aize ournal...pranjal yadava dr. krishan kumar dr. ashok kumar dr. chikkappa, g.k. dr. s.l. jat dr....

MMMMMAIZEAIZEAIZEAIZEAIZE JJJJJOURNALOURNALOURNALOURNALOURNAL

Volume 6 Number 1 & 2 April & October 2017ISSN 2278-8867

(An International Journal of Maize Research and Related Industries)

(Registered Under Societies Registration Act XXI of 1860)Registration No. : S/ND/725/2015

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An International Journal of Maize Research and Related Industries

MMMMMAIZEAIZEAIZEAIZEAIZE J J J J JOURNALOURNALOURNALOURNALOURNAL

1 Importance of soil health management inminimizing the soil borne diseases in Zea maysVimla Singh · Meena Shekhar · Rakesh Mehra ·Sunaina Bisht · Robin Gogoi · Arvind Kumar

9 Identification of heat-stress tolerantrecombinant inbred lines in maize (Zea mays L.)Ishwar Singh · Chikkappa G. Karjagi · Anand P. Atkare· P.K. Shukla · Avni · Pranjal Yadava

22 Price forecasting of maize in major statesAshwini Darekar · A. Amarender Reddy

27 Morphological and molecular diversity inspecialty corn cultivarsD. Chouhan · D. Singh · S. R. Maloo · D. Jain

35 Character association analysis of yield, kernelcomponent and qualitative traits in Maize (Zeamays L.)Nilesh Patel · J. M. Patel · J. A. Patel · L. D. Parmar ·D. M. Thakor

40 Performance of single cross hybrid maize atvarying levels of nitrogen and phosphorus underrainfed conditions of Middle Gujarat PlainsK. H. Patel · P. K. Parmar · M. B. Patel · S. K. Singh· D. M. Rathod · B. N. Thakker

47 Productivity and profitability influenced by plantgeometry and integrated nutrient managementin rainfed sweet corn (Zea mays Saccharata) –horse gram (Macrotyloma uniflorum L.) croppingsequenceA. K. Sinha

52 Selection of high density responsive inbred linesfor enhancing maize productivityGanapati Mukri · R. N. Gadag · ThirunavukkarsauNepolean · Jayant S. Bhat · S. L. Jat

56 Author index

57 Subject indexRESEARCH PAPERS

REVIEW

NAAS RATING: 3.27

Volume 6, Number (1&2), April & October 2017

All previous and current issues of Maize Journal andinstructions to authors are available at:https://mtaisociety.weebly.com

Maize Journal (April & October 2017) 6(1&2): 1-8

REVIEW

Meena Shekhar: [email protected]

1ICAR-Indian Institute of Maize Research, Pusa Campus, NewDelhi-110012, India

2CCS Haryana Agricultural University, Regional Research Station,Karnal-132001, Haryana, India

3Division of Plant Pathology, I.A.R.I., New Delhi-110012, India

Received: 21 April 2017/ Accepted: 18 July 2017© Maize Technologists Association of India 2017

Importance of soil health management in minimizing the soil borne diseasesin Zea mays

Vimla Singh1 · Meena Shekhar1 · Rakesh Mehra2 · Sunaina Bisht3 · Robin Gogoi3 · Arvind Kumar1

Abstract: Maize is a versatile crop adapted to wide agroecologies with a promising potential for overwhelmingcapital gains. However, the crop is afflicted by variousbiotic stresses, largely fungal and bacterial. It amounts tohuge monetary losses to farmers and also amplifies thetoxicity in food chain. With the growing awareness to cutdown the use of agrochemicals in agriculture owing toincreased agrochemical poisoning, there is great demandto use the genotypically and agronomically potential strategyto manage biotic stresses with exuded chemicals andbalanced nutrients to influence crop production. Researchon the pathogens and soil amendments in this regards isgaining significance. Further, interactions of the host andpathogenic organisms, depends on the effects of structural,chemical, and physical soil heterogeneity which helps inabsorption of minerals from soil. The present article reviewsthe balanced nutrition within the cropping systems inrelation to the success of biological interventions. Wediscuss here the need to characterize the agronomicinterventions therein to strategize management of bioticstresses. With the advent of new techniques, directvisualization and quantification of edaphic processes in fieldconditions for better genetic and agronomic approachescan be optimized. By integrating interventions related tosoil chemistry and biology with other management

constraints of specific farming systems, promising yieldsin maize can be derived.

Keywords: Soil environment · Soil health interventions ·Disease management · Maize

Introduction

Interaction among plants, soils nutrients and pests orpathogens is a complex phenomenon. In general plants,suffering from nutrient stress are susceptible to pest anddiseases. Although disease resistance is governed bygenetics but the genetic potential is expressed by plant healthwhich has a direct relation with mineral nutrition. Severityof many diseases can be reduced by proper nutrition (Fig.1). Hence, fertilizer recommendations need to be developedto optimize nutrient uptake and provide the crop withadequate nutrients for normal growth and yield. There maybe 3 or 4 components associated with disease cycle viz.,host, environment, pathogen/pest or vector. By interruptingany aspect of disease cycle effective management ofdiseases can be done. Pesticides are toxic to plants andecosystem in overdoses, which is reflected as negativeimpact on population of soil micro organisms, smallnecrotrophs and decomposers which serve to maintain soilhealth.

Maize is attacked by several species of bacteria causingroot, leaf and stalk wilts, leaf spots, and seedling blight.Also, several genera of fungal diseases of maize includeleaf blight, banded leaf and sheath blight, brown spot, postflowering stalk rot, ear rot, brown leaf spot, downymildews, rust, false smut, cob rots etc. Most of the bacterialand fungal diseases are soil borne.

Nutrients play an important role in growth anddevelopment of plants and microorganisms and areimportant factors in disease control. All the essential

2 Maize Journal (April & October 2017) 6(1&2): 1-8

nutrients can affect disease severity (Huber and Graham,1999). However, a particular nutrient can not only decreasethe severity of a disease but also increase the severity ofthe disease incidence of other diseases or have a completelyopposite effect in a different environment (Graham andWebb, 1991; Huber, 1980). The soil has natural reservoirof macro and micro nutrients which can effectively preventpathogens and pest from attacking crops, if a natural balanceof nutrients is maintained. Soil provides a medium for seedgermination and root growth for absorption of water andmineral nutrients. It stores reserves of nutrients within theorganic matter and mineral components, to be released intousable forms at different rates. Root contains a matrix inwhich transformations of nutrients occur throughbiological, chemical and physical processes (Huber et al.,2005; Powlson et al., 2011; Datnoff et al., 2007). Thepresent review discusses the effect of mineral imbalanceon disease resistance/susceptibility of host plant andinteraction with pathogens. The information presented inthe subsequent sections can be used by maize researchersto devise a comprehensive IDM module in considerationto the soil health.

Host pathogen interactions

Fungi germinate as spores on plant surfaces and invadehost tissues through natural openings like stomata, lenticels

or epidermal cell walls. This invasion is accomplished byrelease of several tissue solubilizing enzymes as well ascompounds to counter the strength and integrity of cells.However, polyphenols and flavonoids are also released byhost to resist the fungal invasion. The production andtransport of these compounds to sites of infection involvesminerals nutrients. In this event, if the host is malnourished,the battle is won by the pathogen. Nitrogen (N) helps inmetabolites translocation, Calcium (Ca++), maintainsintegrity of cell walls and Potassium (K) maintains ionicbalance and turgidity of cell. Spore’s cytoplasm is denser,the walls are thicker, and the metabolic rate is slower thanfound in hyphae. Thus, thin and weak cell walls may leaknutrients within the cell to the apoplast, favouringgermination of fungal spores on leaf and root surfaces.However, mineral nutrient levels directly influence theamount and composition of leakage hence, mineralimbalances lower resistance to fungal diseases providing afavorable environment for pathogens. Boron plays a keyrole in the synthesis of these compounds. Borate-complexing compounds trigger the enhanced formation ofa number of plant defense chemicals at the site of infection(Gupta and Solanki, 2013). Mineral nutrition also affectsthe formation of mechanical barriers in plant tissue, forinstance, the accumulation of silicon (Si) in the cell wallhelps in forming a protective physical barrier to fungalpenetration. Excessively high N levels lower the B and Si

Fig. 1. Healthy Plant Vs Diseased plant(Source: Department of Plant Pathology. The Ohio State University Courtesy: Sarah et al.www.ohioline.osu.edu/factsheet/plpath-gen-2)

3Maize Journal (April & October 2017) 6(1&2): 1-8

content and increase susceptibility to fungal diseases(Zeyen, 2002; Epstein and Bloom, 2003). Othermicronutrients alos play important role in disease resistance,viz., Copper (Cu) is widely used as a fungicide. The actionof Cu as a fungicide relies on direct application to the plantsurface and the infecting fungi. Cu deficiency leads toimpaired defense compound production, accumulation ofsoluble carbohydrates, and reduced lignifications,contributing to lower disease resistance (Weckx andClijsters, 1996). Plants which have low Cu level in tissue(based on nutrient standards, Evans et al., 2006) are moresusceptible to various diseases. Therefore, the diseasesuppression capabilities of Cu can be said to occur over awide range of concentration, and to function both as adirect inhibitor to various diseases as well as enabling plantsto better defend themselves from disease. An experimentwith tomatoes revealed that Zn++ essentially negated thefungicidal effects of Cu treatments. Zn is much less toxicto many pathogens compared to Cu. When uptake of Cuwas suppressed by excess Zn, the stronger disease controlbenefits of Cu were also reduced. Thus, there was a netincrease of disease in the plants

Bacterial diseases primarily leaf top and stalk rots arealso major biotic constraints in maize. Bacteria mainly enterthrough natural openings and invade the intercellular spaces.Thus, the defense mechanism requires strengthening ofinternal tissues affected by antibacterial compounds inresponse to infection to counter the proteolytic enzymes.

Bacterial stalk rot spreads by producing slime in thevascular tissues (xylem and phloem) which disintegratethe cell causing wilting. Certain plant nutrients support inblocking or reducing the bacterial slime. Mineral nutritionaffects susceptibility to bacterial infections in much thesame way as it affects fungal infections. Potassium andCa play key roles in forming an effective barrier toinfections. When K, Ca, and, often, N levels are deficient,plants are more susceptible to bacterial attacks. A frequentsymptom of B deficiency is the development of “corky”tissue along leaf veins and stems as a result of the irregularcell growth that occurs when B is deficient. These irregularcells are more loosely bound than normal cells, essentiallyproducing wounds through which bacteria can enter.Adequate increase in N levels increase minimizes bacterialdiseases; however, excessive N can makes the plantsusceptible.

Disease relationships to K content are more consistent.K reduces bacterial and fungal diseases up to 70% andinsects and up to mites 60%. Potassium (K) is an importantnutrient as it is involved in functioning of stomata and

metabolic pathways as well. According to Levitt (1974)theory, the change in turgor pressure of the guard cells isalso responsible to open and close the stomata and resultsin absorption and loss of K+ ions by guard cells. In K+

deficient condition stomata remain open that result intomore loss of water due to transpiration which is apredisposing condition for PFSR. Again in potassiumdeficient condition the rate of photosynthesis is lower andmay result in pith senescence. Hence, maintaining asufficient supply of potassium to prevent lodging needsmore attention in maize hybrids. The response tophosphorus varies with the season, cultivar and thepathogen while at higher level of phosphorus severity ofstalk rot does not decrease. Lower dose of Potassiumcoupled with higher dose of nitrogen favors stalk rotincidence. Also, limited access to nutrients is critical tophotosynthesis, can cause carbohydrate stress that favorsstalk rot. Calcium affects the incidence of bacterial diseasein a variety of ways. Ca supports in stability of cell walls.Calcium deficiencies trigger the accumulation of sugarsand amino acids in the apoplast, which lowers diseaseresistance. Ca rich plants are tolerant to bacterial diseasesand physiological disorders that cause post harvest rotting.Molybdenum deficiency lowers disease resistance byaffecting the production of nitrate reductase.

Plant nutrition affects both fungi and bacteria, and hencethe severity of diseases they spread. It is reported that thenutrient status of a plant can affect the aphid population onplants e.g. certain aphids tend to settle on yellow reflectingsurfaces, such as chlorotic leaves caused by nutrientdeficiency. Feeding intensity and reproduction by suckinginsects tend to be higher on plants with higher amino acidcontent (Ohashi and Matsuoka, 1987). This condition istypical of plants suffering from certain nutrient stresses.It is reported that, silicon, application physically inhibitsthe feeding ability of some sucking insects like aphids. Instudies with watercress, it was reported that high levels ofZn effectively controls fungus which is also a vector forthe chlorotic leaf spot virus. Therefore, high Zn fertilizationcan be effective to manage both a fungus and a virus(Wenzel and Mehlhorn, 1995).

Role of Macro and micronutrient in biotic stressmanagement

A healthy soil has all the necessary mineral nutrients toregulate disease resistance or tolerance (Graham and Webb,1991). Resistance depends on the genotype of the twoorganisms, plant age and changes in the environment.


Although plant disease resistance and tolerance aregenetically controlled, they are affected by the environmentand especially by nutrient deficiencies and toxicities.However, enough information is not available for appropriaterole of micronutrient management practices in sustainableagriculture that can reduce yield losses in maize due todiseases and improper doses of micronutrient as well. Thereare many agronomic factors that affect the severity of plantdisease viz., planting time, crop rotation, mulching andmineral nutrients, organic amendments (manures and greenmanures), liming for pH adjustment, tillage, seedbedpreparation and irrigation (Huber and Graham, 1999).Certain pathogens can immobilize nutrients in therhizosphere, or in infected tissues such as roots, whileothers interfere with translocation or utilization efficiency,causing nutrient deficiency or hyper accumulation andnutrient toxicity (Huber and Graham, 1999). Also, someorganisms can utilize a significant amount of nutrients fortheir growth, causing a reduction in the availability ofnutrients for the plant which therefore increases itssusceptibility to disease (Fig. 2).

Mineral fertilization reduces disease severity forinstance, when N was applied to the soil the incidence of

post flowering stalk rot is reduced. Also, P reduced Pythiumroot rot and stalk rot infection also (Huber, 1980). However,in case of foliar diseases, e.g. rust and mildews, Napplication caused an increase in the incidence of thediseases. Since the interaction of nutrients and diseasepathogens is complex. The effect of each nutrient oncertain diseases, its role in metabolism and also the possibleeffects of deficiency on the plant are discussed in Table 1below.

Mineral nutrition as affected by soil pH

Most of the mineral nutrients are available to crops withinthe pH range of 6.5-7.5 (Fig. 3). It is recommended toneutralize the pH before plantation. At neutral pH the existingnutrients are unlocked and are readily available to plants.At pH >7.5, phosphate ions react with Ca and Mg to formless soluble compounds while at pH <5.0, phosphate ionsreact with Fe and Al to form less soluble compounds. Alsomost of the plant nutrients become less available if pH goesabove 7.5 therefore; to neutralize pH of acidic soil (pH4.0-6.0) lime/dolomite is used and for alkaline soils pH above8.3, gypsum/magnesium sulphate is used for neutralization.

Soil borne Fungal and Bacterial Diseases

Many soil borne pathogens infect the roots, whichimpairs its ability to absorb water and nutrients (Huber andGraham, 1999). The effects are serious when the levels of

Fig. 2. Visual symptoms due to mineral deficiencies(Image Courteys: University of Arizona Cooperative Extension viaP interest)

Fig. 3. Source: Balance chart of micro and macronutrient to fightagainst pests and diseases(Image Courteys: Mid Klamath Watershed Council)


Table 1. Role of macro and micro nutrients in plant nutrition and disease management

S. No. Nutrients Role in plant metabolism Deficiency symptoms Disease susceptibility/resistance

1. Nitrogen (N) It is vital and major component High rates along with low Zn, Stalk rot in maize (Diplodia spp.,of chlorophyll and affects Ca, or K favour disease. Fusarium spp.), Root rot in maizeoverall growth of plants by Causes succulent growth and (Phythium spp.) Cyst nematodephotosynthesis. It is the basic thinner cell walls, Increased (Heterodera spp.) are reduced by (NH

4

building block of proteins and plant density, thus more form), increased by (NO3 form) nitrogen.

a component of nucleic acids humid air around the plantscauses delay in crop maturity,higher amino acid contentin plant.

Deficiency appears as stunted Northern blight in corn (Helminthos-growth, extremely pale colour, porium spp.) and Fusarium wilt (Fusariumand upright leaves with light spp.) root rot (Rhizoctonia solani) aregreen/yellow colour. Appears increased by (NH

4 form), decreased by

burnt in extreme deficiency. (NO3 form) nitrogen.

2. Phosphorus Stimulates root growth, stalk and Purplish or reddish colour on Corn smut, downy mildews, Pythium rootstem strength, flowering, crop lower leaves and stem, Plants rots and leaf and sheath blight diseasesmaturity, crop quality, resistance short and dark green, extreme caused by Rhizoctonia solaniagainst diseases. It affects overall deficiency develops brown orlife cycle of plants black colour sometime bronze

colour under the leaf

3. Potassium (K) K+ acts as mobile regulator of Excess K inhibits the uptake Plants tolerate nematode infections withenzyme activity. Involved in of Mg and Ca. less yield loss. Reduces bacterial andessentially all cellular functions that K+ deficiency favours accum- fungal diseases, nematodes and termiteinfluence disease severity. It alters ulation of nitrogen compo- incidence Reduces foliar and stalk rotthe compatibility relationship of unds, sugars, etc., providing diseases. Gives stalk strength to the stalkthe host-parasite environment conditions for parasite which is responsible to reduce stalk rotwithin the plant. Shortage of K development. in maize.reduces the amount of the plants Tissue hardening, stomatal Effectively manages, N. Leaf blightnatural antifungal compounds at opening patterns, etc. further (Exherohilum turcicum); Root rotthe site of infection. Potassium enhance infestation intensity. (Gibberella saubinetti), Stalk rotplays a central role in the Deficiency appears as small (Fusarium moniliforme, Gibberella zeae,development of thick cuticles, spots on tips, edges of pale Diplodia zeae), Stem rot (Fusariuma physical barrier to infection or leaves, Spots turn rusty and culmorum), Stewart’s wilt (Erwiniapenetration by sucking insects. fold at tips. stewartii).It supports rapid healing of Infection causes increased production ofwounds and the accumulation of fungus inhibiting phenolic compoundscompounds toxic to the fungus and flavonoids, both at the site ofaround the wounds and insects infection and in other parts of the plant.

The production and transport of thesecompounds is controlled in large part bythe general nutrition of the plant.

4. Calcium (Ca) It is structural component of cell As the Ca content of the Increased leaf spot diseases, bacterialwalls and other plant membranes, tissue increases, the K and stalk rot and top rot diseases.plays role in improving the storage Mg content is decreased, thislife of fleshy fruit by inhibits cell reduces resistance to infection.wall degrading enzymes activity Low Ca permits increasedof pathogens. transport of sugars from with

in the cell to the intercellularspaces in the plant tissuewhich tends favour growthof pathogens.

Ca shortage results in improper Deficiency appears as plantformation or function of the plant appearing dark green, withcell walls, hence, increased tender and pale coloureddisease infection or spread within leaves, drying starts from tipsthe plants. and eventually leaves die.


Table 1. (contd...........) Role of macro and micro nutrients in plant nutrition and disease management


4. Boron (B) It forms carbohydrate-borate Plant cell walls tend to swell Boron deficiency favours yellows incomplexes, which control and split having weakened various crops (Fusarium oxysporum),carbohydrate transport and cell intercellular space which Stem rot (Rhizoctonia solani) andwall protein metabolism, cell provides easy expansion of Charcoal stalk rot in maizemembrane permeability or the infection. (Macrophomina phaseolina).stability, metabolism ofphenolics and a plays a primaryrole in the synthesis of lignin.

It helps in production of disease Deficiency appears as disco-protection compounds and loration of leaf buds whichstructures within plants. break and drop down.

5. Copper (Cu) Copper is an essential nutrient Shortages of key nutrients Decreases the severity of a wide range offor higher plants as well as fungi such as K, Mn, Cu, Zn, and fungal and bacterial diseases, particularlyand bacteria. It is very toxic to B reduce the amount of the foliar diseases e.g. Leaf blight (Helmintho-all plant forms when present at plants natural antifungal sporium spp.) and fungal leaf spothigh levels. The difference in compounds at the site of (Alternaria spp.)tolerance enables growers to use infection.Cu as a disease treatment.

Cu acts to detoxify oxygen Deficiency appears as paleradicals (O= and OH-) and pink coloration between thehydrogen peroxide (H

2O

2), form veins, wilting and drooping

in response to infection thus of leaves.limiting damage to plant cells.

6. Manganese Plays key role in the production Contributes to the suppr- Favours Helminthosporium and(Mn) of phenolic compounds and ession of fungus and bacterial Alternaria leaf spot, various mildews and

lignin formation, two of the major diseases. It catalyses several Pythium root and stalk rot.items in resistance against disease, biochemical reactions.

later detoxification. Deficiency appears as palecoloured leaves, dark greenand articulated veins andveinules.

7. Zinc (Zn) Zn aids both in the production Zinc shortages contribute to Effective against Phytopthora spp.,and detoxification of oxygen the accumulation of unused Fusarium root/stalk rot, leaf spots byradicals and hydrogen peroxide, sugars within the plant and Physoderma spp., Alternaria spp., Postthus limiting damage to plant leakage of sugars onto the flowering stalk rot by Fusarium andcells. It is essential to the surface of the leaf enhancing Verticilium spp., Rhizoctonia stem/sheathintegrity and stability of plant fungal and bacterial invasion. blights, Downey mildew etc.membranes and it is thought to It’s shortage also enhanceshelp prevent “leakage” of feeding intensity and repro-essential elements or compounds duction by sucking insects.

from plant cells. Deficiency appears as pale,narrow and short, dark greenveins. Dark spots on leavesand edges.

