plants grown adjacent to one another were selected from each replication in both the environments after 45 days of

sowing. Number of nodules per plant, Nodules fresh weight per plant (mg), Nodules dry weight per plant (mg), Plant fresh weight (g), Plant dry weight (g), Nitrogen content of plant (mg/gm) by Snell and Snell (1939), Carbohydrate content (%) by Hedge and Hofreiter (1962), Nitrate reductase activity (min/mg protein) by Hageman and Read (1980), Leghaemoglobin content (%) by Appleby and Bergersen, (1980) Combining Ability Analysis (Individual environment) : The combining ability analysis was performed for each environment separately by method 2 Model I of Griffing (1956).

RESULTS AND DISCUSSION Analysis of variance for combining ability revealed that mean squares due to general combining ability (GCA) and specific combining ability (SCA) was significant for all the characters studied (Table 1), this indicated that both additive and non additive genes played a role in the inheritance of these characters. According to Jinks (1954), Griffing (1956) and Hayman (1957) reported general combining ability is attributed to additive, additive x additive interaction and is fixable in nature. While on the other hand specific combining ability is attributed to non additive i.e. dominance and dominance based interactions and is non-fixable. If the random sample of the selected parents is considered the ratio of 2SCA: 2GCA was greater than one for all the characters their by indicating the preponderance of non additive variance in blackgram for the expression of these traits under study. Combining Ability Effects : Characterwise results for general combining ability of parents (gi) and specific combining ability effects (Sij) are given below (Table 3 & 4) :


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Number of nodules per plant : Pooled analysis revealed that out of 10 parents, 4 parents exhibited significant positive GCA effects for number of nodules per plant. Parent RBU 38 (4.66) expressed highest significant positive GCA effects at par with T9 (4.41) followed by RUD 79 (3.02) and RUD 59 (2.82). Among the crosses, 17 crosses recorded significant positive SCA effects, with the range varying from 3.67 (RBU 28 x RUD 59) to 11.42 (TPU 4 x 99-U-27). Other best cross combinations having values near to highest one were T9 x BGU 54 (9.31), RUD 79 x RUD 59 (8.68), RBU 28 x RBU 38 (7.85), Dungla x BGU 54 (7.81) and BGU 102 x BGU 54 (6.94). Nodules fresh weight per plant : Significant positive GCA effects for nodules fresh weight per plant was recorded for five parents in all the three analysis. The highest magnitude of positive GCA effects was depicted by parents RBU 38 (43.23) at par with RUD 59 (39.54) followed by RBU 28 (27.59) and RUD 79 (20.01) in E1, RUD 59 (65.83) followed by RBU 38 (41.55), RBU 28 (23.74) and RUD 79 (21.88) in E2 and RUD 59 (52.68) followed by RBU 38 (42.39), RBU 28 (25.67) and RUD 79 (20.95) in pooled analysis. While, lowest magnitude of significant positive GCA effects was registered for parents T9 (10.57, 6.63 and 8.60 in E1, E2 and in pooled analysis, respectively). A perusal of SCA effects for this trait revealed that out of 45 crosses evaluated 15 crosses in E1, 22 crosse sin E2 and 22 crosses on pooled basis were depicted significant positive SCA effects, out of them 12 crosses were common in all the three analysis. It ranged from 31.23 (Dungla x BGU 54) to 75.37 (RBU 28 x 99-U-27) in E1 21.99 (T9 x RUD 79) to 106.10 (TPU 4 x RUD 59) in E2 and 19.15 (Dungla x RUD 79) to 84.17 (T9 x

BGU 54) on pooled basis. Other important crosses were recorded viz., 99-U-27 x RUD 59 (70.42), T9 x BGU 102 (64.98), TPU 4 x BGU 54 (56.79) and RBU 28 x RBU 38 (55.06) in E1, Dungla x BGU 102 (87.24), Dungla x BGU 54 (84.38) and RUD 79 x RUD 59 (74.80) in E2 and RBU 28 x 99-U-27 (70.57), Dungla x BGU 102 (67.56) and TPU 4 x RUD 59 (66.01) on pooled basis. Nodules dry weight per plant : Five parents viz., T9 (1.39, 1.94 and 1.67), RBU 28 (3.62, 3.75 and 3.68), RBU 38 (6.92, 4.83 and 5.88), RUD 79 (3.70, 2.39 and 3.04) and RUD 59 (6.89, 9.06 and 7.98) exhibited significant and positive GCA effects in both E1 and E2 as well as over the environments. Highest magnitude of positive GCA effects for nodules dry weight per plant was depicted by RBU 38 statistically at par with RUD 59 in E1 and RUD 59 in E2 and pooled analysis. Significant positive SCA effects were observed in 19 crosses in E1, 18 crosses in E2 and 22 crosses in pooled analysis. Out of them, 13 crosses were common to all the three analysis. However, the highest magnitude of SCA effects was recorded under E1 in cross RBU 28 x 99-U-27 (11.22) statistically at par with 99-U-27 x RUD 59 (10.61), T9 x BGU 54 (10.36) and RBU 28 x RBU 38 (9.50). While under E2 and pooled analysis the estimate of positive SCA effects were highest in cross T9 x BGU 54 (18.96 and 14.66). Plant fresh weight : The GCA effects of four parents viz. RBU 28, RBU 38, RUD 79 and RUD 59 were positive and significant in all the three analysis i.e. E1, E2 and pooled. Highest magnitude of GCA effects were recorded for RBU 38 both under E1 (1.37) and pooled analysis (1.53) and RUD 59 under E2 (1.95).


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Among the cross combinations, 20 crosses in E1, 22 crosses in E2 and 25 crosses on pooled basis exhibited significant positive SCA effects. Whereas 19 crosses were common in all the three analysis. The magnitude of positive SCA effects varied from 0.85 (RUD 79 x BGU 102) to 3.94 (T9 x BGU 54) in E1, 1.19 to 4.70 (Dungla x RBU 38) to 4.70 (T9 x BGU 54) in E2 and 0.70 (RUD 79 x BGU 54) to 4.32 (T9 x BGU 54) in pooled analysis. Plant dry weight : A perusal of data revealed that four parents viz., RBU 28, RBU 38, RUD 79 and RUD 59 were expressed significant and positive GCA effects in all the three analysis. The estimates varied from 0.10 (RUD 79) to 0.19 (RBU 38) in E1, 0.09 (RBU 28) to 0.26 (RUD 59) in E2 and 0.10 (RUD 79) to 0.21 (RBU 38) in pooled analysis. However, SCA effects of 20 crosses in E1, 22 crosses in E2 and 25 crosses in pooled analysis were positive and significant. Out of them, 18 crosses were common in both the environments as well as over the environments. Maximum magnitude of positive SCA effects were exhibited by cross T9 x BGU 54 in both E1 (0.49) and in pooled analysis (0.52) and cross TPU 4 x 99-U-27 (0.62) in E2. Other important crosses were RUD 79 x RUD 59 (0.43), TPU 4 x 99-U-27 (0.41) and RBU 28 x RBU 38 (0.35) in E1, T9 x BGU 54 (0.54), TPU 4 x RUD 59 (0.49) and RBU 28 x RBU 38 (0.46) in E2, while TPU 4 x RUD 59 as well as RUD 79 x RUD 59 (0.41) and RBU 28 x RBU 38 as well as 99-U-27 x RUD 59 in pooled analysis. Nitrogen content of plant : The estimates of GCA effects revealed that out of 10 parents evaluated, 5 parents each in E1, 4 parents in E2 and 5 parents on pooled basis, showed significant

positive GCA effects for nitrogen content of plant. Out of them 4 parents viz. RBU 28, RBU 38, RUD 79 and RUD 59 were common in all the three analysis. The maximum and minimum values for significant positive GCA effects were observed viz., 1.54 (RBU 28) and 0.48 (RUD 59) in E1, 1.71 (RBU 38) and 0.70 (RUD 59) in E2, while 1.59 (RBU 38) and 0.42 (T9) in pooled analysis. The significant positive SCA effects were observed in 15 crosses in E1, 14 crosses in E2 and 18 crosses in pooled analysis. Out of them 12 crosses were common in all the three analysis. The range for positive significant SCA effects varied from 0.79 (Dungla x BGU 54) to 2.63 (RBU 28 x RBU 38) in E1, 0.94 (BGU 102 x BGU 54) to 3.24 (Dungla x BGU 54) in E2 and 0.57 (Dungla x RBU 38) to 2.59 (RBU 28 x RBU 38) in pooled analysis. Other better combinations having values near to highest were 99-U-27 x RUD 59 (2.51), T9 x BGU 54 (2.25) and Dungla x BGU 102 (2.07) in E1, T9 x RUD 59 (3.01), 99-U-27 x RUD 59 (2.67) and RBU 28 x RBU 38 (2.55) in E2, while Dungla x BGU 54 (2.01) and T9 x RUD 59 (2.00) in pooled analysis. Carbohydrate content : Significant GCA effects for low carbohydrate content were recorded in five parents each in E1 & pooled basis and 4 parents in E2. Out of them parents viz., RBU 28, RBU 38, RUD 79 and RUD 59 were common in all the three analysis. The estimates of significant negative GCA effects ranged from -0.61 (T9) to -1.99 (RBU 28) in E1, -0.84 (RUD 79) to -1.82 (RBU 38) in E2 and 0.38 (T9) to -1.73 (RBU 38) in pooled analysis. The SCA effects for low carbohydrate content were significant in 17 crosses in E1, 15 crosses in E2 and 21 crosses in