8. Iron (Fe) Fe has a role in both the produc- Generally plant pathogens Effective for managing Rusts and Smutstion and detoxification of oxygen have a high requirement for vector borne diseases.radicals and hydrogen peroxide, Fe. However, increased Fethus it limits damage to plant availability or uptake cancells. actually increase disease

severity.

Deficiency appears as pale col-ored leaves with green majormajor veins. No spots on leaves.


Table 1. (contd........) Role of macro and micro nutrients in plant nutrition and disease management


9. Chlorine (Cl) Essential micronutrient with Excessive Cl increases Effective in controlling Bacterial stalk rotsignificant disease control salinity stress causing plant of maize caused by Erwinia chrysanthemibenefits. damage. and other fungal rot caused by Gibberella

zeae, Gobbler Fusarium moniliforme),Northern corn leaf blight (Exserohilumturcicum), downy mildews

10. Silicon (Si) Increases the effectiveness of Deficiency results in poor Effective in controlling Brown spotthe mechanical barrier that defense against insects and Powdery mildew and Rust diseases.plants present to infection pathogen invasion.

Transported to sites of infection Prevents from vector borne viral diseases.in defense processes it transportsof the disease fighting phenoliccompounds to the infection sites.

11. Molybdenum Little known about the effects Effect of Mo deficiency on Reduces stalk rot caused by Fusarium(Mo) that on plant diseases. plant diseases may be Verticillioides and reduces reproduction

indirect, through its role in of Phytophthora spp. Causes prefloweringN metabolism. stalk rot in maize.

Deficiency appears as light Soil applications of Mo minimizesgreen/lemon yellow or orange populations of the nematodeleaves. Spots on whole leaf Rotylenchulus reniformis.except veins. Sticky secretionsfrom under the leaf.

12. Nickel (Ni) Plays a key role in N Deficiency symptoms similar Reduction in rust pustules (@ 30 ppb tometabolism in plants. to Nitrogen deficiency. 3.3 ppm to the roots).

Critical level in plants is reported Reduced the level of rust infection (@ 1).to be between 10 and 100 ppb(parts per billion).

13. Cadmium (Cd) Low concentrations has been Toxic to plants in low Suppresses the effects of mildews infound to stimulate or enhance quantities. maize.

the formation of lignin. Up to 3 ppm depresses plantgrowth.

14. Lithium (Li) Toxic at low levels Toxic at soil levels in excess Significantly suppress the effects ofof 2 to 5 ppm. various mildews.

nutrients are marginal and also form immobile nutrients.Also, stem girdling or acropetal infection can limit rootgrowth and affect nutrient and water uptake. Pathogensalso affect membrane permeability or mobilization towardsinfected sites, which induces nutrient deficiency or toxicity.Fusarium spp. can increase the concentration of P in leaves,but at the same time, decrease the concentration of N, K,Ca and Mg (Huber and Graham, 1999).

A micronutrient-deficient plant usually has poor defenseagainst soil borne diseases. The effect may be direct e.g.,soil-applied manganese (Mn) inhibit the growth of certainfungi, hence, nitrites are toxic to some Fusarium andPhytophthora species because nitritification requires Mn.The use of ammonium-based fertilizers generally makesplants susceptible to some diseases (e.g., Fusarium and

Phytophthora root rots), whereas nitrate-based fertilizersreduce disease incidence. The effects may be due toreduced pH by ammonium fertilizers and increased soil pHby nitrate fertilizers.

Mineral nutrition and disease incidence

Incidence of pests such as insects, mites, and nematodesare a concerns for yields. These pests also carry pathogens.Visual factors such as leaf color are important factors inpest susceptibility. Mechanical barriers (tough fibers, siliconcrystals, lignifications) and chemical/biochemical barriers(attractants, toxins, and repellents) are primary lines ofdefense against pests which is regulated by mineral balance.There is often a correlation between N applications


(stimulation of growth) and pest attack so; younger tissuesof plants are more susceptibility to pest attack. Borondeficiency increases pest susceptibility due to poorsynthesis of flavonoids and phenolic compounds whichform the biochemical defense mechanism.

Conclusion

Utilizing soil for agriculture inevitably leads to changes insoil properties such as nutrient status, pH, organic mattercontent, and physical characteristics. A balanced humanintervention, replenishment of soil water storage and runoffto prevent risk of soil erosion and transfer of sediments tosurface with proper regulation of the movement ofnutrients, pollutants and sediments to surface- or ground-waters can be effective for maintaining soil health. It’sfurther necessary that plant pathologists should identifyand focus on management practices that promote soil health,have positive effects on the management of soil bornediseases.

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Wenzel, A. A., & Mehlhorn, H. (1995). Zinc deficiency enhancesozone toxicity in bush beans (Phaseolus ulgaris L. cv. Saxa).Journal of Experimental Botany, 46: 867-872.

Zeyen, R. J. (2002). Silicon in plant cell defenses against cerealpowdery mildew disease. Abstract of Second Silicon inAgriculture Conference, pp. 15-21.


RESEARCH PAPER

Ishwar Singh: [email protected] Institute of Maize Research, Pusa Campus, New Delhi110012, India2Department of Biological Sciences, SHIATS, Allahabad 211007,Uttar Pradesh, India3Department of Biology, Stanford University, 385 Serra Mall,Stanford, CA 94305, USA

Received: 23 February 2017/ Accepted: 26 May 2017© Maize Technologists Association of India 2017

Identification of heat-stress tolerant recombinant inbred lines in maize (Zeamays L.)

Ishwar Singh1 · Chikkappa G. Karjagi1 · Anand P. Atkare2 · P. K. Shukla2 · Avni1 · Pranjal Yadava1,3

Abstract: An investigation was conducted to understandthe response of morpho-physiological and yield traits ofmaize recombinant inbred lines (RILs) under managed heatstress conditions created under net-house at ICAR-IIMR,New Delhi. A total of 221 F

5:6 RILs were developed by

following single seed descent method (SSD) by advancingF

1 cross made between heat stress tolerant inbred (LM17)

and heat stress sensitive inbred line (HKI 1015-wg8). Twosets of test-crosses were evaluated generated by crossingrandom set of 22 and 28 RILs with inbred tester LM13and LM14 respectively. The observations were recordedon 26 morphological, physiological and yield parametersand 13 parameters showed significant differences indicatingconsiderable variation among RILs. Traits viz., anthesissilking interval, chlorophyll content, grain weight and 100kernel weight showed significant differences. Correlationstudies showed that chlorophyll content was in strongpositive association with grain weight (0.30), leaf breath(0.50), leaf colour (0.59), leaf length (0.54), stem diameter(0.52) and stem vigour (0.53). Similarly, barrennessshowed very high and negative association with grainweight (-0.42). The study helped in identifying few RILswhich can be used for developing heat stress resilienthybrids in future programmes by using RILs as one of theparental lines in either developing hybrids or in developingheat stress synthetics.

Keywords: Climate change · Correlation · Heat · Grain yield

Introduction

There are several biotic and abiotic stresses which restrictthe potential productivity of crop plants. Heat stress, oneof the major abiotic stresses, is a serious threat to cropproduction worldwide (Hall, 2001; Porter and Semenov,2005). If the current trends of heat and water stresses dueto climate change persist until 2050, yield of major staplefood crops of South Asia like rice, wheat and maize willdecrease significantly by 10, 12 and 17 per cent respectively(Wassman et al., 2009; Asian Development Bank Report,2009; Lobell et al., 2008). High temperature has adverseeffect on the growth and development of maize, it reducesmaize grain yield along with quality. Higher temperature(45-48°C) at flowering and grain formation stages is themost alarming factor which affects the crop growth andyields (Ulukan, 2009; Al-Khatib and Paulsen, 1999; Smith,1996). Higher temperature during flowering and grain fillingperiod results less photosynthate conversion to plantcarbohydrates. This loss of carbohydrate causes lessavailability of starch in grain filling of the ear. Hightemperature also reduces the pollen viability and silkreceptivity, resulting in poor seed set and reduced grainyield (Johnson, 2000; Aldrich et al., 1986).

Plant responses to heat stress vary with the degree andduration of temperature; differs with type of genotype(Ahuja et al., 2010). The adverse effects of heat stressaffects ranges of traits like plant height, days to flowering,anthesis silking interval, grain number per ear, ear numberper plant, 100 grain weight, crop duration etc. It also affectsvarious metabolic processes like photosynthesis,respiration, transpiration etc. as a consequence thechlorophyll content, membrane stability, dry matterproduction, dry matter partitioning, gene expression levelof hormones and primary and secondary metabolites getaffected (Rizhsky et al., 2004). The visible symptoms of


heat stress are early leaf senescence, leaf rolling, tasselblast, tassel firing and pollen sterility etc.

The current patterns and future predictions of climatechange demands development of climate resilient maizehybrids and varieties to sustain and enhance the maizeproduction for the future. Development of heat toleranthybrids is a challenge for maize breeders however, effortsare being made in identification and selection of superiorgenotypes with tolerance to drought and heat stress.Further, development of hybrids from elite lines selectedfor heat tolerance, testing under managed heat stresscondition and deployment of tolerant or moderately toleranthybrids in the target environment will help in sustainingthe yield levels in long run under heat stress condition.Keeping the above facts in view, an attempt was made toidentify heat stress tolerant RILs through evaluation of test-crosses developed by crossing RILs with common tester.The objective of the study is to estimate the correlationamong different morphological, physiological and yieldrelated traits and to analyze the yield performance of RILsunder heat stress.

Material and methods

A total of 221 F5:6

RILs, developed by following singleseed descent (SSD) method from the cross [LM 17 (heat

stress tolerant) × HKI 1015-wg8 (heat stress sensitive)],were considered as the genetic material for the study. A setof 22 and 28 RILs were crossed with common inbredtester LM13 and LM14 respectively during rainy season(July-September), 2014 at ICAR-IIMR, New Delhi. TheRILs were chosen randomly, from a pool of 221 RILs andcrosses to a common inbred tester, depending on thesynchrony in flowering.

The test-crosses (Table 1) were evaluated under heatstress conditions created under net-house conditions duringspring (March-June), 2015 at ICAR-IIMR, IARI Campus,New Delhi. The test-crosses were sown on 23rd March,2015 by following randomized block design in threereplications with plot size of one row of 2.5 metre length.The spacing between rows and plants was maintained at60 × 20 cm respectively. The recommended dose offertilizers, irrigation and plant protection were followed.Data on temperature (minimum to maximum) and relativehumidity was recorded after 60 days after sowing.

The observations were recorded on twenty-sixparameters comprising different morphological (13),physiological (3) and yield (10) related traits. Morphologicalparameters included days to 50% tasseling (DT), Days to50% silking (DS), anthesis-silking-interval (ASI), plantheight (PH), ear placement (EP), tassel length (TL), tasselbranches (TB), stem diameter (Std), stem vigour (StV),

Table 1. Information regarding crosses used in the study [Cross = RIL ID X Tester]

S. No. Treatment Cross S. No. Treatment Cross S. No. Treatment Cross

1 2lm13 13313 × LM13 18 54lm13 13494 × LM13 35 27lm14 13423 × LM14

2 5lm13 13319 × LM13 19 59lm13 13507 × LM13 36 28lm14 13425 × LM14

3 12lm13 13361 × LM13 20 63lm13 13528 × LM13 37 29lm14 13427 × LM14

4 14lm13 13371 × LM13 21 66lm13 13537 × LM13 38 37lm14 13443 × LM14

5 16lm13 13376 × LM13 22 67lm13 13539 × LM13 39 40lm14 13448 × LM14

6 17lm13 13379 × LM13 23 1lm14 13312 × LM14 40 42lm14 13460 × LM14

7 19lm13 13391 × LM13 24 4lm14 13318 × LM14 41 43lm14 13462 × LM14

8 21lm13 13396 × LM13 25 7lm14 13323 × LM14 42 46lm14 13470 × LM14

9 26lm13 13422 × LM13 26 8lm14 13338 × LM14 43 50lm14 13480 × LM14

10 30lm13 13430 × LM13 27 9lm14 13345 × LM14 44 51lm14 13486 × LM14

11 34lm13 13438 × LM13 28 10lm14 13346 × LM14 45 56lm14 13497 × LM14

12 35lm13 13440 × LM13 29 11lm14 13357 × LM14 46 58lm14 13505 × LM14

13 36lm13 13442 × LM13 30 15lm14 13372 × LM14 47 61lm14 13514 × LM14

14 45lm13 13468 × LM13 31 20lm14 13393 × LM14 48 64lm14 13534 × LM14

15 47lm13 13471 × LM13 32 21lm14 13396 × LM14 49 65lm14 13535 × LM14

16 49lm13 13479 × LM13 33 24lm14 13409 × LM14 50 68lm14 13542 × LM14

17 52lm13 13492 × LM13 34 25lm14 13413 × LM14


number of leaf per plant (NoL), leaf length (LL), leaf breath(LB) and leaf area (LA). Physiological parameters comprisedleaf colour (LC), chlorophyll content (CC) and canopytemperature (CT) in °C, which were recorded with thehelp of leaf colour chart SPAD meter and InfraredThermometer (IR) respectively. The yield attributingparameters comprised ear length (EL), ear breath (EB),kernel row (KR), kernel per row (KpR), shelling percentage(SP), 100 kernel weight (100KW), grain weight (GrW),barrenness (Barr), dry leaf weight (DrLWt) and green leafweight (GrLWt). In each plot, observations were recordedon three random plants for all parameters except days to50% tasseling, days to 50% silking and grain yield as theywere recorded on plot basis. The analysis of variance forall the traits were calculated and the p-value, LSD and SE(d)were recorded. Different treatments were compared byusing LSD at 0.05 and 0.01 significance level. ThePearson’s correlation coefficients (r) (p=0.05) between allthe twenty-six parameters were estimated. The analysiswas done with SAS version 9.3 (SAS, Inc., Cary NC).

Result and discussion

Heat stress tolerance is a complex trait, determined byexpression of group of genes together. Since many genesare involved, the mere presence of one or two genes maynot really give stress tolerance to an inbred line. However,in order to pyramid a group of traits, which are responsible

for heat stress tolerance, an indirect evaluation of inbredlines via test-cross evaluation is being widely practicedespecially in maize against abiotic stress conditions likedrought and heat (Chen et al., 2012). Several morpho-physiological traits are responsible for the completeexpression of yield related traits (Commuri and Jones, 2001;Badu-Apraku et al., 1983).

Analysis of variance for test-crosses generated bycrossing RILs with common tester LM13 revealed thatDT, DS, PH, EP, Std, TB, TL, LA, NoL amongmorphological traits (Table 2a); LC, CC amongphysiological traits (Table 2b); EL, EB, SP, GrW and GrLWtamong yield related traits (Table 2c), showed significantdifferences among the test crosses. Significant differencesamong the test crosses indicated underlying variation amongRILs due to segregation and simultaneous fixation for thetraits studied. However, the remaining traits viz., StV, ASI,LL, LB (Table 2a); CTD (Table 2b); DrLWt, Barr, KR,KpR and 100KW (Table 2c), showed no significantdifferences. Similarly, the results of ANOVA for the testcrosses generated by crossing with common tester LM14revealed that the morphological traits viz., DT, DS, ASI,PH, EP, TL, TB, Std, StV, NoL and LL (Table 3a) hadsignificant differences along with some physiological traitsviz., LC, CC (Table 3b) and yield related traits viz., EL,KpR, 100KW, Barr and GrW (Table 3c). However, nosignificant differences were observed for traits like CTD,LB, LA, EB, KR, GrLWt, DrLWt & SP (Table 3a, 3b & 3c).

Table 2a. The mean sum of squares of morphological traits of test-crosses generated with common inbred tester LM13

Source DF DT DS ASI PH EP TL TB StD StV NoL LL LB LA

Rep 2 86.37** 208.09** 14.32 151.14 123.86 85.81* 14.38 3.15** 2.97** 1.70 380.79* 8.48** 28.79

Trt 21 522.28** 882.82** 5.61 1824.55** 491.18** 41.75* 45.87** 0.94** 0.67 4.53** 81.17 1.27 161.37*

Error 42 222.46 547.91 4.50 255.50 128.63 21.39 10.68 0.37 0.45 1.65 81.34 0.82 76.60

SS — 7.90 21.81 26.48 5200.00 2861.90 160.89 173.90 22.14 3.24 49.24 225.81 7.20 274.00

F valu — 0.39 0.82 0.87 5.80 3.15 2.27 2.03 4.06 1.66 3.17 0.72 2.85 0.48

Pr> f — 0.87 0.58 0.54 0.00 0.04 0.11 0.14 0.02 0.21 0.04 0.64 0.06 0.81

Table 2b. The mean sum of squares of physiological traits of test-crosses generated with common inbred tester LM13

Source DF LC CC CTD

Rep 2 0.74 109.63** 16.20**

Trt 21 1.05** 72.44** 0.94

Error 42 0.39 19.98 2.04

SS — 3.24 40.40 4.00

F valu — 2.34 1.08 0.36

Pr> f — 0.10 0.42 0.89


Thirteen out of twenty-six traits viz., DT, DS, PH, EP,Std, TB, TL, NoL, LC, CC, GrlWt, EL, and GRW showedsignificant differences in both set of test crosses. Whereasthere were only three traits viz., CTD, LB and KR out ofthe twenty-six traits which showed no significantdifferences in both set of test-crosses. This indicated that50% of the traits chosen in the study showed significantdifferences among test-crosses under heat stress and theymay be either directly or indirectly associated for heat stresstolerance. Similar findings were observed previously for

such traits by Richards (2006), Prasad et al., (2006) andSteven et al., (2002).

Mean performance of test-crosses

Morphological traits

The significant variation in mean performance of the test-crosses, obtained by crossing RILs with common inbredtester LM13, were observed (Table 4a) for morphological

Table 2c. The mean sum of squares of yield related traits of test-crosses generated with common inbred tester LM13

Source DF EL EB KR KpR SP 100KW GrW Barr DrLWt GrLWt

Rep 2 3.11 0.01 3.00 6.30 188.85** 13.44 3674326.09* 3451.32 3.69* 13.77**

Trt 21 4.99* 0.10 2.46 35.59 125.64** 37.83 3371713.74** 2627.49 16.16 6.18**

Error 42 2.49 0.07 2.23 22.08 35.29 21.58 767136.03 2270.54 19.81 2.22

SS — 91.85 0.41 9.44 214.53 588.42 970.07 4632116.63 1189.08 1.50 11.64

F valu — 4.99 0.74 3.37 3.41 0.75 0.87 1.02 0.45 0.45 0.40

Pr> f — 0.01 0.63 0.03 0.03 0.62 0.54 0.46 0.83 0.83 0.87

Table 3c. The mean sum of squares of yield related traits of test-crosses generated with common inbred tester LM14

Source DF EL EB KR KpR SP 100KW Gr_W Barr_ DrLWt GrLWt

Rep 2 3.45 1.42 3.64 32.86 1480.98 2.86 2851245.34** 1264.72 1.48 8.42

Trt 27 12.41** 1.52 3.03 30.90* 433.66 35.91* 1626474.37** 1380.10* 1.45** 9.80**

Error 54 2.75 1.20 2.07 18.19 294.64 20.40 517350.61 753.91 0.66 4.01

SS 335.19 41.14 81.73 834.38 11708.83 969.59 43914808.00 37262.60 39.22 264.51

F valu 4.51 1.27 1.46 1.70 1.47 1.76 3.14 1.83 2.22 2.44

Pr> f <.0001 0.22 0.12 0.05 0.11 0.04 0.00 0.03 0.01 0.00

Table 3a. The mean sum of squares of morphological traits of test-crosses generated with common inbred tester LM14

Source DF DT DS ASI PH EP TL TB StD StV NoL LL LB LA

Rep 2 197.22** 158.84** 8.10 207.57 434.18* 68.66 19.86 1.94* 1.45 252.33* 4.13* 194.97*

Trt 21 30.82* 117.84** 48.30**2005.73** 595.62** 78.59** 40.37** 1.14*1.256.63** 181.64** 1.57 73.91

Error 42 16.90 23.69 12.22 208.63 135.64 25.03 12.01 0.60 3.13 73.47 0.94 57.10

SS — 832.33 1304.06 3181.72 54154.93 16081.70 2121.94 1090.01 33.81 179.08 4904.24 42.36 1995.68

F valu — 1.82 3.95 4.97 9.61 4.39 3.14 3.36 2.62 2.12 2.47 1.67 1.29

Pr> f — 0.03 <.0001 <.0001 <.0001 <.0001 0.00 0.00 0.00 0.01 0.00 0.05 0.21

Table 3b. The mean sum of squares of physiological traits of test-crosses generated with common inbred tester LM14

Source DF LC CC CTD

Rep 2 0.19 126.41* 8.05

Trt 27 1.01** 57.18* 3.93

Error 54 0.40 32.18 3.01

SS 27.25 1543.94 106.14

F valu 2.54 1.78 1.31

Pr> f 0.00 0.04 0.20


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148.

213

460×

LM

145

913

480×

LM

1419

.0ab

1341

3×L

M14

43.8

abc

27

1331

8×L

M14

3.0bc

1335

7×L

M14

10.5

0bc13

357×

LM

1453

.67cd

1339

3×L

M14

6.27

1347

0×L

M14

48.3

313

409×

LM

148.

5de13

357×

LM

1428

.5ef

28

1344

8×L

M14

2.7c

1346

2×L

M14

10.0

0c13

448×

LM

1450

.00d

1335

7×L

M14

5.6

1332

3×L

M14

43.3

313

448×

LM

146.

7e13

448×

LM

1427

.0f

Gen

eral

Mea

n3.

8—

12.2

6—

70.7

4—

7.37

—53

.14

—15

.1—

38.8

p-V

alue

0.02

—0.

0096

—0.

0023

—0.

0547

—0.

2071

—<

.000

1—

0.00

02

CV

(%)

20.3

3—

14.4

5—

12.1

2—

13.1

5—

14.2

2—

22.8

9—

12.9

SE(d

)0.

636

—1.

446

—6.

999

—0.

792

—6.