10

pooled analysis with crosses common in all the three analysis. The magnitude of significant negative SCA effects varied from -0.94 (T9 x RBU 28) to -4.38 (TPU 4 x RUD 59) in E1, -0.96 (Dungla x RBU 38) to -3.65 (T9 x BGU 54) in E2 and -0.68 (T9 x BGU 102) to -3.92 (T9 x BGU 54) in pooled analysis. Nitrate reductase activity : Pooled analysis of GCA effects revealed that out of 10 parents evaluated, six parents exhibited significant positive GCA effects. The highest magnitude of positive GCA effects was depicted by RBU 38 (15.28) followed by RBU 28 (10.56), RUD 59 (7.95) and T9 (7.59). The significant positive SCA effects in pooled analysis were recorded for 12 crosses. Higher magnitude of SCA effects were exhibited by the T9 x BGU 54 (16.29) exhibited by statistically at par with T9 x 99-U-27 (15.88), RBU 28 x RUD 59 (15.78), RBU 28 x RBU 38 (15.43), RBU 38 x RUD 79 (14.58) and Dungla x TPU 4 (14.46) followed by Dungla x RBU 38 (13.26 and T9 x RUD 59 (10.86). Leghaemoglobin content : Pooled analysis revealed that among 10 parents studied, 4 parents exhibited significant positive GCA effects for leghaemoglobin content. The highest magnitude of significant positive GCA effects was 0.32 (RBU 38), followed by 0.26 (RBU 28), 0.12 (T9) and 0.07 (RUD 59). Estimates of significant positive SCA effects for leghaemoglobin content were expressed in 8 crosses in E1, 13 crosses in E2 and 15 crosses in pooled analysis. Six crosses viz. Dungla x RUD 79, Dungla x RUD 59, T9 x BGU 54, RBU 38 x RUD 79, 99-U-27 x RUD 59 and BGU 102 x BGU 54 exhibited positive significant SCA effects in all the three analysis. The values of significant positive SCA effects ranged from 0.22 (BGU 102 x BGU 54) to 0.38 (Dungla x

RUD 59 in E1, 0.17 (Dungla x RUD 79 and TPU 4 x BGU 54 as well as 99-U-27 x RUD 79) to 0.43 (T9 x RUD 59) in E2 and 0.13 (Dungla x BGU 54) to 0.39 (Dungla x RUD 59) in pooled analysis. Mean squares due to interactions GCA x environment and SCA x environment were significant for most of the characters except number of nodules per plant, nitrate reductase activity. whereas leghaemoglobin in GCA x environmental and SCA x environmental interaction indicated that both GCA and SCA were significantly influenced by the environment. The parental lines RBU 38, RUD 59 and RBU 28 were good general combiner for most of the characters in E1, E2 and over the environments, whereas, RUD 79 on pooled basis for most of the characters, T9 for number of nodules per plant, leghaemoglobin content exhibited good general combining ability. The above-mentioned parents with high GCA could be used in hybridization programme for the improvement of respective traits as the GCA effects attributed to additive, additive x additive gene activity. Estimates of specific combining ability effects provide information on the role of dominance and its interaction for the expression of heterosis. The cross combinations having specific combining ability effects will be useful, if the parents involved are also good general combiner specially in the self pollinated crops (Sprague & Tatum, 1942 and Hayman, 1957). Significant SCA effect for nitrogen content of plant, among hybrids RBU 28 x RBU 38 and 99-U-27 x RUD 59 in both the environments as well as on pooled basis possessed higher SCA effects. Parents of these hybrids also performed good general combining ability. Positive significant SCA effects for seed yield per plant were exhibited by 20 in E1, 21 in


11

E2 and 24 crosses on pooled basis. Cross T9 x BGU 54 in both the environments whereas, 99-U-27 x RUD 59 and RBU 28 x 99-U-27 in E1 and over the environments, respectively, were promising hybrids for the above said character. Parent RUD 59 and RBU 28 also performed well for general combining ability. Among 45 cross combinations T9 x BGU 54 showed good SCA effects for nodules fresh weight per plant, nodules dry weight per plant, plant fresh weight, plant dry weight, carbohydrate content, seed yield per plant and biological yield in all the three analysis whereas, nitrogen content of plant in E1 and number of nodules, nitrate reductase activity, leghaemoglobin content, days to 50% flowering, number of pods per plant, number of seeds per plant, nitrogen content of seed and straw in pooled analysis. Cross RUD 79 x RUD 59 and TPU 4 x RUD 59 performed good SCA for characters like nodules fresh weight per plant, plant fresh weight, plant dry weight, days to 75% maturity and biological yield, whereas 99-U-27 x RUD 59 exhibited high SCA effects for plant fresh weight, plant dry weight, nitrogen content of plant, carbohydrate content,

nitrate reductase activity, leghaemoglobin content. An efficient breeding programme which take into consideration of parents with desirable traits and good general combining ability for nitrogen content of plant which in cross combination may result in high specific combining ability. The present study revealed that the hybrids RBU 28 x RBU 38 and 99-U-27 x RUD 59 were most promising for nitrogen content of plant on the basis of SCA effects, and parents of these hybrids also performed good general combining ability. Besides this, cross T9 x BGU 54 in both the environments where as 99-U-27 x RUD 59 and RBU 28 x 99-U-27 in E1 and pooled analysis were promising for seed yield per plant. Hybrid T9 x BGU 54 was promising for nodules fresh weight per plant, nodules dry weight per plant, plant fresh weight, plant dry weight, carbohydrate content. These crosses involved at least one parent with high degree of general combining ability and thus can be utilized in further breeding programme to produce segregants of fixable nature in segregating generations following simple pedigree method.

Table 1: Analysis of variance for combining ability over the environments for

ninteen characters in blackgram (Vigna mungo L. Hepper) Source d.f Mean square Number

of nodules

per plant

Nodules fresh weight

per plant (mg)

Nodules dry weight per plant

(mg)

Plant fresh

weight (gm)

Plant dry weight (gm)

Nitrogen content of

plant (mg/gm)

Carbohydrate content

(%) Nitrate

reductase activity (mg/min protein)

Leghaemoglobin content

(%)

Environment

1 8566.751**

195808.009**

5798.804**

152.143**

2.780** 136.993**

71.084** 1560.681**

2.258** GCA 9 312.787*

* 36222.768**

++ 776.887**

++ 30.357**

++ 0.553**

++ 34.847**

++ 42.269**+

+ 3329.794

** 0.959**

SCA 45 81.071** 5437.637**++

131.715**++

11.024**++

0.202**++

4.453**++

6.643**++ 162.473**

0.078**++ GCA x Env.

9 13.800 1436.721** 32.572** 1.626** 0.027** 1.014** 1.288** 17.088 0.008

SCA x Env.

45 7.259 426.212** 12.584** 0.628** 0.010** 0.435** 0.516** 6.221 0.013*


12

Error 216

7.258 212.205 5.634 0.189 0.004 0.188 0.227 11.545 0.008

2GCA: 2SCA

1:14.495 1:8.706 1:9.808 1:21.548 1:21.582 1:7.383 1:9.157 1:2.729 1:4.406

2(GCAxE): 2(SCAxE)

1: 0.012 1:10.486 1:15.479 1:18.305 1:15.411 1:17.961 1:16.374 1:57.632 1:25.171

Table 2: Analysis for combining ability in each environment for different

characters in blackgram (Vigna mungo L. Hepper) Source d.f. Mean square Nodules

fresh weight per plant

(mg)

Nodules dry weight per plant (mg)

Plant fresh weight (gm)


Nitrogen content of

plant (mg/gm)

Carbohydrate content (%)

Leghaemoglobin content (%)

GCA E1 E2

9 9 15101.544

** 22557.946**

371.063** 438.395**

11.856** 20.127**

0.225** 0.355**

18.201** 17.659**

25.375** 18.182**

0.438** 0.529**

SCA E1 E2

45 45 2239.879

** 3623.971**

54.344** 89.955**

4.275** 7.376**

0.079** 0.133**

1.902** 2.986**

3.862** 3.298**

0.042** 0.049**

Error E1 E2

108 108

279.598 144.812

4.700 6.568

0.209 0.170

0.005 0.004

0.172 0.205

0.227 0.227

0.009 0.007

2GCA: 2SCA

E1 E2

1:7.935 1:9.313

1:8.130 1:11.586

1:20.950 1:21.665

1:20.369 1:21.943

1:5.758 1:9.561

1:8.671 1:10.264

1:4.652 1:4.767

**- Significant at 1% level, respectively. Table 3: Estimation of GCA effects for various characters in blackgram

Parents

Number of nodules/plant Nodules fresh weight/plant (mg)

Nodules dry weight/plant (mg)

E1 E2 Pool E1 E2 Pool E1 E2 Pool

Dungla

-2.62**

-3.11**

-2.86* -9.68* 4.94 -2.37 -2.13**

1.03 -0.55

T9 3.66** 5.16** 4.41**

10.57* 6.63* 8.60** 1.39* 1.94** 1.67**

RBU 28

1.17 2.01* 1.59**

27.59** 23.74** 25.67**

3.62** 3.75** 3.68**

RBU 38

4.83** 4.48** 4.66**

43.23** 41.55** 42.39**

6.92** 4.83** 5.88**

TPU 4 -4.67**

-3.51**

-4.09* -45.57**

-29.42**

-37.50* -7.63**

-4.19** -5.91*

99-U-27

-2.47**

-3.38**

-2.93* -63.41**

-83.34**

-73.37* -8.80**

-12.00**

-10.40*

RUD 79

3.69** 2.35** 3.02**

20.01** 21.88** 20.95**

3.70** 2.39** 3.04**

RUD 59

1.41* 4.23** 2.82**

39.54** 65.83** 52.68**

6.89** 9.06** 7.98**

BGU 102

-3.10**

-5.05**

-4.07* -1.63 -8.67** -5.15 -0.24 -0.75 -0.50

BGU 54

-1.90**

-3.18**

-2.54* -20.66**

-43.14**

-31.90* -3.72**

-6.06** -4.89*

Gi 0.70 0.77 0.52 4.58 3.30 2.82 0.59 0.70 0.46 Gi-Gj 1.05 1.15 0.78 6.83 4.91 4.21 0.89 1.05 0.69


13

ContinuedTable 3: Estimation of GCA effects for various characters in blackgram Parents


Nitrogen content of plant (mg/gm)

Carbohydrate content (%)


Dungla -0.06** -0.05** -0.05* -0.63** -0.84** -0.73* 0.52** 0.02 0.27** T9 0.02 -0.00 0.01 0.64** 0.20 0.42** -0.61** -0.15 -0.38* RBU 28

0.14** 0.09** 0.12** 1.54** 1.31** 1.43** -1.99** -1.28** -1.63*

RBU 38

0.19** 0.24** 0.21** 1.46** 1.71** 1.59** -1.64** -1.82** -1.73*

TPU 4 -0.22** -0.17** -0.19* -1.56** -0.56** -1.06* 1.30** 0.45** 0.87** 99-U-27

-0.17** -0.26** -0.22* -0.71** -0.97** -0.84* 1.25** 1.06** 1.16**

RUD 79

0.10** 0.11** 0.10** 1.35** 1.36** 1.36** -1.25** -0.84** -1.04*

RUD 59

0.12** 0.26** 0.19** 0.48** 0.70** 0.59** -1.01** -0.88** -0.94*

BGU 102

-0.04* -0.10** -0.07* -1.41** -1.59** -1.50* 1.73** 1.71** 1.72**

BGU 54

-0.08** -0.12** -0.10* -1.17** -1.32** -1.25* 1.70** 1.72** 1.71**

Gi 0.02 0.02 0.01 0.11 0.12 0.08 0.13 0.13 0.09 Gi-Gj 0.03 0.03 0.02 0.17 0.18 0.13 0.19 0.19 0.14

Continued.Table3: Estimation of GCA effects for various characters in blackgram

Parents

Nitrate reductase activity (min/mg protein)

Plant fresh weight (gm) Leghaemoglobin content (%)


Dungla 6.24** 6.25** 6.25** -0.44** -0.12 -0.28* -0.01 0.02 0.01 T9 7.13** 8.06** 7.59** 0.16 -0.02 0.07 0.13** 0.11** 0.12** RBU 28

12.22** 8.91** 10.56** 0.92** 0.65** 0.79** 0.25** 0.26** 0.26**

RBU 38

15.65** 14.90** 15.28** 1.37** 1.69** 1.53** 0.32** 0.32** 0.32**

TPU 4 -7.49** -4.09** -5.79* -1.52** -1.20** -1.36* -0.17** -0.17** -0.17* 99-U-27

-14.90** -15.50** -15.20* -1.40** -2.11** -1.76* -0.15** -0.18** -0.17*

RUD 79

3.49** 3.75** 3.62** 0.75** 0.83** 0.79** 0.03 0.04 0.03

RUD 59

8.16** 7.73** 7.95** 0.91** 1.95** 1.43** 0.04 0.11** 0.07**

BGU 102

-13.53** -12.62** -13.08* -0.25 -0.71** -0.48* -0.22** -0.28** -0.25*

BGU 54

-16.97** -17.40** -17.18* -0.49** -0.95** -0.72* -0.21** -0.24** -0.23*

Gi 0.93 0.93 0.66 0.13 0.11 0.08 0.03 0.02 0.02 Gi-Gj 1.39 1.39 0.98 0.19 0.17 0.13 0.04 0.03 0.03


14

Table 4.1: Estimation of SCA effects for number of nodules per plant, nodules fresh weight per plant and nodules dry weight per plant in blackgram

Crosses

Number of nodules/plant Nodules fresh weight/plant (mg) Nodules dry weight/plant (mg)