17—

2.82

9—

4.08

5

LS

D a

t 1%

1.69

87—

3.85

97—

18.6

86—

NS

—N

S—

7.55

45—

10.9

08


parameters like DT [from 69.33 (13438 × LM13) to 57.67(13494 × LM13)], DS [from 76.50 (13528 × LM13) to62.67 (13494 × LM13)], ASI [from 9.67(13442 × LM13)to 3.50 (13471 × LM13)], PH [from to 171.67 (13479 ×LM13) to 58.33 (13438 × LM13)], EB [from 100.00 (13471× LM13) 38.33 (13438 × LM13)], StD [from 6.27 (13313× LM13) to 4.07 (13438 × LM13)], StV [from 4.50 (13313× LM13) to 3.00 (13438 × LM13)], NL from 15.00 (13442× LM13) to 10.00 (13494 × LM13), LL from 74.67 (13537× LM13) to 55.33 (13361 × LM13), LB from 8.30 (13539× LM13) to 5.47 (13438 × LM13), LA from 59.50 (13528× LM13) to 30.00 (13471 × LM13), TB [from 24.50(13539 × LM13) to 8.50 (13537 × LM13 )] and TL [from43.23 (13430 × LM13) to 27.90 (13396 × LM13)].Similarly, significant variation in mean performance of thetest crosses, obtained by crossing RILs with commoninbred tester LM14 as well (Table 4a). Among the severalmorphological parameters mean performance of the testcrosses differed significantly for traits like DT from 72.33(13462 × LM14) to 60.00 (13486 × LM14), DS from 88.67(13534 × LM14) to 65.00 (13346 ×LM14), ASI 20.67(13534 × LM14) to 3.00 (13346 × LM14), PH from to175.00 (13345 × LM14) to 58.33 (13462 × LM14), EPfrom 95.00 (13345 × LM14) 41.67 (13462 × LM14), StDfrom 6.70 (13346 × LM14) to 4.00 (13357 × LM14), StV5.00 (13346 × LM14) to 2.67 (13448 × LM14), NoL from18.33 (13514 ×LM14) to 10.00 (13462 × LM14), LL from82.00 (13460 × LM14) to 50.00 (13448 × LM14), LB from9.20 (13486 × LM14) to 5.60 (13357 × LM14), from LA70.00 (13409 × LM14) to 43.33 (13323 × LM14), TBfrom 23.67 (13372 × LM14) to 6.67 (13448 × LM14) andTL from 49.00 (13460 × LM14) to 27.00 (13448 × LM14).ASI showed significant variation in mean performance inboth set of test crosses and was as high as 20 days in13534 × LM14. ASI is the most critical trait which getsaffected under stress condition (Richards, 2006). Baena etal., (2007) reported poor development of reproductiveorgans viz., ovules and ovaries as the major reason ofwidening of ASI. Also, the mitosis and meiosis are highlyenergy demanding processes which requires very highenergy and the stress condition might create an internalscarcity with respect to photosynthates, as increasedtemperature increases the respiration rate which in turnleads to loss of photosynthates (Suwa et al., 2010). Theother consequence of widening of ASI is poor seed settingdue to non-synchronization in flowering. Thus ultimatelyreduce the grain yield under stress condition.

Physiological traits

The mean performance of the test crosses (with commoninbred tester LM13) for physiological parameters differedsignificantly (Table 4b) for traits viz., LC from 5.00 (13313× LM13) to 2.67 (13438 × LM13), average CC from 49.50(13507 × LM13) to 30.33 (13438 × LM13) and CTD from29.33 (13528 × LM13) to 27.33 (13319 × LM13). Themean performance of the test crosses generated withcommon tester LM14 also differed significantly (Table 4b)for physiological parameters like LC from 5.33 (13462 ×LM14) to 3.00 (13542 × LM14), average CC from 50.43(13338 × LM14) to 35.30 (13448 × LM14) and CTD from32.33 (13409 × LM14) to 27.00 (13345 × LM14). Heatstress creates severe impact on several physiological traits.The CC under stress condition is an indicator for the healthmetabolic activity in the cell. CC may be responsible forincreased flux of photosynthates to meet the demand ofincreased respiration rate (Abdul and Lee 2011). The CCwas as high as 49.50 in 13507 × LM13 as compared to30.33 in 13438 × LM13. The other important parameter isCTD but the test-crosses generated by using LM14 testerhave shown considerable variation in CTD as compared totest crosses generated using LM13. In previous studies,Pingali and Pandey, (2001) and Ribaut et al., (2004)reported that CTD is a very good indicator of stresstolerance as compared to other traits.

Yield parameters

The test-crosses derived by crossing RILs with commontester LM13 have shown significant variation (Table 4c)for several traits regarding their mean performance of yieldrelated traits. CL has shown significant variation in its meanvalue with range from 12.70 (13430 × LM 13) to 8.37(13492 × LM13). For other traits viz., EB mean valuesvaried from 3.33 (13479 × LM13) to 2.67 (13376 × LM13),KR from 11.69 (13468 × LM13) to 8.16 (13528 × LM13),KpR from 21.57 (13396 × LM13) to 7.43 (13492 × LM13),100 kernel weight from 30.25 (13507 × LM13) to 16.50(13468 × LM13), for SP from 97.10 (13396 × LM13) to72.20 (13492 × LM13) barr 68.23 (13438 × LM13) to -73.33 (13440 × LM13) and for GrW from 4844.20 (13396× LM13) to 440.28 (13492 × LM13), GrLWt from 9.00(13528 × LM13) to 4.17 (13438 × LM13), DrLWt from3.50 (13528 × LM13) to 1.50 (13376 × LM13). Similarly,the test crosses generated using LM14 also shownsignificant variation (Table 4c) in mean performance for


Table 4b. The mean performance of top performing and least performing test-crosses for various physiological traits

Trait LC Avg_CC CTD

Rank Cross Mean Cross Mean Cross Mean

1 13313 x LM13 5.00a 13507 x LM13 49.50a 13528x LM13 29.30

2 13391 x LM13 5.00a 13471 x LM13 49.40a 13430 x LM13 29.00

3 13430 x LM13 5.00a 13468 x LM13 48.95a 13438 x LM13 29.00

21 13376 x LM13 3.50bc 13376 x LM13 32.00c 13479 x LM13 27.70

22 13438 x LM13 2.67c 13438 x LM13 30.33c 13319 x LM13 27.30

General Mean 4.44 — 43.78 — 28.4

p-Value 0.0028 — 0.0002 — 1.0

CV(%) 13.98 — 10.21 — 5.0

SE(d) 0.507 — 3.65 — 1.2

LSD at 1% 1.3674 — 9.8479 — NS

1 13462 × LM14 5.33a 13338× LM14 50.43a 13409 × LM14 32.33

2 13346 × LM14 5.00ab 13460 × LM14 49.23ab 13448 × LM14 31.00

3 13460 × LM14 5.00ab 13505 × LM14 48.77abc 13534 × LM14 31.00

27 13448 × LM14 3.00c 13425 × LM14 35.70de 13425 × LM14 27.33

28 13542 × LM14 3.00c 13448 × LM14 35.30e 13345 × LM14 27.00

General Mean 4.41 — 42.7 — 29.12

p-Value 0.0018 — 0.0362 — 0.1995

CV(%) 14.29 — 13.29 — 5.96

SE(d) 0.515 — 4.631 — 1.417

LSD at 1% 1.3742 — 12.366 — NS

different yield parameters like EL from 15.10 (13534 ×LM14) to 6.501 (13357 × LM14), EB from 6.37 (13514 ×LM14) to 2.09 (13357 × LM14), KR from 13.00 (13425 ×LM14) to 8.66 (13357 × LM14), KpR from 21.00 (13427× LM14) to 7.00 (13357 × LM14), 100 kernel weight from32.83 (13470 × LM14) to 16.17 (13448 × LM14), for SPfrom 95.15 (13486 × LM14) to 50.35 (13443 × LM14)barr 77.80 (13409 × LM 14) to 1.03 (13338 × LM14) andfor GrW from 3222.97 (13312 × LM14) to 458.90 (13462× LM14), GrLWt from 12.00 (13486 × LM14) to 4.25(13357 × LM14), DrLWt from 4.50 (13486 × LM14) to1.67 (13448 × LM14).

The most important of all parameters is yield which isdependent on several yield component traits (Kumar et al.,2011). In fact many traits together contribute for enhancedyield under stress condition. But the identification of relativecontribution of different trait is very important to developa good selection scheme to enhance the rate of geneticgain for stress tolerance. Among all yield contributing traits,HKW is one of the important yield contributing traits (Saleemet al., 2013), varied considerably from 30.25 gm in 13507

× LM13 to 16.50 gm in 13468 × LM13. GrW is the mainselection criteria of all breeders varied from 4844.20 in13396 × LM13 to 440.28 in 13492 × LM13. Whereas theaverage GrW of test crosses was 2154 kgs/ha and 1586kgs/ha in test-crosses generated by using LM13 and LM14respectively. Under severe heat stress condition even if weget hybrids which can yield around 5 t/ha is highlyadvantageous for enhancing the yield and profitability ofthe farming community. The similar studies are also beenreported earlier (Saleem et al., 2013).

Correlation among different traits

The inter-relationship of 26 traits under managed heat stressrepresented by phenotypic correlation coefficient ispresented in Table 5. It revealed that ASI had positive andhighly significant correlation with DS (0.73) and highlynegative correlation with StV (-0.3). The average CCshowed very high and positive association with GrW (0.30),LB (0.50), LC (0.59), LL (0.54), StD (0.52) and StV (0.53).Similarly, average barr showed very high and negative


Tabl

e 4c

. The

mea

n pe

rfor

man

ce o

f to

p pe

rfor

min

g an

d le

ast p

erfo

rmin

g te

st-c

ross

es f

or y

ield

and

yie

ld r

elat

ed tr

aits

Tra

itEL

EB

KR

KpR

100K

W

Ran

kC

ross

Mea

nC

ross

Mea

nC

ross

Mea

nC

ross

Mea

nC

ross

Mea

n

113

430

× L

M13

12.7

0a13

479

× L

M13

3.3

1346

8 ×

LM

1311

.713

396

× L

M13

21.6

1339

6 ×

LM

1330

.3

213

396

× L

M13

12.4

3a13

396

× L

M13

3.3

1339

6 ×

LM

1311

.213

468

× L

M13

21.3

1339

6 ×

LM

1329

.8

313

528×

LM

1312

.31ab

1337

9 ×

LM

133.

213

319

× L

M13

10.8

1339

6 ×

LM

1319

.213

430

× L

M13

28.7

2113

361×

LM

138.

57cd

1349

2 ×

LM

132.

713

442

× L

M13

8.4

1339

6 ×

LM

1310

.213

376

× L

M13

19.7

2213

496

× L

M13

8.37

d13

376

× L

M13

2.7

1352

8 ×

LM

138.

213

396

× L

M13

7.4

1349

2 ×

LM

1316

.5

Gen

eral

Mea

n10

.54

—3.

0—

10.0

—14

.5—

24.8

p-V

alue

0.02

74—

0.1

—0.

4—

0.1

—0.

1

CV

(%)

14.9

7—

8.7

—14

.9—

32.5

—18

.7

SE(d

)1.

289

—0.

2—

1.2

—3.

8—

3.8

LS

D a

t 1%

3.47

73—

NS

—N

S—

NS

—N

S

113

534

× L

M14

15.1

0a13

514

× L

M14

6.37

1342

5 ×

LM

1413

1342

7 ×

LM

1421

.00a

1347

0 ×

LM

1432

.83a

213

427

× L

M14

14.9

0a13

497

× L

M14

3.57

1333

8 ×

LM

1411

.57

1342

5 ×

LM

1420

.84a

1346

0 ×

LM

1430

.17ab

313

460

× L

M14

14.4

0ab13

427

× L

M14

3.5

1348

0 ×

LM

1411

.53

1353

5 ×

LM

1417

.84ab

1354

2 ×

LM

1429

.50ab

2713

312×

LM

148.

56hI

1344

8 ×

LM

142.

3313

372

× L

M14

913

448

× L

M14

9.57

bc13

357

× L

M14

18.0

0cd

2813

357

× L

M14

6.50

I13

357

× L

M14

2.09

1335

7 ×

LM

148.

6613

357

× L

M14

7.00

c13

448

× L

M14

16.1

7d

Gen

eral

Mea

n11

.26

—3.

12—

10.1

8—

14.2

3—

24.3

5

p-V

alue

<.0

001

—0.

2218

—0.

1181

—0.

0489

—0.

0387

CV

(%)

14.7

3—

35.0

7—

14.1

4—

29.9

8—

18.5

5

SE(d

)1.

355

—0.

893

—1.

176

—3.

482

—3.

688

LS

D a

t 1%

3.61

69—

NS

—N

S—

9.29

82—

9.84

63


Tabl

e 4c

(co

ntd)

. The

mea

n pe

rfor

man

ce o

f to

p pe

rfor

min

g an

d le

ast p

erfo

rmin

g te

st-c

ross

es f

or y

ield

and

yie

ld r

elat

ed tr

aits

Tra

itSP

Bar

rG

rWG

rLW

tD

rLW

t.

Ran

kC

ross

Mea

nC

ross

Mea

nC

ross

Mea

nC

ross

Mea

nC

ross

Mea

n

113

396

× L

M13

97.1

0a13

438

× L

M13

68.2

1339

6 ×

LM

1348

44.2

0a13

528

× L

M13

9.00

a13

528

× L

M13

3.5

213

361

× L

M13

95.8

3a13

492

× L

M13

57.9

1336

1 ×

LM

1336

68.7

0ab13

430

× L

M13

8.67

a13

422

× L

M13

3.17

313

468

× L

M13

95.3

0a13

379

× L

M13

38.8

1343

0 ×

LM

1332

52.8

3abc

1331

9 ×

LM

138.

67a

1343

0 ×

LM

133

2113

438

× L

M13

74.0

3c13

396

× L

M13

-35.

313

438

× L

M13

756.

53fg

1336

1 ×

LM

134.

83cd

1343

8 ×

LM

131.

67

2213

492

× L

M13

72.2

0c13

440

× L

M13

-73.

313

492

× L

M13

440.

28g

1343

8 ×

LM

134.

17d

1337

6 ×

LM

131.

5

Gen

eral

Mea

n90

.08

—17

.8—

2154

.76

—6.

91—

2.46

p-V

alue

0.00

02—

0.3

—<

.000

1—

0.00

23—

0.08

77

CV

(%)

6.59

—26

8.2

—40

.65

—21

.56

—27

.89

SE(d

)4.

85—

38.9

—71

5.14

1—

1.21

6—

0.56

1

LS

D a

t 1%

13.0

86—

NS

—19

29.5

—3.

2797

—N

S

113

486

× L

M14

95.1

513

409

× L

M14

77.8

0a13

312×

LM

1432

22.9

7a13

486

× L

M14

12.0

0a13

486

× L

M14

4.50

a

213

480

× L

M14

95.1

1342

7 ×

LM

1475

.57ab

1348

0 ×

LM

1427

17.0

7ab13

427

× L

M14

11.6

7ab13

460

× L

M14

4.00

ab

313

312×

LM

1495

1346

2 ×

LM

1473

.10ab

1350

5 ×

LM

1425

73.0

0abc

1346

0 ×

LM

1411

.17ab

c13

497

× L

M14

3.67

abc

2713

462

× L

M14

56.3

713

346

× L

M14

8.33

d13

448

× L

M14

518.

28f

1344

8 ×

LM

144.

50e

1335

7 ×

LM

142.

00cd

2813

443

× L

M14

50.3

513

338×

LM

141.

03d

1346

2 ×

LM

1445

8.90

f13

357

× L

M14

4.25

e13

448

× L

M14

1.67

d

Gen

eral

Mea

n83

.46

—39

.73

—15

86.3

3—

8.16

—2.

82

p-V

alue

0.11

3—

0.02

95—

0.00

02—

0.00

26—

0.00

65

CV

(%)

20.5

7—

69.1

1—

45.3

4—

24.5

5—

28.6

7

SE(d

)14

.015

—22

.419

—58

7.27

—1.

635

—0.

661

LS

D a

t 1%

NS

—59

.858

—15

68—

4.36

62—

1.76

53


Tabl

e 5.

The

cor

rela

tion

coe

ffic

ient

bet

wee

n al

l the

trai

ts s

tudi

ed

A

SIC

CB

arr

EP

CT

DS

DT

DLW

GLW

GrW

LA

LB

LC

LLN

oLP

HSP

StD

StV

TB

TL

KW

EB

ELK

RK

pR

ASI

1-0

.10.

1-0

.20.

20.

70.

2-0

.1-0

.1-0

.30.

0-0

.2-0

.10.

0-0

.1-0

.3-0

.3-0

.2-0

.3-0

.10.

0-0

.10.

00.

0-0

.2-0

.1

CC

1

-0.3

0.4

-0.3

-0.3

-0.3

0.4

0.5

0.3

0.0

0.5

0.6

0.5

0.3

0.4

0.2

0.5

0.5

0.1

0.5

0.1

0.1

0.2

0.2

0.3

Bar

r

1.

0-0

.20.

20.

20.

20.

00.

0-0

.40.

00.

00.

00.

0-0

.1-0

.3-0

.3-0

.1-0

.10.

00.

0-0

.10.

00.

0-0

.1-0

.2

EP

1.

0-0

.2-0

.3-0

.30.

30.

30.

50.

00.

30.

30.

30.

40.

90.

40.

40.

40.

30.

20.

10.

30.

10.

20.

2

CT

1.0

0.4

0.5

-0.1

-0.1

-0.3

0.0

-0.2

-0.2

-0.1

-0.1

-0.2

-0.2

-0.2

-0.3

-0.1

-0.1

-0.1

0.0

0.0

0.0

-0.1

DS

1.

00.

8-0

.3-0

.1-0

.40.

0-0

.3-0

.2-0

.1-0

.2-0

.4-0

.5-0

.2-0

.4-0

.2-0

.1-0

.1-0

.10.

0-0

.2-0

.1

DT

1.0

-0.3

0.0

-0.3

0.0

-0.3

-0.2

-0.1

-0.1

-0.3

-0.4

-0.2

-0.3

-0.2

-0.2

0.0

-0.1

0.1

-0.2

-0.1

DLW

1.

00.

60.

20.

10.

50.

30.

50.

20.

30.

20.

30.

40.

30.

30.

00.

10.

20.

10.

2

GLW

1.0

0.1

0.1

0.6

0.3

0.7

0.2

0.2

0.1

0.4

0.4

0.3

0.5

0.1

0.1

0.3

0.2

0.2

GrW

1.

00.

10.

10.

30.

10.

20.

50.

60.

30.

30.

10.

20.

00.

10.

20.

40.

5

LA

1.0

0.1

0.0

0.1

0.0

0.0

0.2

0.0

0.1

0.0

0.1

0.0

0.1

0.1

0.2

0.2

LB

1.

00.

30.

50.

20.

30.

20.

50.

50.

30.

50.

10.

00.

20.

10.

2

LC

1.0

0.4

0.4

0.3

0.2

0.5

0.5

0.1

0.4

0.0

0.1

0.2

0.1

0.1

LL

1.0

0.2

0.3

0.1

0.4

0.4

0.2

0.6

0.1

0.1

0.2

0.1

0.1

NoL

1.0

0.4

0.2

0.4

0.4

0.2

0.1

0.1

0.1

0.2

0.0

0.1

PH

1.

00.

30.

40.

30.

20.

20.

10.

30.

20.

20.

2

SP

1.

00.

20.

40.

00.

10.

00.

10.

00.

10.

3

StD

1.

00.

80.

20.

40.

10.

10.

20.

10.

2

StV

1.0

0.2

0.4

0.1

0.1

0.2

0.2

0.2

TB

1.

00.

10.

10.

10.

20.

10.

1

TL

1.0

0.1

0.2

0.2

0.3

0.2

KW

1.

00.

00.

20.

10.

1

EB

1.0

0.1

0.3

0.2

EL

1.0

0.3

0.7

KR

1.0

0.6

KpR

1.

0


association with GrW (-0.42). Average EP as depicted hadsignificant and highly positive correlation with GrW (0.48)and PH (0.85). A positive and highly significant associationwere also reported among average CTD and DS (0.44)and DT (0.46). A negative and significant correlationbetween average DS and DT and GrW were -0.38 and -0.32 respectively. The trait like average DrLWt had veryhigh and positive association with GrLWt (0.61), LB (0.55)and LL (0.52). Analysis revealed that average GrLWt hadhighly significant and positive association with LB (0.61),LC (0.33), LL (0.68), StD (0.41), StV (0.45), TB (0.30),TL (0.47) and average EL (0.35). Average GrW showedhigh and positive association with PH (0.51) and SP (0.57).Similarly, positive and highly significant association wasrecorded for average LB with LL (0.55), StD (0.54), StV(0.55), average LL with TL (0.64), average EL with averageKpR (0.66) and average KR with average KpR (0.57). Theseresults are similar with the earlier reports of Khodarahmpour(2012), Kilen and Andrew, (1969) and Hunter et al., (1977)showing significant associations of these traits. Thesefindings of the study helped in identifying few RILs whichcan be used for developing stress resilient hybrids in futureby using RILs as one of the parental lines in eitherdeveloping hybrids or in developing heat stress synthetics.

References

Ahuja, I., De Vos, R. C., Bones, A. M., & Hall, R. D. (2010). Plantmolecular stress responses face climate change. Trends in PlantScience, 15(12): 664-674.

Aldrich, Sauel R., Walter, O. S., & Robert G. H. (1986). Modern cornproduction. 3rd edition. A & L Publications, Inc., Champaign,IL.

Al-Khatib, K., & Paulsen, G. M. (1999). High temperature effectson photosynthetic processes in temperate and tropical cereals.Crop Science, 39(1): 119-125.

Ashraf, M., & Hafeez, M. (2004). Thermotolerance of pearl milletand maize at early growth stages: growth and nutrient relations.Biologia Plantarum, 48(1): 81-86.

Asian Development Bank (2009). The economics of climate changein Southeast Asia: a regional review. Manila.