E1 E2 Pool E1 E2 Pool E1 E2 Pool Dungla x T9 3.06 4.36 3.71* 15.01 24.27* 19.64* 2.44 3.54 2.99 Dungla x RBU 28 -3.71 -5.10 -4.40* -22.02 -40.84** -31.43** -2.78 -5.60* -4.19** Dungla x RBU 38 -0.11 4.08 1.98 28.67 35.69** 32.18** 2.25 4.99* 3.62* Dungla x TPU 4 2.65 6.12* 4.38* 6.48 10.99 8.74 5.47** 4.35 4.91** Dungla x 99-U-27 -8.16** -6.32* -7.24** -0.69 -1.42 -1.06 -1.36 0.82 -0.27 Dungla x RUD 79 0.68 -1.99 -0.66 26.90 0.35 13.62 1.14 0.43 0.78 Dungla x RUD 59 3.54 -0.46 1.54 4.70 -9.26 -2.28 2.61 -3.57 -0.48 Dungla x BGU 102 1.44 1.90 1.67 47.87** 87.24** 67.56** 3.41 14.90** 9.16** Dungla x BGU 54 7.43** 8.18** 7.81** 31.23* 84.38** 57.81** 4.22* 10.54** 7.38** T9 x RBU 28 2.50 -0.27 1.11 -61.60** -65.20** -63.40** -7.64** -10.51** -9.08** T9 x RBU 38 3.39 -0.10 1.64 41.42** 59.33** 50.38** 3.39 8.40** 5.90** T9 x TPU 4 5.00* 1.19 3.09 -10.10 -18.37 -14.24 0.94 -4.23 -1.65 T9 x 99-U-27 -5.49* -1.48 -3.48* -48.27** -72.79** -60.53** -5.22* -11.10** -8.16** T9 x RUD 79 -1.34 1.82 0.24 16.31 21.99* 19.15* 3.61 2.52 3.06* T9 x RUD 59 -3.52 3.08 -0.22 1.79 24.05* 12.92 -2.25 6.18* 1.96 T9 x BGU 102 1.07 -2.19 -0.56 52.29** 12.21 32.25** 6.55** -0.35 3.10* T9 x BGU 54 6.81** 11.82** 9.31** 64.98** 103.35** 84.17** 10.36** 18.96** 14.66** RBU 28 x RBU 38 6.19* 9.51** 7.85** 55.06** 70.21** 62.64** 9.50** 13.27** 11.38** RBU 28 x TPU 4 2.11 0.95 1.53 25.20 43.19** 34.19** 5.72** 5.29* 5.51** RBU 28 x 99-U-27 0.96 4.16 2.56 75.37** 65.77** 70.57** 11.22** 9.77** 10.49** RBU 28 x RUD 79 -1.02 3.75 1.37 24.95 2.21 13.58 5.05* 0.38 2.71 RBU 28 x RUD 59 5.52* 1.81 3.67* 36.09* 47.60** 41.85** 7.86** 7.71** 7.78** RBU 28 x BGU 102 2.34 6.45* 4.39* 0.59 -16.23 -7.82 2.66 -3.15 -0.24 RBU 28 x BGU 54 -0.10 3.35 1.62 -26.38 -2.76 -14.57 -2.53 0.82 -0.85 RBU 38 x TPU 4 -2.59 -8.37** -5.48** -37.44* -58.62** -48.03** -4.25* -10.46** -7.35** RBU 38 x 99-U-27 2.05 3.29 2.67 0.73 23.30* 12.01 2.58 1.68 2.13 RBU 38 x RUD 79 4.67 5.38* 5.02** 38.98* 14.41 26.69** 4.41* 3.63 4.02** RBU 38 x RUD 59 2.05 5.36* 3.70* 40.79** 47.13** 43.96** 6.22** 10.63** 8.42** RBU 38 x BGU 102 -3.94 -2.96 -3.45 -65.05** -39.37** -52.21** -9.31** -5.90* -7.60** RBU 38 x BGU 54 1.90 2.18 2.04 19.31 19.77 19.54* 4.83* 2.74 3.78* TPU 4 x 99-U-27 10.81** 12.03** 11.42** 8.87 33.94** 21.40* 5.47** 5.71* 5.59** TPU 4 x RUD 79 0.32 -6.38* -3.03 27.12 53.38** 40.25** 6.64** 11.32** 8.98** TPU 4 x RUD 59 2.32 7.73** 5.02** 25.92 106.10** 66.01** 2.44 11.99** 7.21** TPU 4 x BGU 102 -2.46 -3.42 -2.94 -57.58** -79.06** -68.32** -8.09** -12.21** -10.15** TPU 4 x BGU 54 -2.97 -3.56 -3.27 56.79** -34.92** 10.93 -5.61** -2.23 -3.92* 99-U-27 x RUD 79 1.52 6.99** 4.26* -68.05** -44.37** -56.21** -13.86** -7.21** -10.54** 99-U-27 x RUD 59 9.28** 4.43 6.85** 70.42** 53.35** 61.89** 10.61** 11.13** 10.87** 99-U-27 x BGU 102 0.45 1.76 1.11 32.26* 48.52** 40.39** 5.41** 6.93** 6.17** 99-U-27 x BGU 54 1.87 0.86 1.36 19.95 5.99 12.97 2.55 -0.76 0.90 RUD 79 x RUD 59 7.47** 9.88** 8.68** 42.67** 74.80** 58.74** 4.11* 8.07** 6.09** RUD 79 x BGU 102 5.26* 5.63* 5.44** 6.51 23.96* 15.24 2.91 1.21 2.06 RUD 79 x BGU 54 2.31 5.97* 4.14* 14.87 11.10 12.99 4.05* -1.15 1.45 RUD 59 x BGU 102 1.61 2.74 2.18 2.98 -21.31 -9.17 0.72 -1.46 -0.37


15

RUD 59 x BGU 54 -5.09* -8.36** -6.72** -71.66** -92.17** -81.92** -8.14** -16.82** -12.48** BGU 102 x BGU 54 7.46** 6.43* 6.94** 35.17* 42.99** 39.08** 8.00** 7.32** 7.66** Sii 2.12 2.33 1.57 13.81 9.94 8.51 1.79 2.12 1.39 Sij 2.36 2.60 1.75 15.40 11.08 9.49 2.00 2.36 1.55 Sij-Sik 3.47 3.82 2.58 22.64 16.29 13.95 2.94 3.47 2.27 Sij-Skl 3.31 3.64 2.46 21.59 15.54 13.30 2.80 3.31 2.17 Table 4.2: Estimation of SCA effects for plant fresh weight, plant dry weight and nitrogen

content of plant in blackgram Crosses

Plant fresh weight (gm) Plant dry weight (gm) Nitrogen content of plant (mg/gm)

E1 E2 Pool E1 E2 Pool E1 E2 Pool Dungla x T9 0.72 1.35** 1.04** 0.15* 0.14* 0.14** 0.16 0.49 0.32 Dungla x RBU 28 -0.42 -0.54 -0.48 -0.01 -0.17** -0.09* -0.56 -1.49** -1.02** Dungla x RBU 38 2.76** 1.19** 1.97** 0.27** 0.11 0.19** 0.15 1.00* 0.57* Dungla x TPU 4 -0.44 -0.32 -0.38 -0.05 0.04 -0.00 0.05 -0.37 -0.16 Dungla x 99-U-27 -2.73** -2.54** -2.63** -0.22** -0.37** -0.29** -2.01** -2.44** -2.22** Dungla x RUD 79 0.54 0.70 0.62* 0.10 0.16** 0.13** 0.88* 0.44 0.66* Dungla x RUD 59 -0.55 -0.23 -0.39 -0.14* -0.09 -0.12** -0.02 -1.07* -0.54 Dungla x BGU 102 1.98** 2.17** 2.08** 0.33** 0.37** 0.35** 2.07** 1.09* 1.58** Dungla x BGU 54 0.19 2.71** 1.45** -0.04 0.34** 0.15** 0.79* 3.24** 2.01** T9 x RBU 28 0.28 -1.70** -0.71* 0.01 -0.10 -0.04 0.08 0.04 0.06 T9 x RBU 38 1.17** 1.81** 1.49** 0.20** 0.29** 0.24** 2.00** 1.97** 1.99** T9 x TPU 4 -1.57** -2.47** -2.02** -0.36** -0.45** -0.41** -0.03 -2.11** -1.07** T9 x 99-U-27 -2.69** -3.81** -3.25** -0.37** -0.44** -0.40** -1.63** -1.53** -1.58** T9 x RUD 79 0.93* 2.01** 1.47** 0.15* 0.33** 0.24** -0.89* -0.54 -0.71* T9 x RUD 59 -1.39** 0.64 -0.38 -0.16* 0.04 -0.06 0.98* 3.01** 2.00** T9 x BGU 102 1.48** 1.82** 1.65** 0.11 0.14* 0.13** 0.64 0.78 0.71* T9 x BGU 54 3.94** 4.70** 4.32** 0.49** 0.54** 0.52** 2.25** 1.07* 1.66** RBU 28 x RBU 38 2.20** 2.65** 2.42** 0.35** 0.46** 0.40** 2.63** 2.55** 2.59** RBU 28 x TPU 4 2.28** 2.68** 2.48** 0.30** 0.40** 0.35** 0.11 -1.69** -0.79** RBU 28 x 99-U-27 2.28** 2.08** 2.18** 0.36** 0.36** 0.36** 0.45 0.11 0.28 RBU 28 x RUD 79 -0.77 0.18 -0.29 -0.11 -0.10 -0.10* -0.24 -0.63 -0.44 RBU 28 x RUD 59 1.92** 3.21** 2.56** 0.16* 0.31** 0.24** 1.27** 1.80** 1.53** RBU 28 x BGU 102 -1.16** -1.46** -1.31** -0.09 -0.07 -0.08 -0.26 -0.36 -0.31 RBU 28 x BGU 54 -1.26** -0.84* -1.05** -0.19** -0.14* -0.16** -0.12 1.11** 0.50 RBU 38 x TPU 4 -3.19** -4.39** -3.79** -0.44** -0.65** -0.54** -1.42** -1.84** -1.63** RBU 38 x 99-U-27 1.53** 0.70 1.12** 0.17** 0.05 0.11* -0.34 -0.51 -0.43 RBU 38 x RUD 79 1.48** 1.37** 1.43** 0.27** 0.26** 0.27** 0.53 0.34 0.44 RBU 38 x RUD 59 -0.31 1.90** 0.80** 0.01 0.22** 0.11** 1.97** 2.02** 1.99** RBU 38 x BGU 102 -2.08** -2.42** -2.25** -0.34** -0.32** -0.33** -2.67** -2.70** -2.68** RBU 38 x BGU 54 -0.19 -0.01 -0.10 -0.04 -0.01 -0.02 0.29 -0.08 0.10 TPU 4 x 99-U-27 3.04** 4.67** 3.86** 0.41** 0.62** 0.51** 0.92* 2.30** 1.61** TPU 4 x RUD 79 0.14 -0.47 -0.16 0.03 -0.09 -0.03 0.79* 0.76 0.78** TPU 4 x RUD 59 2.57** 3.50** 3.03** 0.34** 0.49** 0.41** -0.06 0.60 0.27 TPU 4 x BGU 102 -1.06* -1.25** -1.15** -0.05 -0.17** -0.11* -0.65 -0.54 -0.59* TPU 4 x BGU 54 0.36 0.54 0.45 0.04 0.08 0.06 -0.44 0.64 0.10


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99-U-27 x RUD 79 -0.99* -1.06** -1.02** -0.15* -0.25** -0.20** -0.04 0.17 0.07 99-U-27 x RUD 59 1.95** 3.18** 2.56** 0.35** 0.44** 0.40** 2.51** 2.67** 2.59** 99-U-27 x BGU 102 -0.88* -0.87* -0.87** -0.21** -0.10 -0.15** -0.48 -0.75 -0.62* 99-U-27 x BGU 54 2.76** 2.49** 2.62** 0.32** 0.29** 0.31** 1.35** 0.39 0.87** RUD 79 x RUD 59 3.22** 2.92** 3.07** 0.43** 0.38** 0.41** 1.21** 1.55** 1.38** RUD 79 x BGU 102 0.85* 1.76** 1.30** 0.14* 0.28** 0.21** 0.26 0.69 0.48 RUD 79 x BGU 54 0.70 0.70 0.70* 0.12 0.09 0.10* 0.54 0.66 0.60* RUD 59 x BGU 102 1.81** 1.28** 1.55** 0.13* 0.18** 0.15** -1.15** -0.26 -0.71* RUD 59 x BGU 54 -3.34** -5.40** -4.37** -0.40** -0.71** -0.55** -2.20** -4.51** -3.35** BGU 102 x BGU 54 0.86* 1.89** 1.38** 0.22** 0.29** 0.25** 0.87* 0.94* 0.90** Sii 0.38 0.34 0.25 0.06 0.05 0.04 0.34 0.37 0.25 Sij 0.42 0.38 0.28 0.06 0.06 0.04 0.38 0.42 0.28 Sij-Sik 0.62 0.56 0.42 0.09 0.08 0.06 0.56 0.61 0.42 Sij-Skl 0.59 0.53 0.40 0.09 0.08 0.06 0.53 0.58 0.40

Table 4.3: Estimation of SCA effects for carbohydrate content, nitrate reductase activity

and leghaemoglobin content in blackgram Crosses

Carbohydrate content (%) Nitrate reductase activity (min/mg protein)

Leghaemoglobin content (%)

E1 E2 Pool E1 E2 Pool E1 E2 Pool Dungla x T9 -0.67 -1.12* -0.89** 3.15 3.93 3.54 -0.19* -0.20* -0.20** Dungla x RBU 28 1.37** 1.73** 1.55** -8.23** -5.69 -6.96** -0.06 -0.20* -0.13* Dungla x RBU 38 -1.30** -0.96* -1.13** 13.74** 12.77** 13.26** 0.16 0.28** 0.22** Dungla x TPU 4 -0.93* -0.78 -0.85** 12.07** 16.85** 14.46** 0.06 0.13 0.10 Dungla x 99-U-27 3.11** 2.80** 2.96** -4.03 -3.78 -3.91 -0.19* -0.13 -0.16** Dungla x RUD 79 -0.68 -0.83 -0.75* -5.06 -6.56* -5.81** 0.31** 0.17* 0.24** Dungla x RUD 59 0.79 0.71 0.75* -4.36 -5.26 -4.81* 0.38** 0.40** 0.39** Dungla x BGU 102 -0.80 -1.31** -1.06** 2.11 0.78 1.44 0.00 0.06 0.03 Dungla x BGU 54 -3.24** -2.52** -2.88** 9.55** 6.29* 7.92** 0.03 0.24** 0.13* T9 x RBU 28 -0.94* -1.25** -1.10** 2.88 1.56 2.22 0.13 0.02 0.08 T9 x RBU 38 -0.83 -0.83 -0.83** 3.48 4.93 4.21 0.25** 0.13 0.19** T9 x TPU 4 -0.49 -0.44 -0.46 -4.07 -6.14 -5.11* 0.15 -0.01 0.07 T9 x 99-U-27 2.19** 4.84** 3.51** -9.73** -11.97** -10.85** -0.15 -0.29** -0.22** T9 x RUD 79 1.29** 1.48** 1.38** -5.46 -6.48* -5.97** 0.05 0.08 0.06 T9 x RUD 59 -0.71 0.09 -0.31 9.05** 12.68** 10.86** 0.17 0.43** 0.30** T9 x BGU 102 -0.18 -1.18** -0.68* 5.66 2.96 4.31 -0.11 -0.05 -0.08 T9 x BGU 54 -4.18** -3.65** -3.92** 15.33** 17.25** 16.29** 0.25** 0.25** 0.25** RBU 28 x RBU 38 0.83 0.55 0.69* 10.71** 20.15** 15.43** 0.10 0.09 0.10 RBU 28 x TPU 4 1.72** 2.68** 2.20** -1.85 -3.45 -2.65 0.03 -0.09 -0.03 RBU 28 x 99-U-27 -0.13 0.61 0.24 -7.36* -8.48** -7.92** -0.01 0.00 -0.00 RBU 28 x RUD 79 0.70 1.41** 1.06** -0.46 -0.59 -0.53 -0.45** 0.09 -0.18** RBU 28 x RUD 59 -1.62** -1.89** -1.76** 14.36** 17.20** 15.78** 0.35** 0.05 0.20** RBU 28 x BGU 102 -0.96* 1.15** 0.09 -7.06* -1.66 -4.36 0.08 0.09 0.09 RBU 28 x BGU 54 -0.72 -1.57** -1.15** -9.60** -6.85* -8.22** -0.20* -0.11 -0.16** RBU 38 x TPU 4 2.67** 2.23** 2.45** -9.04** -11.78** -10.41** 0.03 -0.16* -0.06 RBU 38 x 99-U-27 -1.14* -1.14* -1.14** -4.54 -6.69* -5.61* 0.06 0.25** 0.16** RBU 38 x RUD 79 -2.09** -1.46** -1.78** 16.11** 13.04** 14.58** 0.31** 0.32** 0.32**


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RBU 38 x RUD 59 -1.52** -0.78 -1.15** 3.78 -1.14 1.32 -0.10 0.04 -0.03 RBU 38 x BGU 102 0.87* 0.68 0.78* -5.52 1.83 -1.85 -0.16 -0.23** -0.20** RBU 38 x BGU 54 0.98* 0.21 0.59 -3.87 -8.32** -6.09** 0.11 -0.14 -0.02 TPU 4 x 99-U-27 -2.93** -2.18** -2.55** 1.19 -1.36 -0.08 0.12 0.26** 0.19** TPU 4 x RUD 79 -0.52 -0.28 -0.40 -3.61 -6.22* -4.92* -0.01 0.04 0.02 TPU 4 x RUD 59 -4.38** -2.27** -3.33** 4.96 7.25* 6.10** -0.09 0.10 0.00 TPU 4 x BGU 102 0.60 0.70 0.65* -5.40 -9.09** -7.25** 0.04 -0.08 -0.02 TPU 4 x BGU 54 1.30** 1.50** 1.40** -1.94 -3.78 -2.86 0.15 0.17* 0.16** 99-U-27 x RUD 79 -1.23** -2.00** -1.62** 3.89 6.31* 5.10* 0.13 0.17* 0.15* 99-U-27 x RUD 59 -3.28** -3.49** -3.39** 15.04** 16.73** 15.88** 0.35** 0.19* 0.27** 99-U-27 x BGU 102 0.72 -0.50 0.11 3.35 0.35 1.85 -0.07 -0.05 -0.06 99-U-27 x BGU 54 0.40 0.00 0.20 -0.36 0.89 0.26 -0.06 0.13 0.04 RUD 79 x RUD 59 -1.08* -0.69 -0.88** 7.77* 7.94* 7.86** -0.18* -0.09 -0.13* RUD 79 x BGU 102 -0.96* -0.47 -0.72* 0.26 2.80 1.53 0.09 0.10 0.09 RUD 79 x BGU 54 -0.04 0.68 0.32 -2.17 2.04 -0.06 -0.09 -0.06 -0.08 RUD 59 x BGU 102 1.12* -0.25 0.43 -9.46** -9.57** -9.52** -0.15 -0.09 -0.12* RUD 59 x BGU 54 2.32** 1.60** 1.96** -11.60** -14.67** -13.14** -0.19* -0.29** -0.24** BGU 102 x BGU 54 -1.89** 0.32 -0.79* 1.65 1.68 1.67 0.22* 0.23** 0.23** Sii 0.39 0.39 0.28 2.80 2.81 1.98 0.08 0.07 0.05 Sij 0.44 0.44 0.31 3.13 3.13 2.21 0.09 0.08 0.06 Sij-Sik 0.65 0.64 0.46 4.60 4.61 3.25 0.13 0.11 0.09 Sij-Skl 0.62 0.61 0.43 4.38 4.39 3.10 0.12 0.11 0.08

REFERANCES Anonymous, 2000. The Hindu survey of Indian

Agriculture. The Hindu, National Press, Chennai.