Badu-Apraku, B., Hunter, R. B., & Tollenaar, M. (1983). Effect oftemperature during grain filling on whole plant and grain yieldin maize (Zea mays L.). Can. J. Plant Sci., 63: 357-363.

Baena, G. E., Rolland, F., Thevelein, J. M., & Sheen, J. (2007). Acentral integrator of transcription networks in plant stress andenergy signalling. Nature, 448: 938-942.

Chen, J., Xu, W., Velton, J., Xin, Z., & Stout, J. (2012).Characterization of maize inbred lines for drought and heattolerance. Journal of Soil and Water Conservation, 67: 5.

Commuri, P. D., & Jones, R. J. (2001). High temperatures duringendosperm cell division in maize. Crop Science, 41(4): 1122-1130.

Hall, A. E. (2001). Crop Responses to Environment. CRC PressLLC, Boca Raton, Florida.

Hunter, R. B., Tollenaar, M., & Breuer, C. M. (1977). Effects ofphotoperiod and temperature on vegetative and reproductivegrowth of a maize (Zea mays) hybrid. Canadian Journal ofPlant Science, 57: 1127-1133.

Johnson, C. (2000). Ag answers: post-pollination period critical tomaize yields. Agricultural Communication Service, PurdueUniversity. 42p.

Khodarahmpour, Z. (2012). Morphological classification of maize(Zea mays L.) genotypes in heat stress condition. Journal ofAgricultural Science, 4(5): 31.

Kilen, T. C., & Andrew, R. H. (1969). Measurement of droughtresistance in corn. Agron. J., 61: 669-672.

Kumar, T. S., Reddy, D. M., Reddy, R. H., & Sudhakar, P. (2011).Targeting of traits through assessment of interrelationship andpath analysis between yield and yield components for grainyield improvement in single cross hybrids of maize (Zea maysL.). International Journal of Applied Biology and PharmaceuticalTechnology, 2(3): 123-129.

Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon,W. P., & Naylor, R. L. (2008). Prioritizing climate changeadaptation needs for food security in 2030. Science, 319: 607-610.

Pingali, P. L., & Pandey, S. (2001). Part I: Meeting World MaizeNeeds: Technological Opportunities and Priorities for the PublicSector. In: Pingali, P.L. (ed.). Meeting World Maize Needs:Technological Opportunities and Priorities for the Public Sector.1999/2000 World Maize Facts and Trends. Mexico, D.F.:CIMMYT.

Porter, J. R., & Semenov, M. A. (2005). Crop responses to climaticvariation. Philos. T. R. Soc. B., 360: 2021-2035.

Prasad, P. V. V., Boote, K. J., Allen, L. H., Sheehy, J. E., & Thomas,J. M. G. (2006). Species, ecotype and cultivar differences inspikelet fertility and harvest index of rice in response to hightemperature stress. Field Crops Research, 95(2): 398-411.

Ribaut, J. M., Bänziger, M., Setter, T. L., Edmeades, G. O., &Hoisington, D. (2004). Genetic dissection of drought tolerancein maize: a case study. In: Nguyen, H. and Blum, A. (Eds.).Physiology and Biotechnology Integration for Plant Breeding.New York: Marcel Dekker Inc. pp. 571-611.

Richards, R. A. (2006). Physiological traits used in the breeding ofnew cultivars for water-scarce environments. Agricultural WaterManagement, 80(1): 197-211.

Rizhysky, L., Liang, H., Shuman, J., Shulaev, V., Davletova, S., &Mittler, R. (2004). When defense pathways collide: Theresponse of Arabidopsis to a combination of drought and heatstress. Plant Physiol., 134: 1683-1696.

Saleem, U. R., Arif, M., Hussain, K., Hussain, S., Mukhtar, T.,Razaq, A., & Iqbal, R. A. (2013). Evaluation of maize hybridsfor tolerance to high temperature stress in central Punjab.American Journal of Bioengineering and Biotechnology, 1(1):30-36.

Smith, K. L. (1996). Ohio Agron. Guide. Corn prod. Ohio stateUniv. USA, Bulletin: 472.

Steven, J., Brandner, C., & Salvucci, M. (2002). Sensitivity ofphotosynthesis in C4 maize plant to heat stress. PlantPhysiology, 129: 1773-1780.


Suwa, R., Hakata, H., Hara, H., El-Shemy, H. A., Adu-Gyamfi, J. J.,Nguyen, N. T., Kanai, S., Lightfoot, D. A., Mohapatra, P. K., &Fujita, K. (2010). High temperature effects on photosynthatepartitioning and sugar metabolism during ear expansion in maize(Zea mays L.) genotypes. Plant Physiol. Biochem., 48: 124-130.

Ulukan, H. (2009). Environmental management of field crops: A casestudy of Turkish agriculture. Int. J. Agric. Biol., 11: 483-494.

Wahid, A., Gelani, S., Ashraf, M., & Foolad, M.R. (2007). Heattolerance in plants: an overview. Environmental andExperimental Botany, 61(3): 199-223.

Wassmann, R., Jagadish, S.V.K., Sumfleth, K., Pathak, H., Howell,G., Ismail, A., Serraj, R., Redona, E., Singh, R. K., & Heuer, S.(2009). Regional vulnerability of climate change impacts onAsian rice production and scope for adaptation. Adv. Agron.,102: 91-103.


RESEARCH PAPER

A. Amarender Reddy: [email protected]

1Consultant, 2Director, National Institute of Agricultural ExtensionManagement, Hyderabad

Received: 15 March 2017/ Accepted: 13 May 2017© Maize Technologists Association of India 2017

Price forecasting of maize in major states

Ashwini Darekar1 · A. Amarender Reddy2

Abstract: Maize is one of the most important cereal cropof the world and contributes to food security in most ofthe developing countries. In India, maize is emerging asthird most important crop after rice and wheat. Hence, itis important to have some idea about future harvest pricesbefore sowing. Therefore, the present study wasundertaken to build a model to forecast harvest prices ofkharif season based on past monthly modal prices of maizein selected states viz; Madhya Pradesh, Andhra Pradesh,Karnataka, Bihar and Rajasthan. The time series data onmonthly wholesale price data for a period of 11 years(January, 2006 to December, 2016) was used for thispurpose. ARIMA analysis was employed to quantify thevariation in prices and also to forecast maize prices for theharvesting period. To test the reliability of model MAPE,AIC, and BIC Criterion were used. The model was validatedfor the year 2016-17. Model parameters were estimatedusing the R programming software. In kharif season thecrop is harvested during September – December andForecast shows that market prices of maize, would beruling in the range of Rs. 1,200-1,600 per quintal in thisseason, 2017-18. The forecasted results suggest that thereis likely possibility of higher maize prices during theharvesting season of 2017-18 compared to the last year.Hence, farmers are advised to increase acreage under maizewherever suitable soil and agro-climatic conditions exists.

Keywords: ACF · ARIMA · Box and Jenkins · Forecasting· PACF

Introduction

Maize is one of the most important cereal crops of theworld and contributes to food security in most of thedeveloping countries. Its demand in animal and poultry feedindustry and industrial uses like starch industry is increasingyear after year (Reddy, 2013; Reddy et al., 2013). Maizeis grown all over the world in more than 100 countries.The United States produces near about 40 per cent of theworld’s harvest. China, Brazil, Mexico, Indonesia, India,France and Argentina are other top maize producingcountries. India ranks 6th in production and 15th inproductivity of maize in the World. Maize is one of theimportant coarse cereal crops grown in different agro-climatic conditions of India. It is emerging as third mostimportant crop after rice and wheat. Its importance lies inthe fact that it is not only used for human food and animalfeed but at the same time it is also widely used for cornstarch industry, corn oil production, baby corns etc. Maizeis the preferred source of energy in feed when comparedwith other substitutes due to availability, higher energy andprice economics. Government has been providing pricesupport mechanism to encourage farmers to grow maizeas it has ready use in starch and feed meal industries.Scientific grading and transparent price signals havecontributed in a large way to the farm-gate efficiency ofmaize.

In India maize is grown all over the country and standsfifth in area and second in terms of production andproductivity. Karnataka, Andhra Pradesh, Madhya Pradesh,Bihar, Rajasthan, Tamil Nadu, Telangana and Uttar Pradeshare the major producing states which account for 85 percent of India’s maize production and 80 per cent of areaunder cultivation. In 2015-16, Karnataka was the largestmaize producing state in India with a production of 3.01million tonnes. Andhra Pradesh was next to the Karnatakawith a production of 2.76 million tonnes. In case of area

Karnataka stands first and Andhra Pradesh ranks fifth afterRajasthan, Madhya Pradesh and Maharashtra with an areaof 0.78 million hectares.

Burhan (2006) attempted to forecast exports of mangofrom Pakistan for the years 2005 to 2024 by using loglinear and ARIMA models. Haque et al. (2006) examinedthat the ARIMA model is more efficient for short-termforecasting than the quadratic model in case of shrimp andfrozen food export earning of Bangladesh. Cordeiro et al.(2010) built a forecasting model for the export price ofpine sawn wood in Brazil, using the ARIMA model. Toaccomplish the forecasts in relation to prices of soybean,rice and sweetcorn in RS state, Marchezan and Souza(2010) made predictions for 2007 year by using ARIMAmodel. Paul and Das (2010) have attempted forecasting ofinland fish production in India by using ARIMA approach.Kumar et al. (2011) compared the forecastingperformances of H-WES and Seasonal ARIMA time-seriesmethodology for forecasting onion prices in Bangaloremarket and proved that ARIMA model can be successfullyused for modelling as well as forecasting of monthly priceof onion in Bangalore. Burark and Sharma (2012) confirmedthe suitability of ARIMA models in agricultural priceforecasting. Ozer and Ilkdogan (2013) examined cottonprices in the world by ARIMA model, by using 102 permonth which covered the period January 2004 and June2012 of the world price of cotton. Darekar et al. (2015)forecasted onion prices in Lasalgaon and Pimpalgaonmarket. Darekar et al. (2016) validated that ARIMA modelforecasted onion prices in Kolhapur and Yeola marketsrespectively.

Keeping this mind, the present study has been attemptedto forecast the monthly average prices of maize duringharvesting season by using Autoregressive integratedMoving Average (ARIMA) model.

Materials and methods

Data collection

The time series data on monthly average price of maizefor 11 years (from January, 2006 to December, 2016) ofMadhya Pradesh, Andhra Pradesh, Karnataka, Bihar,Rajasthan and India was used for forecasting the prices.The time series data related to monthly average prices wascollected from AGMARKNET website as per the availability.Future prices of maize were forecasted during harvestingmonths by using past data.

Time series analysis

Auto Regressive Integrated Moving Average (ARIMA): TheBox-Jenkins models (1976), are especially suited to shortterm forecasting because most ARIMA models place greateremphasis on the recent past rather than the distant past. Inthis study, the analysis performed by using ARIMA isdivided into four stages. R programming software wasused for time series analysis and developing ARIMA modelsand forecasting maize prices. These methods have alsobeen useful in many types of situations which involve thebuilding of models for discrete time series and dynamicsystems (Granger and Newbold, 1970). Originally ARIMAmodels have been studied extensively by George Box andGwilym Jenkins during 1968 and their names havefrequently been used synonymously with general ARIMAprocess applied to time series analysis, forecasting andcontrol.

Identification Stage: The stationary check of time seriesdata was performed, which revealed that the maize priceswere nonstationary. The non-stationary time series datawere made stationary by first order differencing. After that,best fit ARIMA models were developed using the data fromJanuary 2006 to December 2016 and used to forecast theprices during harvesting season. Candidate ARIMA modelswere identified by finding the initial values for the ordersof non-seasonal parameters “p”and “q.” They wereobtained by looking for significant spikes in auto correlationand partial auto correlation functions.

Estimation Stage: ARIMA models are fitted and accuracyof the model was tested on the basis of diagnosticsstatistics.

Diagnostic Checking: The best model was selected basedon the following diagnostics.(i) Low Akaike Information Criteria (AIC): AIC is

estimated by AIC = (–2log L+2m), where, m = p + qand L is the likelihood function.

(ii) Low Bayesian Information Criteria (BIC): Sometimes,Bayesian Information Criteria (BIC) is also used andestimated by BIC = log σ2 + (mlog n)/n.

(iii) The Mean Absolute Percent Error (MAPE) was usedas a measure of accuracy of the models

Forecasting Stage: Future values of the time series wereforecasted at this stage. Ansari and Ahamed (2001) appliedARIMA modeling for time series analysis of world tea pricesand industrialized countries export prices. Prawin Arya etal. (2005) applied Box-Jenkins Approach for ForecastingCopra Wholesale Price Series. Nochai and Titida (2006)



used ARIMA model for forecasting oil palm prices. Punitha(2007) used ARIMA model to forecast the arrivals andprices of maize and ground nut in Hubli and Devangeremarkets in Karnataka state. Rabbani et al. (2009) appliedARIMA and forecasted wheat prices in Bangladesh. Shankar& Prabhakaran (2012) used the ARIMA model forforecasting the milk production in Tamil Nadu. Chaudhariand Tingre (2013) found that ARIMA (1,1,0) was the bestfitted model for forecasting of milk production in India.

Results and discussion

Identification

Identification of the model was concerned with decidingthe appropriate values of (p, d, q) (P, D, Q). It was doneby observing Auto Correlation Function (ACF) and PartialAuto Correlation Function (PACF) values. The AutoCorrelation Function helps in choosing the appropriatevalues for ordering of moving average terms (MA) andPartial Auto-Correlation Function for those autoregressiveterms (AR).

Estimation

The number of non-zero coefficients in ACF determinesorder of MA terms and the number of non-zero coefficientsin PACF plots determines order of AR terms. ARIMA modelwas estimated after transforming the variables under studyinto stationary series through computation of eitherseasonal or non-seasonal or both, order of differencing. Acareful examination of ACF and PACF up to 24 lags revealedthe presence of seasonality in the data. However, the serieswas found to be stationary, since the coefficient droppedto zero after the second lag. Forecast for the seasonallyadjusted maize prices by using best fit ARIMA model in Rprograming software shown in Fig. 1.

Diagnostic checking

Preceding 11 years (2006 - 2016) monthly prices data usedfor this model. Various methods and literature are studiedto judge the appropriate model, the best model has beenselected based on the MAPE, lowest minimum AIC andBIC. It has been found that ARIMA (1,1,1) (2,0,0), (0,1,2),(0,1,1) (0,0,1), (1,1,0), (0,1,0) (2,0,0) and (0,1,1) wasthe best fitted model for the maize price data of MadhyaPradesh, Andhra Pradesh, Karnataka, Bihar, Rajasthan andIndia respectively (Table 1).

Forecasting

The forecasting was done for harvesting season fromSeptember to December, 2017-18. The results of forecastof prices of maize in the market are shown in Table 2. Theforecasted values of prices of maize in the selected statesand the forecasted values of prices showed an increasingtrend in the future months. The forecast shows that marketprices of maize, would be ruling in the range of Rs. 1,200– 1,600 per quintal in kharif harvesting season, 2017-18.

The prices of maize in the market during September toDecember 2017-18 would be high in Madhya Pradesh,

Table 1. Residual analysis of monthly prices of maize in selected states

S.No. State ARIMA Model MAPE AIC BIC

1. Madhya Pradesh (1,1,1) (2,0,0) 4.96 1514.76 1528.65

2. Andhra Pradesh (0,1,2) 4.50 1533.19 1544.37

3. Karnataka (0,1,1) (0,0,1) 4.76 1511.42 1519.86

4. Bihar (1,1,0) 3.89 1372.30 1380.74

5. Rajasthan (0,1,0) (2,0,0) 4.02 1459.05 1467.48

6. India (0,1,1) 3.78 1401.11 1409.54

Fig. 1. Forecast for the seasonally adjusted maize prices in India

Forecasts from ARIMA (0,1,1) with draft


Andhra Pradesh and Rajasthan i.e. Rs. 1,500, 1,450 and1,450 per q, respectively. The prices would be low i.e. Rs.1,400 and 1,350 per q in Karnataka and Bihar respectively.Forecasted prices of maize by using ARIMA (0, 1, 1) modelin India is shown in Fig. 2. This forecast is based on pastdata and model and that actual market price may not turnout to be the same as forecasted.

Conclusions

Forecasting of future maize prices can help the farmers todecide the area allocation and marketing. The study usedARIMA model to forecast prices of maize during the harvestperiod by using past ten year monthly data. The study alsovalidated the model and model predicted the future priceswith 95% confidence interval. Similar model was used byPunitha (2007) and Darekar et al. (2015) to forecast the

prices and arrivals of agricultural commodities and drawnconclusions. ARIMA model is an extrapolation method thatrequires only the historical time series data on the variableunder study. The forecasted results suggest that there arelikely possibility of higher maize prices during harvestingseason (September to December) when compared to thelast year. Hence, farmers are advised to increase maizeacreage where ever suitable agro-climatic conditions exist.This forecast is based on past data and model and thatactual market price may not turn out to be the same asforecasted. There may be some possible deviations of theactual prices from the predicted prices in the light oftentative developments in the commodity markets such aschange in international prices, export or import restrictionsetc.

References

Ansari, M. I., & Ahmed, S. M. (2001). Time series analysis of Teaprices: An application of ARIMA modeling and co integrationanalysis. Indian Economics Journal, 48: 49-54.

Box, G. E. P., & Jenkin, G. M. (1976). Time Series of Analysis,Forecasting and Control, Sam Franscico, Holden-Day, California.USA.

Burark, S. S., & Sharma, H. (2012). Price Forecasting of Coriander:Methodological Issues. Agricultural Economics ResearchReview, 25(Conference Number): 530.

Burhan, A. K. M. M. (2006). Forecasting mango export from Pakistan:an econometric analysis of time series data. Science International,18(3): 253-256.

Table 2. Projected prices for maize in major producing states duringkharif harvesting season 2017-18 (Rs/q)

S.No. State Lower Mid- UpperLimit point Limit

1. Madhya Pradesh 1,300 1,500 1,600

2. Andhra Pradesh 1,250 1,450 1,550

3. Karnataka 1,200 1,400 1,600

4. Bihar 1,250 1,350 1,450

5. Rajasthan 1,250 1,450 1,550

6. India 1,200 1,400 1,600

Fig. 2. Forecasted maize prices in India during kharif harvesting season


Chaudhari, D. J., & Tingre, A. S. (2014). Use of ARIMA modelingfor forecasting green gram prices for Maharashtra. Journal ofFood Legumes, 27(2): 136-139.

Cordeiro, S. A., Soares, N. S., Braga, M. J., Silva, M. L., & Da.(2010). Export price forecast of Brazilian pine sawn wood.Scientia Forestalis, 38(86): 205-214.

Darekar, A. S., Pokharkar, V. G., & Datarkar, S. B. (2016). OnionPrice Forecasting In Kolhapur Market of Western MaharashtraUsing ARIMA Technique. International Journal of InformationResearch and Review, 3(12): 3364-3368.

Darekar, A. S., Pokharkar, V. G., Gavali, A. V., & Yadav, D. B. (2015).Forecasting the prices of onion in Lasalgaon and Pimpalgaonmarket of Western Maharashtra. International Journal of TropicalAgriculture, 33(4): 3563-3568.

Haque, M. E., Imam, M. F., & Awal, M. A. (2006). Forecastingshrimp and frozen food export earning of Bangladesh usingARIMA model. Pakistan Journal of Biological Sciences, 9(12):2318-2322.

Kumar, T. L. M., Munirajappa, R., & Surendra, H. S. (2011).Application of seasonal ARIMA model for forecasting monthlyprices of potato in Bangalore market. Mysore Journal ofAgricultural Sciences, 45(4): 778-782.

Marchezan, A., & Souza, A. M. (2010). Forecasting the price ofmajor grains produced in Rio Grande do Sul. Ciencia Rural,40(11): 2368-2374.

Ozer, O. O., & Ilkdogan, U. (2013). The world cotton price forecastingby using Box-Jenkins model. [Turkish]. Journal of TekirdagAgricultural Faculty, 10(2): 13-20.

Paul, R. K., & Das, M. K. (2010). Statistical modelling of inland fishproduction in India. Journal of the Inland Fisheries Society ofIndia, 42: 1-7.

Punitha, S. B. (2007). A Comparative analysis of market performanceof agricultural commodities - An economic approach. M.Sc.(Agri.) Thesis, University of Agriculture Science, Dharwad,Karnataka, India.

Reddy, A. A. (2013). Training manual on value chain analysis ofdryland agricultural commodities. International Crops ResearchInstitute for Semi-Arid Tropics (ICRISAT), Hyderabad,pp.112.

Reddy, A. A., Yadav, O. P., Dharm Pal Malik, S. I., Ardeshna, N. J.,Kundu, K. K., Gupta, S. K., & Sammi Reddy, K. (2013).Utilization pattern, demand and supply of pearl millet grainand fodder in Western India (No. 37, pp. 1996-2009). WorkingPaper Series.


RESEARCH PAPER

Dilip Singh: [email protected]

1M.Sc. PBG Student, 2PI AICRP on Maize, 3Professor, PBG,4Assistant Professor MPUAT, Udaipur-313001, Rajasthan, India

Received: 23 January 2017/ Accepted: 19 April 2017© Maize Technologists Association of India 2017

Morphological and molecular diversity in specialty corn cultivars

D. Chouhan1 · D. Singh2 · S. R. Maloo3 · D. Jain4

Abstract: The present study was carried out using twentydiverse speciality corn genotypes of QPM, sweet corn,pop corn and baby corn. The material was planted duringkharif, 2015, at instructional farm of Rajasthan College ofAgriculture, Udaipur. Observations were recorded fortwenty yield contributing and variability parameters andcorrelation coefficient were computed. DNA was isolatedwith CTAB extraction buffer method and isolated DNAwas used as template for amplification of DNA using fifteenrandomly selected decamer primers. It could be emphasizedthat sufficient variability existed in the material andcharacters like grain yield, stover yield, biological yield,TSS, test weight and cob height exhibited high estimatesof GCV, PCV and heritability. Further some charactersshowed significant positive genotypic and phenotypiccorrelation with grain yield. Baby corn genotype HM-4exhibited highest performance for grain yield while QPMgenotypes (HQPM-1), sweet corn (Madhuri) and pop corn(Amber pop corn) turned out to be the best performers forgrain yield/plant and most of the contributing characters.Molecular characterization through RAPD analysis revealedhigh polymorphism and marked genetic diversity amongthe speciality corn genotypes as also reflected by theirmorphological differences. Hence molecular character-ization appeared to be precise, quick and effective tool inanalyzing genetic diversity and identification of germplasm.