Anonymous. 2001. India, publications Division Ministry of Information and Broadcasting, New Delhi.

Appleby, C.A. and F.J. Bergersen. 1980. In : Methods for evaluating biological nitrogen fixation (Ed. Bergersen, F.J.). John Wiley & Sons, New York, 315.

Griffing, B. 1956. Concept of general and specific combining ability in relation to diallel crossing system. Aust. J. Biol. Sic., 9:463-493.

Hageman, R.H. and A.J. Read. 1980. In : Methods in Enzymology. Vol. 69 part C (Ed. Anthony Sanpietra) Academic Press, New York, pp. 270.

Hedge, J.E. and B.T. Hofreiter. 1962. In : Carbohydrate chemistry 17 (Eds.) Whistler, R.L.and B.C. Miller, J.N. Academic Press, New York.

Hyaman, B.I. 1957. Interaction, heterosis and diallel crosses. Genetics, 42:336-353.

Jinks, J.L. 1954. The analysis of continuous variation in a diallel cross of Nicotina rustica. Genetics, 39:567-788.

Snell, F.D. and C.T. Snell. 1939. Calorimetric methods of analysis 3rd Ed. 11 pp. 802-804, D. Van. Nostrand Co. Inc. New York.

Sprague, G.F. and L.A. Tatum. 1942. General versus specific combining ability in single crosses of corn. J. Amer. Soc. Agron. 34:923-932.

Swaminathan, M.S. 1981. Improving pulses production and productivity challenges ahead. Pulse Crops, News Letter, 1(2):2-5.


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STUDIES OF CHARACTER ASSOCIATION IN WHEAT (triticum species) M. R. Dhaker and A.S.Shekhawat

Krishi Vigyan Kendra, Dausa (Rajasthan) Received: 29.01.17 Accepted: 18.04.17

ABSTRACT The experiment was carriedout during Rabi 2003-04 at the Agricultural Research Farm R B S college Bichpuri (Agra). The experiment revealed that grain yield was significant positively correlated with number of effective tillers (0.626) followed by length of spike ,100 seed weight ,number of seeds per spike and significant negative correlated with days to maturity (0.610) followed by plant height ,number of spike-lets per spike at phenotypic level and also positive significant correlated with number of effective tillers (0.628) followed by length of spike ,number of seeds per spike ,100 seed weight and significant negative correlated with plant height followed by days to maturity,number of spike-lets per spike at genetic level. Being a complex character grain yield per plant is depending upon a number of yield contributing factors. Therefore the genetic makeup can be better known through components. The correlation coefficient gives an idea about the various associations existing between yield and yield components. Key words:-Wheat, Genotypic and Phenotypic Correlation.

The wheat is an important cereal crop in the world. This cereal is the axiom comes true that Necessity is the mother of invention in the way that this cereal converted the ancient food gathers and hunters in to the modern,well organized and civilized human being in the course of evaluation . It is the leading grain crop of the temperate climates of the world .Global demand of wheat is going of approximately 2% per year,twice the current rate of grain in genetic yield potential. It is grown on 8307 thousand hectors with total production of 21.708 million tons and an average yield of 2.615 tons per hector (Anon, 2001). Center of origin of wheat is Asia Minor. It is clearly known south West Asiaaround8000 years ago the people of Asia Minor found a different way of living in course of civilization through the cultivation of genera Triticum and Hordium. The cultivation of wheat in India started around 5000years ago . The success of wheat varieties up to the considerable extent was due to incorporation of the Norin-10 dwarfing gens (rht, rht-1) in wheat from Japan and

Mexico. Dr. N.E. BorlaugaNoble laureates introduced the Mexican wheat in India which semi dwarf in nature consequentlyrightly responsible to fertilizers and irrigations. Wheat production can enhanced through the development of new cultivars having genetic base and better performance -under various agro-climatic conditions. Researchers developed technique to analyze genotypesfor all possible crosses. Genetic analysis of some economic characters showed different pattern to inheritance. The partial dominance with additive gen effect was important for plant height. The number of tillers per plant was conditioned by partial dominant type of gen action this is far below than that of most of the countries of the world like (Germany, 7.9 tons\ha),France (6.6tons\ha)and Egypt (6.4 tons/ha). .Wheat provided large fraction of the dietary protein and total food supply. It is grown all over the world in varied agro-climatic conditions. In this crop the progress has been possible with the development of improved varieties due to strong research


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back up, semi dwarf gene,photo insensitive, disease resistant and fertilizers responsive wheat varieties.Information on the nature and magnitude of variation and heritability in population owing to genetic and non genetic factors is one of the prerequisites in successful breeding programme. This aids to plant breeder in selecting parents likely to produce desirable segregation. Economic yield in wheat is a polygenic trait and also influenced by the number of environmental factor including temperature at emergence, vegetativestage, grain filling period and grain formation. Sowing time is the most important in this respect.

MATERIAL AND METHODS The experiment was conducted at Research Farm of R.B.S. college of Bichpuri Agra during Rabi 2003-04. The material was planted on 11December 2003. Twenty five genotypes were planted in simple randomized block design having five rows of each plot, 4meter length and 25 10cm spacing. All the recommended package of practices was followed as and when required. Observations were recorded both on plot basis and single plant basis. Observations on plot basis were for the days to maturity and yield, for single plant basis five competitive plants were randomly selected from each plot. Average of these plants in respect to different plant character was taken for statistical analysis. The correlation coefficient represented in terms of (r) were used to measure the mutual association between two attributes and this was compared in terms of genotypic and phenotypic correlation coefficient based on variances and co variances by method given by Searle(1).The significance of correlation coefficient (r) were tested with t value at (n-2) degree of freedom.

RESULTS AND DISCUSSION Correlation coefficient at both phenotypic and genotypic level in all the possible combination of nine characters was computed and ispresented in Table no. 1 and 2. Genotypic correlation: - Days to maturity showed positive and significant correlation with number of spikelets per spike (0.540) and non-significant positive with number of seeds per spike (0.321), plant height (0.149), while negative significant correlation with number effective tillers (-0.424), length of spike (-0.462) and negative non-significant correlation with 100 seeds weight (-0.175), grain yield per plant (-0.145) and grain yield per plot (-0.288). Number of effective tillers was positive significant correlated with grain yield per plant (0.682)and positive non-significant with length of spike lets(0.290), number of seeds per spike(0.135) and grain yield per plot(0.157) similar results were found by Thakuret. al(1998) while negative non-significant correlation with number of spike lets per spike(-0.234) and 100 seeds weight (-0.037) at genotypic level. Length of spike lets showed positive non significant correlated with number of spike lets per spike (0.198), number of seeds per spike (0.107), grain yield per plant (0.290) and grain yield per plot (0.253) while significant negative correlated with 100 seeds weight (-0.0393) at genetic level. Similar were finding Sindhu et al. (1976). Number of spike lets per spike showed positive significant correlation with number of seeds per spike (0.628) while negative significant correlation with 100 seed weight (-0.651), grain yield per plot (-0.407) and negative non-significant correlated with grain yield per plant (0.021) at genetic level. Number of seeds per spike showed positive non-