Keywords: Molecular · GCV · PCV and correlation inspecialty corn

Introduction

Maize (Zea mays L.) is one of the most important and highlyevolved coarse cereal crops. In India the maize area,production and productivity were 9.23 million ha, 23.67million tonnes and 26.18 q ha-1, respectively. Informationon the diversity in the germplasm and genetic relationshipsamong breeding materials could be an invaluable aid in cropimprovement strategies (Mohammadi and Prasanna, 2003).The genetic diversity is analyzed by using molecularmarkers.

Molecular techniques are effectively used for detectingdifferences in the DNA of individual genotypes so as toassess variability so as to assess variability amongstcultivars for identification of potential parental lines basedon molecular diversity. These differences in general arecalled molecular markers. DNA based markers provide adirect measure of genetic diversity and go beyond diversitybased on agronomic traits or geographic origin (Dreisigackeret al., 2005), thus help in better germ plasm managementand development of more efficient strategies for cropimprovement. Therefore the present study was carried outwith objectives to assess genetic diversity using RandomlyAmplified Polymorphic DNA (RAPD) marker using 20diverse elite lines/hybrids.


The twenty elite cultivars of sweet corn, pop corn, qualityprotein maize and pop corn were planted during kharif,2015, at Instructional Farm of Rajasthan College ofAgriculture, Udaipur. The experiment was laid out inrandomized block design with three replications. Acomprehensive study was made to record specificobservation/ phenotypic characters in terms of “descriptors”for each genotype. The details of characters was studied


are included in Table 1. The molecular diversity was studiedby using decamer oligonucleotide primers. DNA extractedfrom different speciality corn genotypes were comparedusing RAPD methodology. The crop was planted with onsetof monsoons and raised with complete recommendedpackages and practices. DNA was extracted from youngleaves (3–4 weeks old) using CTAB method (Doyle andDoyle, 1990). The amplified samples were separated onagarose gel electrophoresis (1.2%). The bands were scoredfor their presence or absence. A set of 15 decameroligonucleotide RAPD primers were used for PCRamplification.

Results

Analysis of variance revealed significant differences amongall the genotypes for different characters indicatingpresence of sufficient variability in the commercialcultivars. It is expected as the material consisting of

different kinds like QPM, pop corn, baby corn and sweetcorns. The consistent and high per se performance ofsuperior genotypes was concluded for twenty characterswhich are given in Table 1. HQPM-1, QPM genotypesMadhuri among sweet corn and Amber pop corn turnedout to be the best performers for grain yield/plant and mostof the yield contributing characters. Further HQPM-7(QPM) and HM-4 (baby corn) displayed high per se forboth grain yield/plant and grain protein content. Therefore,these entries could be gainfully utilized in breedingprogrammes or could be recommended for cultivation aftermulti location evaluation.

Phenotypic coefficient of variation (PCV) estimated washigher than genotypic coefficient of variation (GCV) forall most all the characters. High GCV and PCV was recordedfor grain yield, stover yield and biological yield while highheritability (broad sense) was recorded for TSS, grain yield,stover yield, biological yield, cob height, leaves/plant andgrain rows/cob. Further genetic advance as percentage of

Table 1. Genotypes classified as per their high per se performance

S.No. Character Best genotypes Genotypes showing high per se performance

1. Ear length HM-4 VEHQ-11-1, HQPM-5, Madhuri, Win Orange, VEHQ-14-1 and HQPM-7

2. Ear girth Win Orange Priya, HQPM-5, HQPM-7, HQPM-4, Madhuri and HQPM-1

3. Grain rows/cob HQPM-4 HQPM-1, HQPM-7, HQPM-5, Vivek QPM-9, VEHQ-14-1, MMH QPM 6-12-13 & HM-4

4. Grains/row Win Orange, MMH QPM 6-12-13, Madhuri, V L Amber pop corn, PQPMH-1, Priya and HM-4Vivek QPM-9

5. Tip sterility* HQPM-5, Priya, PQPMH-1, Amber pop corn, Madhuri and HQPM-1Vivek QPM-9

6. Leaves/plant Vivek QPM-9 MMH QPM 6-12-13, HQPM-5, HQPM-1, HQPM- 4, Madhuri , Sugar candy ,VEHQ-14-1 and HM-4

7. Tassel length Misthi HQPM-1, HQPM-4, Vivek QPM-9, VEHQ-14-1, Win Orange and HM-4

8. Cob height HQPM-5 HQPM-4, HQPM-7, VEHQ-11-1, V L Amber pop corn, Amber pop corn and Priya

9. Plant height HQPM-7 HQPM-5, Vivek QPM-9, HQPM-1, HQPM-4, Priya, VEHQ-14-1 and HM-4

10. Days to 50% tasseling HQPM- 5 HQPM-7, Vivek QPM 9, VEHQ-14-1, Madhuri, Win Orange and HQPM-1

11. Days to 50% silking HQPM-5 HQPM-7, Vivek QPM-9, VEHQ-11-1, VEHQ-14-1, Madhuri and HQPM-1

12. Shelling% HQPM-1 PQPMH-1, HQPM-7, Vivek QPM-9, MMH QPM 6-12-13, HM-4and HQPM-4

13. Test weight HM-4 HQPM-7, HQPM-5, HQPM-1, Vivek QPM-9, HQPM-4 and PQPMH-1

14. Grain yield HM-4 HQPM-4, HQPM-1, HQPM-7, HQPM-5, VEHQ-14-1 and Vivek QPM-9

15. Stover yield HM-4 HQPM-4, HQPM-7, HQPM-5, VEHQ-14-1, HQPM-1 and Vivek QPM-9

16. TSS Win Orange Madhuri, Priya, Sugar-75, Sugar candy, Misthi and Bajaura sweet corn

17. Ear/plant Misthi Priya, Win Orange, Bajaura sweet corn, Sugar-75, Amber pop corn and Madhuri

18. Biological yield HM-4 HQPM-4, HQPM-7, HQPM-1, HQPM-5, VEHQ-14-1and Vivek QPM-9

19. HI HQPM-1 HM-4, PQPMH-1, VEHQ-11-1, MMH QPM 6-12-13, Bajaura sweet corn & Sugar-75

20. Protein content Win Orange V L Amber pop corn, Madhuri, Priya, HM-4, VEHQ-14-1 and HQPM-7

*Minimum


Table 2. Mean, standard error (SE±), range, coefficient of variation (CV), genotypic coefficient of variation (GCV), phenotypic coefficient ofvariation (PCV), heritability broad sense (H) & genetic gain (GG) for eleven characters in speciality corn

S.No. Characters Mean ± SE Range CV (%) GCV (%) PCV (%) H (%) Genetic advance as(%) of mean (GG)

1. Ear length 18.31 ± 0.39 15.03-21.37 09.63 07.63 12.29 38.58 09.76

2. Ear girth 13.05 ± 0.28 08.07-15.53 09.62 14.94 17.78 70.70 25.89

3. Grain rows/cob 13.26 ± 0.10 09.90-16.00 03.35 12.60 13.03 93.40 25.08

4. Grains/row 37.38 ± 0.82 26.00-44.00 09.80 10.61 14.45 53.99 16.07

5. Tip sterility* 01.94 ± 0.05 01.00-03.80 12.17 41.73 43.47 92.16 82.53

6. Leaves/plant 12.13 ± 0.06 10.07-13.93 02.24 09.49 09.75 94.69 19.01

7. Tassel length 38.69 ± 0.64 28.33-47.33 07.36 13.70 15.55 77.62 24.87

8. Cob height 71.89 ± 0.74 47.30-110.17 04.61 21.98 22.45 95.78 44.29

9. Plant height 212.4 ± 2.40 172.70-251.37 05.06 11.16 12.10 82.72 20.72

10. Days to 50% tasseling 47.25 ± 0.43 39.33-51.00 04.03 05.70 06.98 66.69 09.59

11. Days to 50% silking 52.12 ± 0.44 45.00-57.00 03.76 06.24 07.28 73.42 11.02

12. Shelling% 73.11 ± 0.70 60.22-81.17 04.26 09.01 09.96 81.75 16.79

13. Test weight 162.9 ± 3.60 107.93-251.30 09.88 22.77 24.82 84.16 43.04

14. Grain yield 34.72 ± 0.70 10.38-07.22 09.01 56.86 57.57 97.55 115.70

15. Stover yield 48.36 ± 1.25 14.43-90.50 11.55 57.37 58.31 96.08 115.63

16. TSS 9.652 ± 0.08 06.80-14.53 03.68 28.76 29.00 98.38 58.78

17. Ear/plant 01.09 ± 0.01 01.00-01.23 06.02 04.94 07.79 40.26 06.46

18. Biological yield 83.08 ± 1.78 13.60-160.70 09.58 57.05 57.85 97.25 115.91

19. HI 41.96 ± 0.31 40.00-43.90 03.35 02.27 04.05 31.56 02.63

20. Grain protein content 10.61 ± 0.07 10.24-10.90 02.92 01.04 03.10 11.28 00.72

*Minimum

mean (genetic gain) along with high estimate of heritabilityand GCV were recorded for cob height, test weight, grainyield/plant, stover yield, TSS and biological yield (Table2). Hence these characters appeared promising and couldbe gainfully utilized in speciality corn improvementprogramme. The results were in close conformity withfindings of Vashistha et al. (2013) and Sharma (2014).

Grain yield/plant showed significant and positivegenotypic and phenotypic correlations with ear length, eargirth, grain rows/cob, total no of leaves/plant, tassel length,cob height, plant height, days to 50 per cent tasseling, daysto 50 per cent silking, shelling percentage, test weight,stover yield and biological yield. Grain protein contentshowed significant positive genotypic correlation with daysto 50 per cent tasseling, TSS and days to 50 per cent silking.However, insignificant association was recorded for grainyield. Wali et al. (2006) and Sumathi et al. (2005) alsoconfirm present findings (Table 3 and 4).

For molecular characterization, 15 primers werescreened, out of which 10 primers produced amplification.

A total of 82 scorable bands were obtained, out of which69 bands were polymorphic and level of polymorphismwas as high as 82.95 per cent. This might be causingmarked morphological differences. Each evaluated primer,produced 5 to 12 fragments. Size of fragments rangedbetween ~200 bp to ~3000 bp. The average number ofbands per primer was found to be 8.2. The averagenumbers of polymorphic bands per primer were 6.9 (Table5 and Plate 1-3).

Jaccard’s Similarity Coefficient value for RAPD primersranged from 0.44 to 0.88. Based on dendrogram generatedthrough UPGMA method (Fig. 1), most of the genotypeswere classified into two main clusters. First cluster included17 genotypes while second cluster possessed only threegenotypes of pop corn. Distinct bands with variableintensity were recorded for QPM genotypes (VQPM-9,VEHQ-14-1, MMH QPM-6-12-13, VEHQ-11-1, HQPM-7and HQPM-1), sweet corn genotypes (Misthi, Bajaura sweetcorn, Priya, Madhuri and Sugar-75) and pop corn


Tabl

e 3.

Gen

otyp

ic c

orre

lati

on a

mon

g gr

ain

yiel

d an

d yi

eld

attr

ibut

ing

char

acte

rs

S.C

ompo

nent

Ear

Ear

Gra

inG

rain

s/T

ip-

Lea

ves/

Tass

elC

obP

lant

Day

sD

ays

Shel

ling

Tes

tG

rain

Sto

ver

TSS

Ear

/B

iolo

gica

lH

IP

rote

inN

o.le

ngth

girt

hro

ws/

row

ster

ilit

ypl

ant

leng

thhe

ight

heig

htto

50%

to 5

0%%

wei

ght

yiel

dyi

eld

plan

tyi

eld

cont

ent

cob

tass

elin

gsi

lkin

g

1.E

ar l

engt

h1.

000.

28*

-0.1

9*0.

020.

070.

37*

0.38

*0.

32*

0.43

**0.

89**

0.85

**-0

.51*

*0.

39**

0.42

**0.

45**

0.04

-0.2

5*0.

44**

-0.5

4**

0.14

2.E

ar g

irth

—1.

000.

31*

0.01

-0.1

10.

54**

0.39

**0.

050.

54**

0.38

**0.

28*

0.09

0.52

**0.

61**

0.64

**0.

35*

-0.2

8*0.

63**

-0.4

5**

0.01

3.G

rain

row

s/co

b—

—1.

00-0

.38*

*0.

140.

50**

0.16

0.47

**0.

47**

0.18

*0.

000.

46**

0.34

*0.

46**

0.46

**-0

.34*

-0.6

6**

0.46

**0.

05-0

.17*

4.G

rain

s/ro

w—

——

1.00

-0.8

3**

0.31

*-0

.23*

-0.1

50.

010.

080.

42**

-0.1

50.

01-0

.09

-0.0

8-0

.05

-0.2

1*-0

.08

-0.2

9*-0

.39*

*

5.T

ip s

teri

lity

——

——

1.00

-0.2

8*0.

110.

01-0

.15

0.00

-0.3

3*-0

.27*

0.00

0.01

0.00

0.12

0.07

0.01

0.22

*0.

06

6.L

eave

s/pl

ant

——

——

—1.

000.

23*

0.22

*0.

49**

0.48

**0.

33*

0.46

**0.

49**

0.57

**0.

58**

-0.2

2*-0

.65*

*0.

58**

-0.3

8**

0.68

**

7.Ta

ssel

len

gth

——

——

——

1.00

-0.1

00.

46**

0.56

**0.

36*

0.05

0.35

*0.

56**

0.54

**0.

21*

0.30

*0.

55**

-0.0

60.

76**

8.C

ob h

eigh

t—

——

——

——

1.00

0.61

**0.

42**

0.27

*0.

29*

0.39

**0.

32*

0.34

*-0

.57*

*-0

.59*

*0.

33*

-0.2

3*-0

.23*

9.P

lant

hei

ght

——

——

——

——

1.00

0.64

**0.

54**

0.48

**0.

81**

0.79

**0.

82**

-0.4

0**

-0.6

7**

0.81

**-0

.35*

-0.1

9*

10.

Day

s to

50%

tas

seli

ng—

——

——

——

——

1.00

0.84

**-0

.13

0.40

**0.

47**

0.53

**-0

.06

-0.2

9*0.

50**

-0.8

0**

0.42

**

11.

Day

s to

50%

sil

king

——

——

——

——

——

1.00

-0.1

30.

34*

0.30

*0.

36*

-0.0

8-0

.17*

0.34

*-0

.69*

*0.

19*

12.

Shel

ling%

——

——

——

——

——

—1.

000.

64**

0.65

**0.

64**

-0.6

8**

-0.8

7**

0.64

**0.

10-0

.52*

*

13.

Test

wei

ght

——

——

——

——

——

——

1.00

0.83

**0.

82**

-0.4

6**

-0.8

5**

0.83

**0.

05-0

.54*

*

14.

Gra

in y

ield

——

——

——

——

——

——

—1.

000.

95**

-0.2

8*-0

.64*

*0.

96**

-0.2

0*0.

03

15.

Sto

ver

yiel

d—

——

——

——

——

——

——

—1.

00-0

.28*

-0.6

5**

0.96

**-0

.26*

0.03

16.

TSS

——

——

——

——

——

——

——

—1.

000.

87**

-0.2

8*-0

.19*

0.33

*

17.

Ear

/pla

nt—

——

——

——

——

——

——

——

—1.

00-0

.64*

*-0

.05

0.34

*

18.

Bio

logi

cal y

ield

——

——

——

——

——

——

——

——

—1.

00-0

.23*

0.03

19.

HI

——

——

——

——

——

——

——

——

——

1.00

-0.7

2**

20.

Gra

in p

rote

in c

onte

nt—

——

——

——

——

——

——

——

——

——

1.00

*Sig

nifi

cant

at 5

and

**

Sig

nifi

cant

at 1

per

cen

t pro

babi

lity

leve

l, re

spec

tive

ly


Tabl

e 4.

Phe

noty

pic

corr

elat

ion

amon

g gr

ain

yiel

d an

d yi

eld

attr

ibut

ing

char

acte

rs

S.C

ompo

nent

Ear

Ear

Gra

inG

rain

s/T

ipL

eave

s/Ta

ssel

Cob

Pla

ntD

ays

Day

sSh

ellin

gT

est

Gra

inS

tove

rTS

SE

ar/

Bio

logi

cal

HI

Pro

tein

No.

leng

thgi

rth

row

s/ro

wst

eril

ity

plan

tle

ngth

heig

hthe

ight

to 5

0%to

50%

%w

eigh

tyi

eld

yiel

dpl

ant

yiel

dco

nten

tco

bta

ssel

ing

silk

ing

1.E

ar l

engt

h1.

000.

51**

-0.0

90.

43**

0.12

0.21

*0.

23*

0.20

*0.

37**

0.54

**0.

47**

0.02

0.40

**0.

35*

0.35

*0.

110.

030.

35**

-0.1

10.

34*

2.E

ar g

irth

—1.

000.

26*

0.33

*-0

.04

0.44

**0.

33*

0.03

0.49

**0.

29*

0.19

*0.

27*

0.53

**0.

57**

0.59

**0.

35*

-0.1

10.

58**

-0.1

9*0.

25*

3.G

rain

row

s/co

b—

—1.

00-0

.26*

0.13

0.47

**0.

160.

45**

0.43

**0.

13-0

.01

0.41

**0.

30*

0.44

**0.

43**

-0.3

3*-0

.38*

0.44

**0.

06-0

.09

4.G

rain

s/ro

w—

——

1.00

-0.5

6**

0.20

*-0

.14

-0.1

20.

090.

060.

22*

0.14

0.17

*0.

010.

020.

030.

000.

02-0

.07

0.30

*

5.T

ip s

teri

lity

——

——

1.00

-0.2

5*0.

100.

00-0

.12

0.04

-0.2

4*-0

.19*

0.01

0.03

0.01

0.13

0.09

0.02

0.12

-0.0

2

6.L

eave

s/pl

ant

——

——

—1.

000.

23*

0.21

*0.

44**

0.37

*0.

28*

0.40

**0.

42**

0.55

**0.

55**

-0.2

1*-0

.43*

*0.

55**

-0.1

9*0.

14

7.Ta

ssel

len

gth

——

——

——

1.00

-0.0

80.

39**

0.40

**0.

26*

0.03

0.27

*0.

49**

0.48

**0.

18*

0.14

0.48

**-0

.05

0.19

8.C

ob h

eigh

t—

——

——

——

1.00

0.54

**0.

31*

0.22

*0.

26*

0.36

*0.

30*

0.33

*-0

.56*

*-0

.35*

0.32

*-0

.12

-0.0

7

9.P

lant

hei

ght

——

——

——

——

1.00

0.53

**0.

46**

0.46

**0.

73**

0.73

**0.

74**

-0.3

4*-0

.36*

0.74

**-0

.14

-0.0

6

10.

Day

s to

50%

tas

seli

ng—

——

——

——

——

1.00

0.86

**-0

.08

0.29

*0.

39**

0.44

**-0

.04

-0.1

00.

42**

-0.3

6*0.

17*

11.

Day

s to

50%

sil

king

——

——

——

——

——

1.00

-0.1

10.

26*

0.25

*0.

30*

-0.0

7-0

.04

0.28

*-0

.36*

0.02

12.

Shel

ling%

——

——

——

——

——

—1.

000.

62**

0.63

**0.

61**

-0.5

7**

-0.4

3**

0.62

**0.

090.

00

13.

Test

wei

ght

——

——

——

——

——

——

1.00

0.76

**0.

75**

-0.3

9**

-0.4

5**

0.76

**0.

040.

01

14.

Gra

in y

ield

——

——

——

——

——

——

—1.

000.

97**

-0.2

6*-0

.38*

0.98

**-0

.09

0.06

15.

Sto

ver

yiel

d—

——

——

——

——

——

——

—1.

00-0

.26*

-0.3

7*0.

96**

-0.2

5*0.

08

16.

TSS

——

——

——

——

——

——

——

—1.

000.

58**

-0.2

6*-0

.11

0.17

17.

Ear

/pla

nt—

——

——

——

——

——

——

——

—1.

00-0

.38*

-0.0

50.

20*

18.

Bio

logi

cal y

ield

——

——

——

——

——

——

——

——

—1.

00-0

.18*

0.07

19.

HI

——

——

——

——

——

——

——

——

——

1.00

-0.1

7*

20.

Gra

in p

rote

in c

onte

nt—

——

——

——

——

——

——

——

——

——

1.00

*Sig

nifi

cant

at

5 an

d **

Sig

nifi

cant

at

1 pe

r ce

nt p

roba

bili

ty l

evel

, re

spec

tive

ly


Table 5. Polymorphism information of RAPD primers analyzed

S.No. Primers code Sequence 5’ -3’ Base pair Total no. of Total no. of Total No. of Polymorphism %bands (a) Monomorphic Polyomorphic (b/a × 100)

bands bands(b)

1. OPA-05 AGGGGTCTTG 2500-200 6 1 5 83.33

2. OPB-03 CATCCCCCTG 2500-200 6 1 5 83.33

3. OPB-07 GGTGACGCAG 3000-200 10 2 8 80.00

4. OPB-06 TGCTCTGCCC 2500-200 11 1 10 90.90

5. OPB-02 TGATCCCTGG 3000-300 6 1 5 83.33

6. OPA-03 AGTCAGCCAC 2500-200 9 1 8 88.90

7. OPB-01 GTTTCGCTCC 1500-200 9 1 8 88.90

8. OPD-02 GGACCCAACC 1500-100 8 1 7 87.5

9. OPB-05 TGCGCCCTTC 1200-200 5 2 3 60

10. C-19 GTTGCCAGCG 3000-200 12 2 10 83.33

11. OPA-09 GGGTAACGCC NA NA NA NA NA

12. OPA-06 GGTCCCTGAC NA NA NA NA NA

13. OPA-10 GTGATCGCAG NA NA NA NA NA

14. OPA-08 GTGACGTAGG NA NA NA NA NA

15. OPA-07 GAAACGGGTG NA NA NA NA NA

Total 82 13 69

Average 8.2 1.3 6.9 82.95

Plate 1. RAPD profile of speciality corn genotypes generated with primers OPB-06 and OPB-02

RAPD profile generated thought primer OPB-06 (5’-TGCTCTGCCC-3’) RAPD profile generated thought primer OPB-02 (5’-TGATCCCTGG-3’)

Plate 2. RAPD profile of speciality corn genotypes generated with primers OPA-03 and OPB-01

RAPD profile generated thought primer OPA-03 (5’-AGTCAGCCAC-3’) RAPD profile generated thought primer OPB-01 (5’-GTTTCGCTCC-3’)


RAPD profile generated thought primer OPD-02 (5’-GGACCCAACC-3’) RAPD profile generated thought primer OPB-05 (5’-TGCGCCCTTC-3’)

Speciality corn genotypes G1-G20 are labelled on the photo plateG1: HQPM-1; G2: HQPM-5; G3: HQPM-4; G4: HQPM-7; G5: PQMH-1; G6: VQPM-9; G7: VEHQ-11-1; G8: VEHQ 14-1; G9: MMHQPM6-12-13; G10: Sugar 75; G11: Misthi; G12: Sugar Candy; G13: Madhuri; G14: Win Orange; G15: Priya; G16: Bajaura SC; G17: Amber PopCorn; G18: VLAmber Pop Corn; G19: Bajaura Pop Corn; G20: HM-4

Plate 3. RAPD profile of speciality corn genotypes generated with primers OPD-02 and OPB-05

Fig. 1. Dendogram generated for speciality corn genotypes using UPGMA Cluster based on Jaccard Similarity Coefficient (RAPD analysis)

genotypes (Amber pop corn, Bajaura pop corn and V.L.Amber pop corn).

The result showed probable association betweendendrogram obtained by RAPD analysis and morphologicalcharacters. Except HQPM-7 and HQPM-1 all the remainingseven genotypes of QPM corn were genetically diverse asevident from morphological and molecular studies.Amongst sweet corn varieties Misthi, Sugar-75, Bajaurasweet corn, Madhuri and Priya were also distinct atmorphological as well as molecular level with respect toone or more characters as also evident from their bandpatterns. All the three pop corn genotypes were also

different with respect to descriptors and band patterns.The maximum similarity value of 0.88 was observed inSugar 75 and Sugar candy. While amongst 20 specialitycorn genotypes maximum similarity coefficient wasrecorded between sweet corn and baby corn genotypes(Bajaura sweet corn and HM-4). Molecular characterizationthrough RAPD analysis revealed high polymorphism,marked genetic diversity and similarity among the specialitycorn genotypes which is reflected by their morphologicaldifferences. Hence molecular characterization appeared tobe precise, quick and effective tool in analyzing geneticdiversity, similarity and identification of germplasm.


References

Doyle, J. J., & Doyle, J. L. (1990). A rapid total DNA preparationprocedure for fresh plant tissue. Focus, 12: 13-15.

Dreisigacker, S. P., Zhang, M. L., Warburton, B., Skormand, D.,Hoisington, A. E., & Melchinger, R. (2005). Genetic diversityamong and within CIMMYT wheat landrace accessionsinvestigated with SSRs and implications for plant geneticresources management. Crop Sci., 45: 653-661.

Mohammadi, S. A., & Prasanna, B. M. (2003). Analysis of geneticdiversity in crop plants-salient statistical tools andconsiderations. Crop Sci., 43: 1235-1248.

Sharma, R. (2014). Assessment of Morphological, Biochemical andMolecular Diversity in Zea mays L. Ph.D. Thesis, Departmentof MBBT, MPUAT, Udaipur.

Sumathi, P., Nirmalakumari, A. & Mohanraj, K. (2005). Geneticvariability and traits interrelationship studies in industriallyutilized oil rich CIMMYT lines of maize (Zea mays L). J.Madras Agric., 92(10): 612-617.

Vashistha, A., Dixit, N. N., Dipika., Sharma, S.K., & Marker, M.(2013). Studies on heritability and genetic advance estimates inmaize genotypes. Bio. Sci. Dis., 4(2):165-168.


RESEARCH PAPER

J. M. Patel: [email protected]

1Department of Genetics and Plant Breeding, C.P. College ofAgriculture, S.D. Agricultural University, Sardarkrushinagar–385506

2Wheat Research Station, SDAU Vijapur

3Cotton Research Station, SDAU, Talod

4College of Agriculture, SDAU, Tharad

5College of Horticulture, SDAU, Jagudan

Received: 13 November 2016/ Accepted: 07 February 2017© Maize Technologists Association of India 2017

Character association analysis of yield, kernel component and qualitativetraits in Maize (Zea mays L.)

Nilesh Patel1 · J. M. Patel2 · J. A. Patel3 · L. D. Parmar4 · D. M. Thakor5

Abstract: An investigation was carried out on correlationanalysis for eleven morphological, one physiological andfour qualitative traits on forty six genotypes of maize.Positive and significant correlation was observed for grainyield per plant with number of kernels per row followedby ear diameter (mm), plant height (cm), ear height (cm),leaf area (cm2), Fe content (ppm), ear length (cm) andnumber of kernel rows per ear. Positive behavior of thesecharacters towards grain yield indicated that, these traitscan play a crucial role for the improvement of inbred linesas well as for the development of superior hybrids in maize.

Keywords: Correlation, Inbred, Maize

Introduction

Maize (Zea mays L.) is one of the most important staplefood crops in the world and it ranks first in productionfollowed by wheat and rice. Maize is one of the oldestcultivated crops. It has dual uses as food for human beingas well as feed for animals. It is also equally important forindustries uses. Probably, there is no any cereal crop onthe earth, which has such immense potential as maize andtherefore it occupies the unique place as “Queen of cereals”.At present grain yield of maize in India is much lower thanworld average. It is mainly due to cultivation of localcultivars in >30% of maize area of the country, nonavailability of good quality seed of varieties/hybrids andlosses due to biotic and abiotic stresses. Grain yield is acomplex quantitative trait that depends on genetic potentialand its interaction with environmental conditions..Micronutrient malnutrition is major concern globally, butmost prevalent in the developing countries where themajority of the population relies on staple foods like wheat,rice and maize. These staples are deficient in micronutrientsuch as iron, zinc and vitamin A. Iron (Fe) deficiency isthe most common nutritional deficiency, which lead toanemia affecting 4 to 5 billion people worldwide, whereas,zinc (Zn) deficiency lead to anorexia, depression, psychosis,impaired growth and development, altered reproductivebiology, gastrointestinal problems and impaired immunity .

Correlation analyses are used to understand theassociation between different traits. Since yield is a complexcharacter understanding magnitude of character associationbetween simple yield contributing characters, with yieldwould be immense help in the direct selection for theimprovement of yield.



An investigation was carried out at maize research station,Sardarkrushinagar Dantiwada Agricultural University,Bhiloda in which forty six inbred lines of maize (Table 1)were evaluated in randomized block design (RBD) replicatedthrice with a row length of 4m and spacing of 60cmbetween the rows and 20 cm between plants during Kharif2016. Recommended agronomic management practiceswere followed during crop growth period to raise a healthycrop. The data were recorded on eleven morphological,one physiological and four qualitative characters viz., daysto 50 % tasselling, days to 50 % silking, days to 75 %husking, plant height (cm), ear height (cm), ear length (cm),ear diameter (mm), number of kernel per rows per ear,number of kernel rows per ear, grain weight per plant(g),100 grain weight (g), leaf area (cm2), starch content(%), protein content (%), Zn content (ppm), and Fe content(ppm). Data recorded on mean of five randomly selectedplants were used for statistical analysis whereas floweringand maturity traits were recorded on plot base. The estimateof covariance was worked out as per Singh and Chaudhary(1985). The genotypic and phenotypic correlations wereworked out as per procedure suggested by Hazel (1943).

Covg (XY)Genotypic correlation r

g = —————————

√σ²g (X) σ²

g (Y)

Covp (XY)Phenotypic correlation r

p = —————————

√σ²p (X) σ²

p (Y)

Where, rg = Genotypic correlation

rp = Phenotypic correlation

Covg (XY) = Genotypic covariance between character Xand Y.

Covp (XY) = Phenotypic covariance between charactersX and Y.

σ²g (X) = Genotypic variance of character X.

σ²g (Y) = Genotypic variance of character Y.

σ²p (X) = Phenotypic variance of character X.

σ²p (Y) = Phenotypic variance of character Y.

Estimates of correlation coefficients were comparedagainst r-values given in Fisher and Yates (1943) table at(n-2) df at the probability levels of 0.05 and 0.01 to testtheir significance.

Result and discussion

In the present study, genotypic and phenotypic correlationamong the sixteen characters of maize inbreds wasevaluated (Table 2). In general, genotypic correlation washigher than phenotypic correlation indicating a lowinfluence of environmental factors.

Days to 50 % silking showed positive and highlysignificant correlations with days to 75 % brown husk(0.545) number of kernels per row (0.2949) and ironcontent (0.260) at both genotypic and phenotypic levels,while leaf area (cm2) (-0.288) and ear length (-0.480)showed negative and highly significant correlation. Highlysignificant and positive correlation was observed for plantheight with ear height (0.854) followed by ear diameter

Table 1. List of maize genotypes for experiment

S. No. Genotype S. No Genotype S. No Genotype S. No Genotype

1. GYL- 1 13. WNC- 18005 25. VL109178 37. BLD- 74

2. GYL- 5 14. WNC- 32177 26. VL109180 38. BLD- 42

3. GYL- 7 15. WNC- 18115 27. Z489-157 39. BLD-76

4. BLD-2 16. WNC- 18354 28. BLD- 232 40. BLD- 208

5. IC- 070 17. WNC- 18242 29. BLD- 203 41. BLD-16

6. BLD-30 18. WNC- 32862 30. BLD- 188 42. BLD- 128

7. I-07-5-8 19. WNC- 31857 31. BLD- 297 43. BLD- 131

8. WNC- 32160 20. WNC- 31708 32. BLD- 276 44. BLD- 100

9. WNC- 31734 21. HY10RN-10235-235 33. BLD- 46 45. BLD-223

10. WNC- 32255 22. CML-15 34. BLD- 173 46. BLD- 94

11. WNC- 32867 23. HY10RN- 10235-269 35. BLD- 265

12. WNC- 32863 24. VL1032-1 36. BLD- 51


Tabl

e 2.

Gen

otyp

ic a

nd p

heno

typi

c co

rrel

atio

n co

effi

cien

ts o

f m

aize

yie

ld w

ith

vari

ous

grow

th a

nd q

uali

ty c

ompo

nent

s

Cha

ract

ers

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X12

X13

X14

X15

X16

X11

X1

rg1

0.99

98**

-0.0

329

-0.0

385

-0.3

86-0

.119

-0.4

772*

*0.

3243

**-0

.477

2**

0.60

03**

0.14

450.

2566

**0.

0266

-0.0

388

0.31

66**

0.02

64

rp1

0.92

38**

-0.0

279

-0.0

48-0

.118

2-0

.053

-0.1

214

0.22

40**

-0.1

214

0.54

48**

0.01

550.

1179

0.03

67-0

.000

70.

1862

*-0

.020

8

X2

rg1

-0.0

671

-0.0

806

-0.2

883*

*-0

.211

7*-0

.480

3**

0.29

49**

-0.4

803*

*0.

5451

**0.

0917

0.13

09-0

.027

8-0

.040

90.

2601

**-0

.024

3

rp1

-0.0

468

-0.0

648

-0.1

118

-0.0

572

-0.1

464

0.22

43**

-0.1

464

0.57

20**

0.01

040.

0991

0.00

12-0

.020

50.

1752

*-0

.062

5

X3

rg1

0.85

44**

0.36

98**

0.66

78**

0.50

44**

0.39

47**

0.50

44**

0.04

98-0

.012

-0.0

633

0.09

180.

3123

**0.

1610

0.51

98**

rp1

0.83

81**

0.11

610.

4100

**0.

3282

**0.

3340

**0.

3282

**0.

0242

-0.0

026

-0.0

263

0.06

690.

2759

**0.

1313

0.44

74**

X4

rg1

0.36

13**

0.62

09**

0.54

44**

0.37

46**

0.54

44**

0.05

63-0

.112

2-0

.026

7-0

.058

0.32

08**

0.25

08**

0.47

09**

rp1

0.16

650.

4176

** 0

.361

3**

0.31

00**

0.36

13**

0.04

39-0

.067

40.

0133

-0.0

561

0.27

72**

0.20

29*

0.40

85**

X5

rg1

0.79

1**

0.39

87**

0.27

52**

0.39

87**

-0.0

365

0.22

29*

0.11

820.

0741

0.45

89**

0.37

79**

0.53

7**

rp1

0.26

18**

0.2

137

*0.

1301

0.21

37*

0.06

160.

0585

0.04

370.

0332

0.18

01*

0.17

40*

0.30

06**

X6

rg1

0.60

9**

0.78

85**

0.60

9**

-0.2

174*

0.32

82**

0.02

810.

0376

0.28

12**

0.47

65**

0.85

72**

rp1

0.59

94**

0.38

08**

0.59

94**

-0.0

616

0.13

240.

0673

0.02

370.

1524

0.24

50**

0.46

38**

X7

rg1

0.24

23**

1.00

0**

-0.3

905*

*0.

3096

**-0

.085

8-0

.078

20.

0953

0.28

82**

0.46

26**

rp1

0.12

751.

000*

*-0

.168

8*0.

1685

*-0

.031

7-0

.028

80.

0711

0.16

330.

2816

**

X8

rg1

0.24

23**

0.16

620.

2062

*0.

0977

0.02

38-0

.035

00.

2714

**0.

7314

**

rp1

0.12

750.

1447

0.15

930.

0733

0.02

71-0

.031

20.

2501

**0.

6653

**

X9

rg1

-0.3

905*

*0.

3096

**-0

.085

8-0

.078

20.

0953

0.28

82**

0.46

26**

rp1

-0.1

688

*0.

1685

*-0

.031

7-0

.028

80.

0711

0.16

330.

2816

**

X10

rg1

0.26

01**

0.20

42*

0.01

620.

1450

0.24

09**

0.05

77

rp1

0.16

60.

1791

*0.

0154

0.11

330.

1749

*0.

0348

X12

rg1

0.03

04-0

.075

30.

1945

*0.

1882

*0.

2422

**

rp1

0.02

89-0

.073

20.

1729

*0.

1763

*0.

2186

*

X13

rg1

0.29

15**

-0.1

802*

0.34

08**

0.20

28*

rp1

0.27

89**

-0.1

690*

0.32

50**

0.17

89*

X14

rg1

-0.0

921

-0.2

035*

0.00

31

rp1

-0.0

93-0

.201

3*0.

0047

X15

rg1

0.33

19**

0.10

71

rp1

0.31

87**

0.10

58

X16

rg1

0.29

31**

rp1

0.28

52**

X1:

Day

s to

50%

Tas

seli

ng; X

2: D

ays

to 5

0% s

ilki

ng; X

3: P

lant

hei

ght (

cm);

X4:

Ear

hei

ght (

cm);

X5:

Lea

f are

a (c

m)2 ;

X6:

Ear

dia

met

er (m

m);

X7:

Ear

leng

th s

(cm

); X

8: N

umbe

r of k

erne

lspe

r row

; X9:

Num

ber o

f ker

nel r

ows

per e

ar; X

10: D

ays

to 7

5% h

uski

ng; X

11: G

rain

wei

ght p

er p

lant

(g);

X12

: 100

gra

in w

eigh

t (g)

; X13

: Sta

rch

cont

ent (

%);

X14

: Pro

tein

con

tent

(%);

X15

:Z

n co

nten

t (p

pm);

X16

: F

e co

nten

t (p

pm)


(0.667), grain weight per plant (=0.5198), ear length (0.504)number of kernel rows per ear (0.504) number of kernelsper row (0.394) and zinc content (ppm) (0.312).

Ear height (cm) showed highly significant and positivecorrelation with grain weight per plant (g) (r

g = 0.470),

ear diameter (mm) (rg = 0.620), ear length (cm) (r

g =

0.544), number of kernels per row (rg = 0.374), number

of kernel rows per ear (rg = 0.544), zinc content (ppm) (r

g

= 0.320). Leaf area (cm2) showed positive and highlysignificant correlation with ear diameter (mm) (r

g= 0.791)

followed by grain weight per plant (rg= 0.537), zinc content

(ppm) (rg= 0.4589 ear length (cm) (r

g= 0.3987), while with

number of kernel rows per ear (rg= 0.3987), and iron

content (ppm) (rg= 0.3789), In this study the magnitude

of genotypic correlation was higher than phenotypiccorrelation.

Ear diameter (mm) displayed highly significant andpositive correlation with number of kernels per row(r

g=0.788) followed by ear length (cm) (r

g=0.609), number

of kernel rows per ear (rg=0.609), 100 grain weight

(rg=0.322) and zinc content (ppm) (r

g=0.281) while with

days to 75 % brown husk (rg=-0.217) showed negative

significant correlation at genotype level. Ear length (cm)exhibited positive and highly significant correlation withgrain weight per plant (g) (r

g=0.462), 100 grain weight (g)

(rg=0.309) iron content (ppm) (r

g=0.288), number of

kernels per row (rg=0.242), whereas, negative and highly

significant correlation was found with days to 75 % brownhusk (r

g=-0.390).

Number of kernels per row exhibited highly significantpositive correlation with grain weight per plant (g)(r

g=0.7314 and r

p=0.6653) and iron content (ppm)

(rg=0.2714 and r

p=0.2501) at both genotypic and

phenotypic levels. Number of kernel rows per ear is themost important economic character which showed highlysignificant and positive correlation with grain weight perplant (g) (r

g=0.462), 100 grain weight (r

g=0.309), iron

content (ppm) (rg=0.2882) while it showed negative and

highly significant correlation with days to 75 % brownhusk (r

g=-0.390). Days to 75 % brown husk showed highly

significant and positive correlation with iron content (ppm)(r

g=0.2409 at genotypic level only, whereas, with starch

content (%) (rg=0.2042 and r

p=0.1791) and iron content

(ppm) (rp=0.1749) it showed positive and significant

correlation. 100 grain weight (g) showed highly significantand positive correlation with grain weight per plant (g)(r

g=0.242), zinc content (ppm) (r

g=0.194) and iron content

(ppm) (rg=0.188).

Starch content (%) manifested positive and highlysignificant correlation with iron content (ppm) (r

g=0.3408)

and protein content (rg=0.291) whereas, negative and

significant correlation was observed with zinc content (rg=-

0.180). Protein content (%) showed significant andnegative correlation with iron content (ppm) (r

g=-0.2035

and rp=-0.2013) at both genotypic and phenotypic levels,

and traits like zinc content (ppm) and grain weight perplant (g) had non-significant correlation with this character.Zinc content (ppm) showed highly significant and positivecorrelation with iron content (ppm) (r

g=0.331). Zinc content

(ppm) was non significantly correlated with trait like grainweight per plant (g). Iron content (ppm) exhibited highlysignificant and positive correlation with grain weight perplant (g) (r

g=0.2931 and r

p=0.2852) at both genotypic and

phenotypic levels.Grain yield per plant an important commercial trait,

depicted highly significant positive correlation with eardiameter (mm) (r

g=0.8572 number of kernels per row

(rg=0.731) leaf area (cm2) (r

g=0.537, plant height (cm)

(rg=0.519), ear height (cm) (r

g= 0.470), ear length (cm)

(rg=0.462), number of kernel rows per ear (r

g=0.462) and

iron content (ppm) (rg=0.293) whereas, non significant

correlation was found with the traits like days to 50 %tasseling, days to 50 % silking, days to 75 % brown husk,protein content (ppm) and zinc content (ppm).

Similar results were reported earlier in maize by severalworkers on different characters viz., for the associationof grain yield with ear length (Selvaraj and Nagarajan, 2011),Dixit et al. (2014) for kernel per row, ear diameter, earlength, plant height, ear height, 100 grain weight, numberof kernels row per ear. Knife et al. (2015) found highsignificant favorable relationship of grain weight per plantwith ear diameter, ear length, plant and ear height. Sharmaet al. (2014) found highly significant and positivecorrelation of grain yield per plant with plant height, earheight, ear diameter, ear length, 100 grain weight, kernelrow per ear and kernel per row. Synrem et al. (2016) foundthat grain weight per plant was significantly correlated with100 grain weight. The results indicated that ear diameter,number of kernel rows per ear and number of kernels perrow are highly correlated with grain yield per plant andneed to be considered for selection.

Conclusion

Thus, the investigation of the study indicated that eardiameter per plant (mm), number of kernel per row, leafarea (cm2), plant height (cm) and number of kernel row


per ear showed positive and significant association withgrain weight per plant. However positive behavior of thesecharacters towards grain yield indicated that, these traitscan play a crucial role for the improvement of inbred linesas well as for the development of superior hybrids in maize.

References

Chakraborti, M., Hossain, F., Kumar, R., Gupta, H. S., & Prasanna,B. M. (2009). genetic evaluation of grain yield and kernelmicronutrient traits in Maize. Pusa Agriculture Science, 32: 11-16.

Dixit, A. K., Singh, A., Shahi, J. P., Kumar, P., & Singh, R. K. (2014).Characters association and path coefficient studies in Maize(Zea mays L.) under rabi conditions. Trends in Biosciences,7(17): 2479-2481.