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significant correlation with grain yield per plant (0.185) while negative significant correlation with was recorded with 100 seeds weight (-0.471) negative non-significant correlation with grain yield per plot (-0.260) at genetic level. 100 seeds weight was positive non-significant correlated with grain yield per plant (0.175) while negative non-significant was with grain yield per plot (-0.059). Grain yield per plant showed negative non-significant with grain yield per plot (-0.082) at genetic level. Similar were finding Dawari and Luthra(1991), Kumar and Hunsal (1998). Phenotypic correlation: - Days to maturity showed positive significant correlation with number of spike lets per spike (0.427) and positive non-significant with (0.129), number of seeds per spike (0.220) while non-significant negative correlation with number of effective tillers (-0.327), length of spike (-0.336), 100 seeds weight (-0.197), grain yield per plot (-0.238) and significant negative correlation with grain yield per plant (-0.610) at phenotypic level. Plant height showed positive non-significant with 100 seed weight (0.283) while it showed negative non-significant correlation with number of effective tillers (-0.287), number of spike lets per spike (0.328), number of seeds per spike (0.358), grain yield per plot (-0.049) and significant negative correlation with length of spike (-0.408) grain yield per plant (-0.390) and at phenotypic level. Similar were found Sindhu et al (1976). Plant height was non-significant positive correlated with 100 seeds weight (0.283) while significant negative correlation with length of spike (-0.408), number of spike lets per spike (-0.328) and grain yield per plant (-0.390) and non-significant correlated with number of seeds per spike (-0.358) followed by number of effective tillers (-0.227) and grain yield per plot (-

0.049) similar were finding of Dawari and Luthara (1991) .Number of effective tillers showed significant positive correlation with grain yield per plant (0.626). Similar were found Thakur et. al (1998) and positive non-significant with length of spike (0.227), number of seeds per spike (0.127) and grain yield per plot (0.141) while non-significant negative correlation with number of spike lets per spike (-0.213) and 100 seeds weight (-0.043) at phenotypic level. Length of spike showed positive non-significant correlation with number of spike lets per spike (0.200), similar were found Bahadursingh and Lodhi (1993), number of seeds per spike (0.119), grain yield per plant (0.261), and grain yield per plot (0.231) while negative non-significant correlation with 100 seed weight (-0.370) at phenotypic level similar finding Sindhu et. al(1976). Number of spike lets per spike showed significant positive correlation with number of seeds per spike (0.540) and significant negative correlation with 100 seeds weight was recorded (-0.564),non-significant negative with grain yield per plant (-0.100)and grain yield per plot (-0.307) at phenotypic level. Number of seeds per spike showed positive non-significant correlation with grain yield per plant (0.164) similar findings Sindhu et. al(1976) while negative significant phenotypic correlation was recorded 100 seed weight (-0.455) and negative non-significant correlation with grain yield per plot (-0.226)at phenotypic level similar were found Annadamet. al(1978).100 seed weight showed positive non-significant phenotypic correlation with grain yield per plant (0.170) while it showed negative non-significant correlation with grain yield per plot (-0.048). Grain yield per plant showed negative correlation with grain yield per plot (-0.064) at phenotypic level. Investigation reveal that for


21

improvement in grain yield per plant selection for number of effective tillers,

number of spike lets per spike and 100 seeds weight be made.

Table.1: - Genotypic correlation coefficient among yield and its components under study. Characters Days to

maturity Plant

height(cm) No. of

effective tillers

Length of spike

No. of spike

lets per spike

Np. of seeds per

spike

100 seeds weight

Grain yield per

plant (gm)

Grain yield per plot(kg)

Days to maturity

- 0.149 -0.424 -0.462 0.540 0.321 -0.275 -0.145 -0.288

Plant height(cm)

- - -0.243 -0.432 -0.389 -0.372 0.293 -0.424 -0.064

No. of effective tillers

- - - 0.290 -0.234 0.135 -0.037 0.628 0.157

Length of spike

- - - - 0.198 0.107 -0.393 0.290 0.253

No. of spikelets per spike

- - - - - 0.628 -0.651 -0.021 -0.407

No. of seeds per spike

- - - - - - -0.471 0.185 -0.260

100 seeds weight

- - - - - - - 0.175 -0.059

Grain yield per plant(gm)

- - - - - - - - -0.082


- - - - - - - - -

Table 2: - Phenotypic correlation coefficient among yield and its components under study in wheat. Characters Days to

maturity Plant height(cm)


Length of spike

No. of spike lets per spike


100 seeds weight

Grain yield per plant (gm)


Days to maturity

- 0.129 -0.327 -0.336 0.427 0.220 -0.197 -0.610 -0.238

Plant height(cm)

- - -0.227 -0.408 -0.328 -0.358 0.283 -0.390 -0.049


- - - 0.227 -0.213 0.124 -0.043 0.626 0.141

Length of spike

- - - - 0.200 0.119 -0.370 0.261 0.231

No. of spike lets per spike

- - - - - 0.540 -0.564 -0.100 0.307


- - - - - - -0.455 0.164 -0.226

100 seeds weight

- - - - - - - 0.170 -0.048

Grain yield per plant (gm)

- - - - - - - - -0.064


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- - - - - - - - -

REFERENCES Anand, S.C.; Aulakh, H.S. and Sharma, S.K.

1978. Association among yield components in dwarf wheat. Indian J. of Agric. Sci., 42(10): 935-938

Bahadur Singh and Lodhi 1993. Path coefficient analysis in wheat crop. Crop research. 6: 410-415.

Dawari, N.H. and Luthra, O.P. 1991. Character association studies under high and low environment in wheat. Indian J. Agric. Res. 25(2): 68-72

Kumar B.N.A.; Hunshal-Cs. 1998. Correlation and path coefficient analysis in durum wheat (Triticum durum Deist.) under different planting dates. Crop research.Hisar, 16:3, 385-316.

Searley, S.R. 1961 .Phenotypic, genotypic and environmental correlation biometrics, 17:474-480.

Sindhu G.S., Gill, K.S. and Ghai, B.S. 1976. Correlation and path coefficient analysis in wheat (Triticum aestivum. L) Punjab Agric. Univ. J. Res.131: 215-213

Singh, S.P.; Pianchi A.A and Narsinghani, V.G. 1982. Characters correlation and selectionindices inF2 population of wheat. Indian J. Agric. Sci. 52 (7): 424-429

Snedecor, G.W. and Coechron, W.G. 1967.Statistical method.Oxford and IBH, pp.381-480.

Thakur, S.K., Pandey, R.L., Kandelkar, V.S. Kandelkar 1998. Genetic association and variability for grain yield and other quantitative characters in F2 population of wheat crosses. Advance in plant science, 12 (1): 237-239.


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EFFECT ON SOIL MOISTURE DEPLETION PATTERN IN Jatropha curcas BASED INTERCROPPING SYSTEM IN SEMI ARID REGION OF HARYANA

M.K.Singh, Bimlendra Kumari, K.S.Ahlawat, Sandeep Arya and K.K.Bhardwaj Department of Forestry, CCS Haryana Agricultural University, Hisar-125004

Email: [email protected] Received: 17.02.17 Accepted: 29.09.17

ABSTRACT In order to find out the depletion of moisture content in Jatropha based intercropping system where rabi crops i.e. Mustard cv. RH-30, Taramira cv T-27, Chickpea cv.HC-5 and Barley cv. BH-393 were sown in 4.5 x 7.5 m size plots at Forestry farm of CCS HAU, Hisar. Moisture content was studied at the time of sowing, before first and second irrigation at 1 m and 2.5 m distance from Jatropha line planted at 5 x 3 m spacing. Vertical moisture depletion at 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70- 80 and 80-90cm depths was also recorded in Jatropha based intercropping system and in control. The result reveled that depletion of moisture content was higher at 1 m distance as compared to 2.5 m distance due to severe competition of test crops with Jatropha plantation. Maximum moisture depletion pattern was recorded upto 0-50cm depth in both Jatropha based intercropping system and in control. However, higher moisture depletion was recorded in Jatropha based intercropping system than control. Grain yield of test crops were significantly reduced in Jatropha based intercropping system as compared to control during both the years of study.