Fisher, R. A., & Yates, F. (1943). Statistical tables for biologicalagricultural and medical research. Oliver and Boyd, Edinburg,6th ed. pp. 63.

Hazel, L. (1943). The genetic bases for constructions selection ofindices. Genetics, 28: 476-490.

Kinfe, H., Alemayehu, G., Wolde, L., & Tsehaye, Y. (2015).Correlation and path coefficient analysis of grain yield and yieldrelated traits in Maize (Zea mays L.) hybrids, at Bako, Ethiopia.Journal of Biology, Agriculture and Healthcare, 5(15): 2224-3208.

Selvaraj, I. C., & Nagarajan, P. (2011). Interrelationship and path-coefficient studies for qualitative traits, grain yield and otheryield attributes among Maize (Zea mays L.). InternationalJournal of Plant Breeding and Genetic, 5: 209-223.

Sharma, T.; Kumar, A.; Dwivedi, S. C., & Vyas, R. P. (2014).Estimates of genetic factors and correlation analysis in maize(Zea Mays L.). Plant Archives, 14(1): 19-21.

Singh, S.P., & Choudhary, B. D. (1985). Biometrical methods inquantitative genetic analysis. Kalyani Publishers, New Delhi.pp. 57.

Synrem, G. J., Marker, S., Bhusal, T., & Kumar, L. N. (2016).Correlation and path coefficient analysis in maize genotypes.Annals of Plant and Soil Research, 18(3): 232-240.


RESEARCH PAPER

K.H. Patel: [email protected]

Main Maize Research Station, Anand Agricultural University, Godhra,Panchmahals-389001, Gujarat, India

Received: 05 October 2016/ Accepted: 12 May 2017© Maize Technologists Association of India 2017

Performance of single cross hybrid maize at varying levels of nitrogen andphosphorus under rainfed conditions of Middle Gujarat Plains

K. H. Patel · P. K. Parmar · M. B. Patel · S. K. Singh · D. M. Rathod · B. N. Thakker

Abstract: A field experiment was conducted to study theperformance of newly released single cross hybrids atvarying level of nitrogen and phosphorus under rainfedcondition at three different locations of Middle Gujarat. AtGodhra, Devgadhbaria and Dahod Centre, treatment ofnitrogen 160 kg N/ha gave significantly higher grain yieldi.e. 5607 kg/ha and 4496 kg/ha, respectively compared toother treatments. The effect of phosphorus has nosignificant effect on yield at Godhra but at Dahod andDevgadhbaria, application of phosphorus @ 60 kg P

2O

5/ha

gave significantly higher grain yield. White maize hybridGAWMH-2 gave significantly higher grain yield (5231 kg/ha) at Godhra location and yellow hybrid GAYMH-1 gavesignificantly higher grain yield (4028 kg/ha) at Dahod.

Keywords: Economics · Grain and stover yield · Maizehybrids

Introduction

Maize is one of the most important and highly evolvedcoarse cereals, which is widely cultivated around the world.In India, it is an important crop not only in terms of acreagebut also in context to its versatility for adoption under widerange of agro-climatic conditions. Identification of popularmaize genotype having wider adaptability andresponsiveness to input is considered first step fordevelopment of production technology (Parihar et al., 2011;Yadav et al., 2015). Hence, there is need to test earlymaturing hybrids under prevailing rainfed agro-climatic ofcondition of middle Gujarat. Further, grain and fodder yieldpotentials of the released hybrids can realized fully whenthey are grown under adequate fertilization (Dass et al.,2012; Jat et al., 2013). Research information on theresponse of maize hybrids to nutrient application is limitedbut hybrid maize responded to good nutrient application(Jat et al., 2013a). Hence, the growers are applying thesame rate of fertilizers recommended for hybrid corn.Therefore, there is need to work out optimum combinationof nitrogen and phosphorus fertilization for early maturinghybrids. Considering these facts and paucity of researchfindings on these aspects in middle Gujarat, the study entitled“Performance of single cross hybrid maize in varying levelsof nitrogen and phosphorus under rainfed condition” wascarried out.


The field experiments were conducted during Kharif 2015to Kharif 2016 at three different locations of middle Gujarati.e. Godhra, Dahod and Devagahbaria (D’Baria). Twonewly released hybrids were tested against different threedoses of nitrogen i.e. at 80, 120 and 160 kg/ha accompaniedwith 20 kg P

2O

5/ha, 40 kg P

2O

5/ha and 60 kg P

2O

5/ha. The

experiment was laid out in factorial randomized block design(RBD) by keeping gross size of 4.5 m x 6.0 m and net plotsize of 3.0 x 5.6 m in three replications. The texture of soilwas sandy loam at Godhra and medium black type at Dahodand D’Baria. The phosphorus was applied as basal whilenitrogen was applied in four equal splits at basal, 4-leafstage, 8-leaf stage and tasseling stage. Yield is a complexquantitative trait and greatly influenced by externalenvironment, which results in scale or rank shift in itsperformance (Dia et al., 2016). This relative shift ofgenotype performance from one environment (location ×year combination) to another is known as genotype ×environment interaction (G×E) (Dia et al., 2012; 2012a).The two-year data of three locations for growthparameters, yield attributes and yields were analyzed todecipher the effect of the interaction of genotype withlocations, management and years.

Results and Discussion

Growth & yield attributes

The nitrogen treatment effects were found significant atGodhra location only for test weight (Table 1 and 2). AtGodhra, treatment of 160 kg N/ha gave significantly highertest weight (263 g) but it was at par with 120 kg N/ha.The ear length, ear girth and grain rows/ear was notsignificantly affected by the application of nitrogen. Thetreatment effects were found significant at D’Baria whilenon-significant for test weight at Godhra and Dahod. AtD’Baria, 60 kg P

2O

5/ha gave higher test weight (246 cm).

While at Godhra, treatment of 60 kg P2O

5/ha gave 260 g

taste weight and at Dahod, 40 kg P2O

5/ha gave higher test

weight (287 g).The non-significant effect on test weight could be

attributed to more genetics link character compared toinfluence by crop management practices. The ear length,ear girth and grain rows/ear was not affected significantlyby the phosphorus application. The hybrid effects werefound non-significant on test weight and ear length butshowed significant increase in ear girth in GAYMH-1,yellow hybrid at Godhra location compared to others.Kumar et al. (2014) also reported that better nutrition hadsignificant effect on the growth and yield attributed ofvarious maize hybrids.

Effect on yields

The treatment effects of nitrogen were found significantdifference at the location of Godhra and Dahod and non-

significant at D’Baria. At both the center Godhra and Dahodtreatment of 160 kg N/ha gave significantly higher grainyield 5607 kg/ha and 4496 kg/ha respectively (Table 3).Similarly, the nitrogen treatment effects were foundsignificant at Godhra and Dahod while non-significant atD’Baria for stover yield. At Godhra and Dahod treatmentof nitrogen i.e.160 kg N/ha gave significantly higher stoveryield of 5656 and 3689 kg/ha, respectively. The nutrientapplication lead to higher growth that in turn increased thebiomass and better source sink relationship for obtaininghigher yields of the maize. The similar results of betternutrition linked increased yield in maize hybrids were notedby Kumar et al. (2014).

The phosphorus effects were found non-significant atGodhra while significant at Dahod and D’Baria for grainyield. At Dahod and D’Baria treatment of 60 kg P

2O

5/ha

gave significantly higher grain yield 4236 and 4829 kg/ha,respectively. While at Godhra found non-significantdifference among various treatments was observed for Papplication in maize. However, treatment of 40 kg P

2O

5/ha

gave higher grain yield. For stover yield, the phosphorustreatment effects were found significant at D’Baria. AtD’Bariathe phosphorus effect of 60 kg P

2O

5/ha gave higher

stover yield of 6348 kg/ha. Kumar et al. (2002) alsoreported the better nutrition lead enhancement in maizeyields under rainfed conditions.

At Godhra and Dahod the yield response by hybrid wasfound significant while non-significant at D’Baria. AtGodhra, white maize hybrid GAWMH-2 gave significantlyhigher grain yield of 5231 kg/ha while at Dahod yellowmaize hybrid GAYMH-1 gave significantly higher grain yield(4028 kg/ha). The results are in close conformity with thefindings of Jat et al. (2012). Similarly, for stover yield theeffect of hybrid found significant at D’Baria. The yellowhybrid GAYMH-1 gave higher stover yield (6008 kg/ha)than white hybrid GAWMH-2 (5689 kg/ha). The markedvariation in growth, yield and quality parameters of hybridscould be ascribed to their genetic capabilities to exploitavailable resources for their growth and development(Kumar et al., 2014).

Effect on economics

Economics of different treatments presented in Table 4indicated that at Godhra application of 160 kg N + 20 kgP

2O

5 gave maximum net realization with more benefit cost

ratio (BCR) i.e. Rs. 84414/ha with 4.29 BCR for yellowmaize hybrid (GAYMH-1) while same treatment gave Rs.91030/ha with 4.55 BCR for GAWMH-2 (Table 4). While



Table 1. Effect of nitrogen and phosphorus application on growth parameters of newly released maize hybrids in kharif season at variouslocations of Middle Gujarat Plains (pooled means of two years)

Treatment Final plant stand Plant height (cm) Ear length (cm)

Godhra Dahod D’Baria Godhra Dahod D’Baria Godhra Dahod D’Baria

N application (kg/ha)

80 96 98 98 171.1 159 163 15.7 15.4 -

120 99 102 101 183.1 177 177 17.6 15.9 -

160 98 100 108 196.4 189 179 17.9 16.4 -

SEm ± 1.8 1.5 6.1 8.4 7.2 8.9 0.52 0.37 -

CD(P=0.05) NS NS NS NS NS NS NS NS -

P2O

5 application (kg/ha)

20 99 99 98 181.9 170 167 16.9 15.8 -

40 97 100 102 183.5 175 172 17.2 16.0 -

60 97 100 107 185.1 180 181 17.1 16.0 -

SEm ± 1.8 1.5 3.4 2.2 4.9 7.5 0.3 0.15 -

CD (P=0.05) NS NS NS NS NS NS NS NS -

N X P

SEm ± 4.3 2.5 2.7 3.9 3.1 2.9 0.72 0.27 -


Maize hybrids

GAYMH-1 98 100 104 184.0 176 173 17.8 16.5 -

GAWMH-2 97 100 101 183.0 175 173 16.3 15.4 -

SEm ± 3.8 4.9 1.2 1.8 1.4 0.84 1.3 0.77 -


N X V

SEm ± 2.5 2.1 2.2 3.2 2.5 1.4 0.79 0.22 -


P X V

SEm ± 2.5 2.1 2.2 3.2 2.5 1.4 0.21 0.54 -


N X V X P

SEm ± 4.4 3.6 3.8 5.6 4.3 2.5 0.62 0.38 -


Y X N

SEm ± 2.5 2.1 2.2 3.2 2.5 1.4 0.21 0.22 -

CD (P=0.05) NS NS 6.3* 9.2* 7.1* 4.1* 0.61* 0.63* -

Y X P

SEm ± 2.5 2.1 2.2 3.2 2.5 1.4 0.21 0.22 -

CD (P=0.05) NS NS 6.3 NS 7.1* 4.1* 0.61* NS -

Y X V

SEm ± 2.1 1.7 1.8 2.6 2.0 1.8 0.17 0.18 -

CD (P=0.05) 5.9 4.8 NS NS NS NS 0.49* 0.51* -

Y X N X P X V

SEm ± 2.4 2.5 5.4 7.9 6.2 3.5 0.52 0.55 -

CD (P=0.05) 6.3 NS NS NS NS NS 1.48* NS -


Table 2. Effect of nitrogen and phosphorus application on yield attributes of newly released maize hybrids in kharif season at variouslocations of Middle Gujarat Plains (pooled means of two years)

Treatment Ear girth (cm) Test weight (g) Grain rows/cob

Godhra Dahod D’Baria Godhra Dahod D’Baria Godhra Dahod D’Baria


80 12.5 12.9 - 247 272 218 12.7 12.7 -

120 13 13.3 - 263 283 235 12.9 12.7 -

160 13.4 13.4 - 263* 297 249 14.1 12.8 -

SEm ± 0.40 0.22 - 3.1 4.0 3.9 0.68 0.17 -

CD (P=0.05) NS NS - 9.0 NS NS NS NS -

P2O


20 12.8 13.1 - 257 281 226 13.2 12.7 -

40 13.0 13.3 - 255 287 231 13.2 12.7 -

60 13.1 13.3 - 260 283 246* 13.2 12.9 -

SEm ± 0.10 0.08 - 8.8 1.7 1.1 0.15 0.17 -

CD (P=0.05) NS NS - NS NS 3.1 NS NS -

N X P

SEm ± 0.17 0.13 - 12.8 3.0 1.9 0.44 0.29 -

CD (P=0.05) 0.50 NS - NS 5.5* NS NS -

Maize hybrids

GAYMH-1 13.2 13.4 - 254 288 235 13.6 13.1 -

GAWMH-2 12.8 13.1 - 261 280 234 13.0 12.4 -

SEm ± 0.08 0.23 - 6.5 4.9 0.92 0.41 0.13 -

CD (P=0.05) 0.24 NS - NS NS NS NS 0.39* -

N X V

SEm ± 0.29 0.10 - 4.5 2.5 1.5 0.61 0.24 -

CD (P=0.05) NS NS - NS NS NS NS NS -

P X V

SEm ± 0.14 0.10 - 4.5 2.5 1.5 0.21 0.24 -

CD (P=0.05) NS NS - NS NS NS 0.61 NS -

N X V X P

SEm ± 0.25 0.18 - 7.8 4.2 2.7 0.38 0.41 -

CD (P=0.05) 0.71 NS - 22.1* NS NS 1.07 NS -

Y X N

SEm ± 0.14 0.10 - 4.5 2.4 1.5 0.21 0.24 -

CD (P=0.05) 0.41* 0.29* - NS 6.9* 4.5* 0.62* NS -

Y X P

SEm ± 0.14 0.10 - 4.5 2.4 1.5 0.21 0.24 -

CD (P=0.05) NS NS - 12.8 NS NS NS NS -

Y X V

SEm ± 0.12 0.08 - 3.7 2.0 1.3 0.17* 0.19 -

CD (P=0.05) NS 0.24* - 10.4 5.7 NS 0.51 NS -

Y X N X P X V

SEm ± 0.35 0.25 - 11.1 6.0 3.9 0.53 0.58 -

CD (P=0.05) NS NS - NS NS NS NS NS -


Table 3. Effect of nitrogen and phosphorus application on grain and stover yields of newly released maize hybrids in kharif season at variouslocations of Middle Gujarat Plains (pooled means of two years)

Treatments Grain yield (kg/ha) Stover yield (kg/ha)

Godhra Dahod D’Baria Godhra Dahod D’Baria


80 4664 3378 3437 5120 3297 4945

120 4704 3763 4221 4937 3613 5887

160 5607* 4496* 5130 5656* 3689* 6714

SEm ± 149 95 338 195 77 859

CD (P=0.05) 422 269 NS 552 219 NS

P2O


20 4968 3636 3741 5003 3431 5558

40 5050 3765 4217 5504 3519 5639

60 4957 4236* 4829* 5207 3649 6348*

SEm ± 149 95 91 195 77 55

CD (P=0.05) NS 269 257 NS NS 159

N X P

SEm ± 258 165 157 338 134 78

CD (P=0.05) NS Ns 455* 957 NS NS

Maize hybrids

GAYMH-1 4752 4028 4341 5330 3617 6008*

GAWMH-2 5231* 3730 4184 5146 3449 5689

SEm ± 122 78 74 159 63.4 55

CD (P=0.05) 345 220 NS NS NS 159

N X V

SEm ± 211 135 128 276 109 78

CD (P=0.05) NS NS NS NS NS NS

P X V

SEm ± 211 135 128 276 109 78


N X V X P

SEm ± 366 234 223 479 190 110


Y X N

SEm ± 211 135 128 276 109 78

CD (P=0.05) NS NS 363* NS NS 225*

Y X P

SEm ± 211 135 128 276 109 78


Y X V

SEm ± 172 110 105 225 89 78


Y X N X P X V

SEm ± 517 330 315 677 269 156



Table 4. Effect of nitrogen and phosphorus application on economics of newly released maize hybrids in kharif season at various locationsof Middle Gujarat Plains (mean of two years)

Treatment* Net income (Rs/ha) Benefit cost ratio (BCR)

Godhra Dahod D’Baria Godhra Dahod D’Baria

N1P

1V

160716 40505 40993 3.47 2.65 2.67

N1P

1V

277485 36991 42071 4.15 2.51 2.71

N1P

2V

154440 37141 52088 3.15 2.47 3.06

N1P

2V

277422 41063 50245 4.06 2.63 2.99

N1P

3V

168353 45761 55570 3.63 2.76 3.14

N1P

3V

262554 40950 48570 3.41 2.58 2.87

N2P

1V

155921 43847 55130 3.23 2.75 3.19

N2P

1V

261545 44582 53560 3.45 2.77 3.13

N2P

2V

173002 46793 60122 3.83 2.81 3.33

N2P

2V

274834 48300 60877 3.90 2.87 3.36

N2P

3V

159793 56505 69467 3.25 3.13 3.62

N2P

3V

273866 43110 66159 3.79 2.63 3.49

N3P

1V

184414 57521 67467 4.29 3.24 3.63

N3P

1V

291030 48171 61133 4.55 2.88 3.38

N3P

2V

182127 59089 74433 4.11 3.24 3.82

N3P

2V

280885 49487 70122 4.07 2.88 3.66

N3P

3V

175847 78086 94386 3.80 3.88 4.49

N3P

3V

284181 63277 89579 4.11 3.34 4.31

*N1, N

2 to N

3are 80, 120 and 160 kg N/ha; P

1, P

2 and P

3 are 20, 40 and 60 kg P

2O

5/ha; V

1 and V

2 are GAYMH-1 and GAWMH-2, respectively.

Fix cost: Rs. 22750, cost of N1, N

2 to N

3 were Rs. 1114, 1670 and 2227 while for P

1, P

2 to P

3 were Rs 700, 1400 and 2100, respectively. The

selling prices of the grain were Rs. 17/kg while for stover were Rs 2.5/kg used for computation of the economic returns from the maize.

at Dahod, application of 160 kg N + 60 kg P2O

5/ha gave

maximum net realization i.e. Rs.78086/ha with 3.88 BCRfor yellow maize hybrid (GAYMH-1) while same treatmentgave Rs.63277/ha with 3.33 BCR for white maize hybrid(GAWMH-2). Many workers (Kumar et al., 2014; Pariharet al., 2017) have also reported better nutrient lead increasedeconomics of maize production systems.

Conclusion

The farmers of middle Gujarat zone III growing rainfedmaize hybrids GAYMH-1 and GAWMH-2 in Panchmahaldistrict are advised to fertilize crop with 160 kg N and 20kg P

2O

5/ha for securing higher grain yield with higher net

returns. While the farmers of Dahod District are advisedto fertilize crop with 160 kg N and 60 kg P

2O

5 per hectare

for securing higher grain yield with higher net returns. Thenitrogen application should be done in 4 equal split at basal,4 leaves, 8 leaves and tasseling stage of the crop growth.

References

Dass, S., Jat, S. L., Chikkappa, G. K., Kumar, B., Kaul, J., Parihar,C. M., Kumar, A., Kumar, R., Kamboj, M. C., Singh, V., Yatish,K. R., Jat, M. L., & Singh, A. K. (2012). Genetic Enhancementand crop management lead maize revolution in India. Maize J.,1(1): 7-12.

Dass, S., Kumar, A., Jat, S. L., Parihar, C. M., Singh, A. K.,Chikkappa, G. K., & Jat, M. L. (2012a). Maize holds potentialfor diversification and livelihood security. Indian J. Agron.,57(3rd IAC Special Issue): 32-37.

Dia, M., Wehner, T. C., Hassell, R., Price, D. S., Boyhan, G. E.,Olson, S., King, S., Davis, A. R., & Tolla, G. E. (2016). Genotype× environment interaction and stability analysis for watermelonfruit yield in the U. S. Crop Sci., 56: 1645–1661. http://dx.doi.org/10. 2135/cropsci2015.10.0625.

Dia, M., Wehner, T. C., Hassell, R., Price, D. S., Boyhan, G. E.,Olson, S., King, S., Davis, A. R., Tolla, G. E., Bernier, J., Juarez,B., Sari, N., Solmaz, I., & Aras, V. (2012). Mega environmentidentification for watermelon yield testing in the US. In:Cucurbitaceae Proceedings of the Xth EUCARPIA Meeting onGenetics and Breeding of Cucurbitaceae, Antalya, Turkey. 15-


18 October, 2012. University of Cukurova, Ziraat Fakultesi.pp. 385–390.

Dia, M., Wehner, T. C., Hassell, R., Price, D. S., Boyhan, G. E.,Olson, S., King, S., Davis, A. R., Tolla, G. E., Bernier, J., Juarez,B., Sari, N., Solmaz, I., & Aras, V. (2012a). Stability of fruityield in watermelon genotypes tested in multiple USenvironments. Cucurbitaceae. In: Proceedings of the XthEUCARPIA Meeting on Genetics and Breeding ofCucurbitaceae, Antalya, Turkey. 15–18 October, 2012.University of Cukurova, Ziraat Fakultesi. pp 84–88.

Jat, M. L., Satyanarayana, T., Majumdar, K., Parihar, C. M., Jat, S.L., Tetarwal, J. P., Jat, R. K., & Saharawat, Y. S. (2013). Fertilizerbest management practices for maize systems. Indian J. Ferti.,9(4): 80-94.

Jat, S. L., Parihar, C. M., Singh, A. K., Jat, M. L., Sinha, A. K.,Mishra, B. N., Meena, H., Paradkar, V. K., Singh, C. S., Singh,D., & Singh, R. N. (2013a). Integrated nutrient management inquality protein maize (Zea mays) planted in rotation with wheat(Triticum aestivum): Effect on productivity and nutrient useefficiency under different agro-ecological conditions. Indian J.Agric. Sci., 83(4): 391-396.