Key words: Moisture depletion, rabi crops and Jatropha curcas. India is self sufficient in petroleum as only about 30% of total demand of petro-fuel is meeting by our natural resources. Hence, two third of our requirement has to imported from other countries. Therefore, there is urgent need for alternate source of energy which is renewable, safe and non-polluting. After rigorous study and research, oil extracted from different plant species was tested for fuel which could emerge as a strong bio-fuel and better the environment at the same time. All these characteristics were found in one species, called, Jatropha. Jatropha curcas L (family Euphorbhiace) is a multipurpose large shrub or small tree. It grows on well drained soil with good aeration and is well adapted to marginal soils with low nutrient content. Its leaves and stems are toxic to animals. So, it is not browsed, but after treatment, the seed or seed cake can be used as an animal feed. Being rich in nitrogen, the seed cake is also an excellent source of

plant nutrients Makkar et al (2001). Its multifarious benefits as a source of green manure, soil ameliorator and improve rural economy by generating huge manpower employment during various stages of its cultivation and downstream processing makes it a potential candidate for large scale plantation on marginal lands Behera et. al (2010). Jatropha oil is closely matched with the values of conventional diesel and can be used in the existing diesel engine without any modification Antony et al (2011), Singh et al (2013) and Maina (2014). Agroforestry is a modern tool to develop sustainable land use and to increase food production by growing woody species (trees, shrubs, bamboos etc) with agricultural crops and / or animals in some form of spatial arrangement or temporal sequence. Because these species co-exist crucial to determine the success of an agroforestry system. A survey of the available information reveals that

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most of the agroforestry species have negative allelopathic effects on food and fodder crops. Allelopathy and important ecological phenomenon play a significant role in diverse ecosystems. Allelochemicals-the chemicals involved in interplant interactions process diversity in terms of nature and structure. The phenomenon has been reported and agricultural systems are known to be allelopathic. In agroforestry system the role of allelopathy is especially important as it may lead to soil sickness and may be a casual factor for declining crop productivity reported by Batish et al (2011). Keeping in view the importance of Jatropha oil, Jatropha plantation is being promoted by different agencies. It could be intercropped with other crops plants; however, meager work has been done in relation to intercropping of food crops with Jatropha. The present study was, therefore, undertaken to find out the effect of Jatropha curcas based intercropping system on crops yield and moisture depletion pattern.

MATERIALS AND METHODS Field experiment was conducted during winter season of 2005-06 and 2006-07 at the Research Farm of Department of Forestry, CCS Haryana Agricultural University, Hisar located at 29010N latitude and 75046E longitude with an elevation of 215.2 m above the mean sea level. The climate of the experimental site is semi-arid with dry hot summer, cold winter and receives 452 mm average annual rainfall. Soil of the experimental field was sandy loam in texture, slightly saline in nature, low in nitrogen, and medium in phosphorus and rich in potassium. The field experiment consisted of Jatropha curcas planted in September, 2003 at 5m x 3m spacing intercropped with Mustard (Brassica juncea) cv. RH-30, Taramira (Eruca

sativa) cv T-27, Chickpea (Cicer arietinum) cv.HC-5 and Barley (Hordeum vulgare) cv. BH-393. The treatments were replicated four times in Randomized Block Design. In between the inter spaces of Jatropha plantation all the test crops were sown in middle of July with spacing of 45 x30cm in both years. The recommended package of practices for the test crops were followed both in control and Jatropha. Yield of test crops were recorded at the time of final harvest and analyzed statistically. Moisture content was studied at the time of sowing, before first and second irrigation at 1 m and 2.5 m distance from Jatropha line planted at 5 x 3 m spacing. Vertical moisture depletion at 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70- 80 and 80-90cm depths was also recorded in Jatropha based intercropping system and in control. Number of branches per plant, number of flower cluster per plant, number of fruit per cluster, number of seeds per kernel and seed yield (q/ha) of Jatropha were recorded. Picking of the mature fruits was done at regular intervals from October to January. The Jatropha plants were cut back in March with the help of saw at above 45-60 cm above the ground due to killing of the above parts by severe frost in the first fortnight of January 2006. All the plants sprouted again in the month of April-May.

RESULTS AND DISCUSSION The results of the present study revealed that Jatropha plantation had adverse effect on yield of Mustard, Taramira, Chickpea and Barley during the both years of experimentation. (Grpah-1). It could be ascribed to more sensitivity of all the test crops to shade and below ground interferences of Jatropha. Sharma (2003) has also reported 94 percent decrease in seed yield of mothbean compared to 71 and 72 percent seed yield


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decrease in mungbean and horsebean, respectively. Under 7 year old Acacia tortilis intercropping system compared to sole cropping. Due to increase in crown size and increased competition of roots for moisture and nutrients, the grain yield of all the test crops were significantly reduced in association with Jatropha over control during both years of experimentation. Singh et al (2015) also reported that grain yield of pearlmillet, Greengram, clusterbean and mothbean was significantly reduced in Jatropha based intercropping system as compared to control. Divya et al (2006) have also reported reduced plant height and grain yield of intercrops i.e. groundnuts, blackgram, cowpea, frenchbean, sunflower and gingelly under Jatropha plantation at different spacings.. Rizvi et al (1999) have found that mimosine inhibited large number of physiological and biochemical parameters in V. Mango and P. Aureus. They found that Mimosine inhibited seedling vigor, food mobilization efficiency, solubilisation of starch, breakdown of proteins and activity of amylase. The reduced amylase activity was at synthetic as well as catalytic level and it was mediated by Gibberellic acid. They further reported that Mimosine altered the hormonal balance of the seedlings leading to an inhibition in their growth. When V. Mango plants were grown in the soil having different amounts of Leucaena leaves, nitrogenise activity of root nodules was inhibited. Jatropha after two years of plantation (2005-06) produced negligible mean seed yield 0.16 q/ha with no variation in seed yield between sole crop and intercrop (graph-2). The poor seed yield was due to severe damage to the fruits on account of frost condition (-3.50C) in the first fortnight of January, 2006. Singh et al (2009) has also reported susceptibility of Jatropha to frost and its ability to sprout

again in spring. The number of fruits/cluster (3.6) was also reduced (9.6 fruits/cluster) with negligible variation between intercropped Jatropha and sole Jatropha. It was due to the fact that the Jatropha plants energy was mainly diverted towards vegetative growth which is evident from nearly fourfold increase in branches/plant after pruning in February, 2005. Only the fruits which set in early flowering (September) could mature before the frost. During the second year (2006-07) number of branches per plant, number of flower cluster per plant, number of fruits per clusters, number of seeds per kernels and seed yield (q/ha) were found more as compared to first year of experimentation similar observation also reported by Singh et al, 2012. Moisture content was higher in the control as compared to intercropping system at all the soil depths and all the stages during summer season. The differences in moisture content in intercropping system compared to control were clearly visible before first and second irrigation. Zonation of soil moisture was determined upto 90 cm depth with the interval of 10 cm. The moisture content increased with increasing depth in both the systems (Table-1, 2, 3) similar observation was also reported by Lehmann et al (1998) that soil water depletion was higher under the tree row than in the alley and higher in alley cropping than in monoculture systems. Water competition between tree and crop was confirmed by the carbohydrate analysis showing lower sugar contents of roots in Agroforestry than in monoculture. The Agroforestry combination used the soil water between the hedgerows more efficiently than the sole cropped trees or crops, as well water uptake of the trees reached deeper and started earlier after the flood irrigation than of the sorghum, whereas the crop could better utilize topsoil water. The


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root system of the alley cropped Acacia and Sorghum exploited a larger soil volume utilizing soil resources more efficiently than the respective monoculture. Moisture content under Jatropha in the upper soil depth (0-50 cm) was about 50 percent compared to control both before first and second irrigation during summer season. At lower depths also moisture content in agroforestry was less than control field. Percent decrease in moisture content under Jatropha over control was higher at 1 m distance than 2.5 m distance from the trees line at all soil depths and at all the data recorded stages during both the summer season. Abuger et al (2011) also reported that there is no significant effect of Jatropha curcas hedgerow distance on growth and yield of maize in the first year. In the second year, significant differences were realized in plant height, diameter, stover weight, grain weight, weight of cob and weight of seed/cob. The closest distance from hedgerow (1 m) gave lowest plant height, diameter and stover weight.

Grain weight, weight of cob, weight of seed/cob was lowest at 1 m. it can be concluded that closer spacing at 2 years would have an effect on growth and yield of maize. Singh et al (2015) also reported that depletion of moisture content was higher at 1m distance as compared to 2.5 m distance due to severe competition of test crops with Jatropha plantation. Maximum moisture depletion pattern was recorded upto 0-50 cm depth in both Jatropha based intercropping system and in control. It may be concluded that the moisture depletion pattern of Jatropha based intercropping system was higher upto 1 m distance as compared to 2.5 m distance due to severe competition of test crops with Jatropha plantation. Maximum moisture depletion pattern was recorded upto 0-50cm depth in both Jatropha based intercropping system and in control. However, higher moisture depletion was recorded in Jatropha based intercropping system than control. Grain yield of test crops were significantly reduced in Jatropha based intercropping system as compared to control.

Graph 1: Effect of Jatropha curcas on grain yield (q/ha)

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