Kumar, A., Thakur, K. S., & Manuja, S. (2002). Effect of fertilitylevels on promising hybrids maize (Zea mays) under rainfedconditions of Himachal Pradesh. Indian J. Agron., 47(4): 526-580.

Kumar, V., Singh, A. K., Jat, S. L., Parihar, C. M., Pooniya, V.,Sharma, S., & Singh, B. (2014). Influence of site-specific nutrientmanagement on growth and yield of maize (Zea mays) underconservation tillage. Indian J. Agron., 59(4): 657-660.

Parihar, C. M., Jat, S. L., Singh, A. K., Hooda, K. S., Chikkappa, G.K., Singh, D. K., & Kumar, R. S. (2011). Maize productiontechnologies.DMR Technical Bulletin 2011/3. Directorate ofMaize Research, Pusa Campus, New Delhi-110012. pp. 36.

Parihar, C. M., Jat, S. L., Singh, A. K., Majumdar, K., Jat, M. L.,Saharawat, Y. S., Pradhan, S., & Kuri, B. R. (2017). Bio-energy,water-use efficiency and economics of maize-wheat-mungbeansystem under precision-conservation agriculture in semi-aridagro-ecosystem. Energy, 119: 245-256. DOI: 10.1016/j.energy.2016.12.068.

Yadav, O. P., Hossain, F., Karjagi, C. G., Kumar, B., Zaidi, P.H ., Jat,S. L., Chawla, J. S., Kaul, J., Hooda, K. S., Kumar, P., Yadava,P., & Dhillon, B. S. (2015). Genetic Improvement of Maize inIndia: Retrospect and Prospects. Agric. Res., 4(4):1-14. DOI10.1007/s40003-015-0180-8.


RESEARCH PAPER

A. K. Sinha: [email protected]

Senior Scientist, RMD College of Agriculture & Research Station,Indira Gandhi Krishi Vishwavidyalaya, Ambikapur, Surguja,Chhattisgarh-497001, India

Received: 18 March 2017/ Accepted: 15 June 2017© Maize Technologists Association of India 2017

Productivity and profitability influenced by plant geometry and integratednutrient management in rainfed sweet corn (Zea mays Saccharata) – horsegram (Macrotyloma uniflorum L.) cropping sequence

A. K. Sinha

Abstract: A field experiment was conducted in twoconsecutive kharif seasons of 2016 and 2017 at Ambikapurto work out the effect of plant geometry and integratednutrient management systems on the productivity andprofitability of sweet corn (Zea mays L.) - horse gram(Macrotyloma uniflorum L.) cropping system. Theexperiment was laid out in split plot design with threetreatments of plant geometry (60 X 20 cm, 50 X 20 cmand 40 X 20 cm) in main plots and four levels of fertilitylevels (RDF, 75% of RDF + 5 t vermicompost, 75% ofRDF + 5 t forest litter and 75% of RDF + 5 t FYM) withthree replications. The horse gram was taken as utera cropsown one week before harvesting of sweet corn by usingcommon starter dose (20-50-20 kg N- P- K/ha). Thepresent experiment was undertaken to find out the effectof planting geometry and different integrated nutrientmanagement systems on the system productivity and netreturn as well as to fulfill the protein requirement amongtribal people. Green cob, green fodder of sweet corn, horsegram yield and economics were higher in plant spacing of50 cm X 20 cm, which was significantly superior thanboth 60 cm X 20 cm and 40 cm X 20 cm. Further,application of 75% RDF + 5 t vermicompost significantlyincreased the green cob, green fodder, horse gram yieldover 100% RDF and 75% RDF + FYM but was at par

with 7% RDF + 5 t forest litter. Combined effect of plantinggeometry (50 cm X 20 cm) and application of 75% RDF +5 t vermicompost resulted in significantly higher sweetcorn-equivalent yield in terms of system productivity (26.9t/ha) which was comparable to planting geometry (50 cmX 20 cm) with application of 75% RDF + 5 t forest litter(25.0 t/ha) but significantly superior than all other treatmentcombinations. Hence, Sowing of sweet corn -horse gramat 50 cm X 20 cm with 75% RDF + 5 t vermicompostproduced maximum system productivity in terms of sweetcorn-equivalent yield (26.9 t/ha).

Keywords: Horse gram · Integrated nutrient management· Net return · Planting geometry · Sweet corn · Systemproductivity

Introduction

Starting from metropolis to the tribal dominated rural areas,a sizable quantity of maize is consumed as green cob in thecountry. Recently specialty corns such as sweet corn, babycorn and popcorn have emerged as alternative food sources,especially for affluent society. Maize growers are shiftingtowards specialty corn production due to higher returnsand opening opportunities for employment generationespecially in peri-urban areas. Maize is a nutrient exhaustivecrop, it responded up to 200 kg N/ha, 100 kg P

2O

5/ha and

60 kg K2O/ha. Hence, there is need to explore high the

supply of these nutrients through organic and inorganicsources. The organic source of nutrient supply has distinctadvantages of sustainability of crop production. However,availability of huge quantity of organic manure fromexternal source is often considered as problem for farmers.In this context, practices such as green manuring, recyclingcrop residues, use of FYM, biofertilizer and vermicompost


are important in cropping system. However, positive roleof these practices on soil and crops has been welldocumented. Judicious combination of organic andinorganic fertilizers helps to sustain soil productivity (Raoet al., 2002). Further, inclusion of leguminous crop incropping system is beneficial, as it increased systemproductivity and sustained soil health. Horse gram(Macrotylom auniflorum L) is the most protein-rich pulseconsumed by tribal peoples grown mostly in dry agriculturallands taken as post kharif crop.

Sweet corn is new introduction particularly as shortduration crop; so, efforts are required to standardize andeconomize its cultivation for maximum productivity.Although the agronomic requirements like plant geometryand fertility levels (Sahoo and Mahapatra, 2007) and weedcontrol methods (Arvadiya et al., 2012) for sweet cornhas been worked out for irrigated conditions but work onintensive cultivation of sweet corn and horse gram atdifferent planting geometry and at different nutrientmanagement systems having both inorganic and organicsources has not been done for rainfed conditions. Maizecrop behave differently at different cropping density andnutrient management practices (Jat et al., 2017). Therefore,the present experiment was undertaken to find out the effectof planting geometry and different integrated nutrientmanagement systems on the system productivity and netreturn in Northern Hill region of Chhattisgarh.


The present investigation was conducted during rainy(kharif) seasons of 2016 and 2017 at the Research Farm,RMD Collage of Agriculture & Research Station, Ambikapur(Chhattisgarh) situated at 23o18' N latitude and 83015' Elongitude and at altitude of 611 meter above mean sea level.The soil of experimental field was sandy loam in texturewith pH of 5.7, organic carbon 0.56%, 234 kg/ha availableN, 8.4 kg/ha P

2O

5 and 268 kg/ha K

2O. The meteorological

data recorded at meteorological observatory of the stationindicated that rainfall received during the crop seasons was1381.1 mm (59 rainy days) and 1024.2 mm (46 rainy days)in 2016 and 2017, respectively. The crop experienced meanweekly maximum temperature ranging from 235 to 30.7oCand 24.4 to 31.9oC during 2016 and 2017, respectivelywhereas mean weekly minimum temperature ranging from6.3 to 23.8oC and 6.0 to 23.6oC during 2016 and 2017,respectively. The field experiment was laid out in split plotdesign with three treatments of planting geometry (60 cmX 20 cm, 50 cm X 20 cm and 50 cm X 20 cm) in main

plots and four levels of fertility levels (RDF, 75% RDF + 5t vermicompost, 75% of RDF + 5 t forest litter, 75% ofRDF + 5 t FYM) with three replications. The recommendeddose of fertilizer (RDF) given to the crop was 120-60-40kg N-P

2O

5-K

2O/ha. Madhuri as a sweet corn and Indira

Kulthi was grown as a Kulthi variety. Sweet corn wasshown on 5th and 8th July in 2016 and 2017, respectivelywhereas horse gram was sown as utera crop sown 60days after sowing of sweet corn. Sweet corn was sown atdifferent spacing as per treatments whereas horse gramwas sown 60 days after sowing on both sides of sweetcorn. Gap filling and thinning were done within 10 daysafter sowing to maintain the optimum plant population.Inorganic and organic source of nutrients were applied asper treatments. Organic source of nutrients, viz.FYM,vermicompost and well-decomposed forest litter wereapplied 2 weeks before sowing of sweet corn crop. TheNPK content in FYM (0.87, 0.28, 0.93%), vermicompost(1.90, 1.89, 1.01%) and forest litter (1.36, 1.72, 0.87%)were determined on dry-weight basis before application.Nitrogen as per treatment was applied in three splits. Halfdose of nitrogen along with full dose of phosphorous andpotassium was applied at the time of sowing of sweet corn.The remaining dose of nitrogen was applied equally in twosplits at 30 and 45 days after sowing. Common dose ofnutrients 20-50-20 kg N-P

2O

5- K

2O /ha was applied as

starter dose at the time of sowing of horse gram, whichwas taken as utera crop. Five plants were tagged randomlyfrom each plot for recording of growth and yield attributes.The sweet corn green cobs were harvested when the cobshad dried silk, green husk corn and soft kernel during thirdweek of September. Total green fodder yield was calculatedby adding tassel weight, husk weight and green fodderweight of plants per plot at the time just after final pickings.Gross returns, net returns and benefit: cost ratios werecalculated based on prevailing market price of inputs andproduce. To compute the productivity of system sweetcorn-equivalent yield (SCEY) was obtained by dividing theeconomic value of the produce (yield of produce x priceof produce) with the price of sweet corn. Systemproductivity was worked out by adding sweet corn yieldand sweet corn-equivalent yield of total green fodder, horsegram grain or horse gram straw of respective year.

Results and discussion

Yield attributes

The yield attributes of sweet corn viz. cobs/ha, weight ofgreen cob (gm), cob length (cm), cob girth (cm), rows/


cob, kernels/ row and 1000 fresh kernel weight (g) wereinfluenced significantly with the varying plant geometry.There was significant improvement in number of cobs withdecreasing plant spacing from 60 cm X 20 cm to 40 cm X20 cm. However, values of weight of green cob, cob length,cob girth, number of rows/ cob, number of kernels/ rowand 1000 fresh kernel weight declined with each decreasein plant spacing from 60 cm X 20 cm to 40 cm X 20 cm(Table 1). Higher yield attributing characters of sweet cornwere observed under 60 cm X 20 cm which was statisticallyat par with 50 cm X 20 cm but significantly superior than40 cm X 20 cm. Similarly, the yield attributes of horsegram sown both sides of sweet corn as utera crop, viznumber of branches/plant, number of pods/plant, podlength, number of seeds/pod and 1000 seed weight,declined with decreasing plant spacing from 60 cm X 20cm to 40 cm X 20 cm of sweet corn (Table 1). Reducedspacing between plants might have increased competitionfor various resources and created stress environment forplant growth, which resulted lighter cobs, lesser kernels/cob and poor fresh kernel weight at higher density. Sahooand Mahapatra (2007) reported similar results.

Application of 75% RDF + 5 t vermicompostsignificantly increased the yield attributes of sweet corn,viz number of cobs/ha, weight of green cob, cob length,cob girth, rows/ cob, kernels/ row and 1000 fresh kernelweight over 100% RDF and 75% RDF + 5 FYM, however,it was at par with 75% RDF + 5 t forest litter (Table 1). Inhorse gram, maximum yield attributes, viz. number ofbranches/plant, number of pods/plant, pod length, numberof seeds/pod and 1000 seed weight were observed under75% RDF + 5 t forest litter treatment of sweet corn whichwas significantly superior to 100% RDF and 75% RDF +FYM treatments but comparable with 100% RDF + 5 tforest litter treatment. Optimum supply of nutrientsthroughout the crop growth period owing to thecombination of organic and inorganic sources resulted inhigher yield attributes (Choudhary et al., 2011).

Green cob, green fodder yield and yield of horse gram

The green cob yield was maximum under 50 cm X 20 cmspacing that was significantly superior than both 60 cm X20 cm to 40 cm X 20 cm (Table 2). There was less greencob yield at higher spacing (60 cm X 20 cm), owing tolower number of cobs /unit area whereas at lower plantspacing (40 cm X 20 cm), greater competition for differentresources reduced the values of different yield attributes,which consequently decreased the yield (Kumar, 2008). Ta

ble

1. Y

ield

att

ribu

tes

of s

wee

t cor

n an

d ho

rse

gram

as

infl

uenc

ed b

y pl

anti

ng g

eom

etry

and

inte

grat

ed n

utri

ent m

anag

emen

t sys

tem

(po

oled

dat

a of

2 y

ears

)

Tre

atm

ents

Sw

eet c

orn

Hor

se g

ram

No.

of

Gre

en c

obC

ob le

ngth

Cob

gir

thN

o. o

fN

o. o

fF

resh

ker

nel

No.

of

No.

of

Pod

leng

thN

o. o

f10

00 s

eed

cobs

wei

ght

(cm

)(c

m)

row

s/co

bke

rnel

s/w

eigh

tbr

anch

es/

pods

/pla

nt(c

m)

seed

s/po

dw

eigh

t (g)

(000

’/ha

)(g

)ro

w(g

/100

0 se

ed)

plan

t

Pla

ntin

g ge

omet

ry (c

m)

60 X

20

96.3

190.

118

.014

.313

.838

.227

5.0

7.8

70.5

5.4

6.0

19.9

50 X

20

109.

418

6.3

17.7

14.0

13.5

37.0

265.

97.

663

.65.

05.

519

.6

40 X

20

110.

514

3.1

14.4

11.8

10.1

30.7

245.

47.

256

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75.

319

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SEm

±0.

42.

60.

020.

20.

20.

32.

70.

12.

10.

10.

20.

1

CD

(P

=0.

05)

1.4

10.4

0.4

0.7

0.7

1.2

11.0

0.4

8.2

0.5

0.6

0.6

Inte

grat

ed n

utri

ent m

anag

emen

t

100%

RD

F*

105.

616

5.1

15.9

12.5

11.2

32.2

243.

37.

060

.64.

95.

218

.9

75%

RD

F +

5 t

Ver

mic

ompo

st10

5.8

183.

317

.514

.213

.637

.527

7.7

8.1

66.1

5.2

5.9

20.2

75%

RD

F +

5 t

For

est

litt

er10

4.8

174.

317

.013

.713

.036

.626

7.6

7.8

64.6

5.1

5.7

19.7

75%

RD

F +

5 t

FY

M10

5.4

170.

116

.413

.312

.234

.925

9.9

7.2

62.5

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11.

90.

20.

20.

20.

43.

40.

11.

10.

10.

10.

1

CD

(P

=0.

05)

3.3

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0.5

0.6

0.6

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0.3

3.1

0.2

0.3

0.5

*RD

F :

120

N, 6

0 P 2O

5 an

d 40

K2O

kg/

ha


Maximum green cob was obtained with 75% RDF + 5 tvermicompost which was statistically at par with 75% RDF+ 5 t forest litter but significantly superior than RDF and75% RDF + 5 t FYM. Increase in green cob yield owing tovermicompost, forest litter and FYM incorporation mightbe attributed to steady release of nutrients to soil for longerduration after decomposition resulting in better plant growthand yield attributing characters. Mukharjee (2014) reportedsimilar effects of organic manure incorporation in maize.

Planting geometry contributed significantly towardsgreen fodder yield. The increase in plant population byreducing spacing from 60 cm X 20 cm to 40 cm X 20 cmled to higher green fodder yield (Table 2). Minimum spacingof 40 cm X 20 cm produced the maximum green fodderof 25.13 t/ha. The highest number of plants/unit areacontributed significantly towards green fodder yield(Kumar, 2008). All the integrated nutrient managementsignificantly affected the green fodder. Application of 75%RDF + 5 t vermicompost had maximum green fodder yield(24.69 t/ha) which was statistically at par with 75% RDF+ 5 t forest litter but significantly superior than RDF and75% RDF + 5 t FYM. The green fodder yield advantageobserved in combination of inorganic fertilizers withapplication of vermicompost, forest litter and FYM mightbe due to the increased growth and yield attributingcharacters in maize. Shanwad et al. (2010) also reportedthe enhancement in maize productivity with combinedapplication of nutrients through organic and inorganicresources.

Yield components of horse gram taken as utera cropsown both sides of sweet corn significantly influenced bydifferent plant geometry. The grain yield of horse gramwas maximum (1.17 t/ha) under 50 cm X 20 cm spacingwhich was significantly superior to both 60 cm X 20 cm(1.08 t/ha) to 40 cm X 20 cm (0.99 t/ha) (Table 2). Therewas less grain yield of horse gram at higher spacing (60cm X 20 cm), owing to lower number of yield attributingcharacters whereas at lower plant spacing (40 cm X 20cm), greater competition for different resources reducedthe values of different yield attributes, which consequentlydecreased the yield. Maximum grain yield of horse gramwas obtained with 75% RDF + 5 t vermicompost whichwas statistically at par with 75% RDF + 5 t forest litter butsignificantly superior than RDF and 75% RDF + 5 t FYM.Increase in grain yield of horse gram owing tovermicompost, forest litter and FYM incorporation mightbe attributed to steady release of nutrients to soil for longerduration after decomposition resulting in better plant growthand yield attributing characters. The results confirm thefindings of Mukharjee (2014). Ta

ble

2. E

ffec

t of

plan

t geo

met

ry a

nd in

tegr

ated

nut

rien

t man

agem

ent s

yste

m o

n yi

eld

and

econ

omic

s of

sw

eet c

orn

and

hors

e gr

am (

pool

ed d

ata

of 2

yea

rs)

Tre

atm

ents

Gre

en c

ob y

ield

Gre

en fo

dde

Hor

se g

ram

Sw

eet c

orn

equi

vale

ntS

yste

m p

rodu

c-E

cono

mic

s of

sys

tem

(t/h

a)(t

/ha)

grai

n yi

eld

yiel

d (t

/ha)

tivi

ty (

t/ha

)(x

103

Rs/

ha)

(t/h

a)G

reen

fodd

erH

orse

gra

m(S

wee

t cor

n)G

ross

ret

urns

Net

ret

urns

Ben

efit

: cos

t rat

io

Pla

ntin

g ge

omet

ry (c

m)

60 X

20

18.3

021

.05

1.08

0.53

3.8

22.6

122

6.1

185.

84.

6

50 X

20

20.3

923

.45

1.17

0.59

4.1

25.0

825

0.8

210.

05.

2

40 X

20

15.5

125

.13

0.99

0.63

3.5

19.9

519

9.5

157.

73.

8

SEm

±0.

250.

370.

010.

010.

00.

262.

62.

60.

1

CD

(P

=0.

05)

0.97

1.45

0.05

0.04

0.2

1.03

10.3

10.3

0.2

Inte

grat

ed n

utri

ent m

anag

emen

t

100%

RD

F*

17.2

722

.13

0.96

0.55

3.4

21.3

021

3.0

174.

14.

5

75%

RD

F +

5 t

Ver

mic

ompo

st19

.19

24.6

91.

170.

624.

124

.02

240.

219

9.7

4.9

75%

RD

F +

5 t

For

est

litt

er18

.08

23.2

21.

100.

583.

922

.63

226.

318

3.2

4.3

75%

RD

F +

5 t

FY

M17

.74

22.7

91.

090.

573.

822

.24

222.

418

0.9

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±0.

320.

410.

030.

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353.

53.

50.

1

CD

(P

=0.

05)

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1.05

10.5

10.5

0.3

*RD

F :

120

N, 6

0P2O

5 an

d 40

K2O

kg/

ha


System productivity

System productivity of the cropping system (sweet corn– horse gram) was significantly affected due to differentplant geometry (Table 2). The highest system productivitywas recorded under plant geometry (50 cm X 20 cm) whichwas statistically superior than 60 cm X 20 cm to 40 cm X20 cm. The highest -equivalent yield was attributed withthe combined effect of green cob yield, fodder yield andhorse gram yield.

Application of different integrated nutrient managementsystems had significant effect on system productivity ofdifferent component of sweet corn- horse gram croppingsystem (Table 2). The maximum system productivity wasrecorded under 75% RDF + 5 t vermicompost which wasstatistically at par with 75% RDF + 5 t forest litter butsignificantly superior than RDF and 75% RDF + 5 t FYM(Table 2). Islam and Munda (2012) and Jat et al. (2013)reported increase in system productivity owing to combinedapplication of organic and inorganic fertilizer on precedingcrop and residual effect of organic fertilizer on succeedingcrop.

Economics

Planting geometry (50 cm X 20 cm) followed by horsegram recorded maximum gross return, net return andbenefit: cost ratio that was statistically superior than 60cm X 20 cm to 40 cm X 20 cm. This could be ascribed tohigher yield of green cob, green fodder and horse gramunder plant spacing of 50 cm X 20 cm. Among integratednutrient management systems, 75% RDF with 5 tvermicompost had maximum gross return, net return andbenefit: cost ratio which was statistically comparable with75% RDF with 5 t forest litter but significantly superior toother nutrient management systems like RDF and 75% RDF+ 5 t FYM. This was owing to higher yield of sweet corn,green fodder and horse gram under the treatment 75%RDF with 5 t vermicompost. Mukharjee (2014) reportedsimilar results under maize- yellow mustard croppingsystem.

It is therefore concluded that Sowing of sweet corn at50 cm X 20 cm with 75% RDF + 5 t vermicompostproduced maximum system productivity in terms of sweetcorn-equivalent yield, net profit and benefit-cost ratio.Based on the present study, sowing of sweet corn at 50cm X 20 cm plant geometry in combination with 75%RDF + 5 t vermicompost or forest litter produced

comparable higher net profit for farmers of Northern hillsof Chhattisgarh and make sweet corn available for marketalong with horse gram which is the main source of pulse(protein) for tribal peoples.

References

Arvadiya, L. K., Raj, V. C., Patel, T. U., & Arvadia, M. K. (2012).Influence of plant population and weed management on weedflora and productivity of sweet corn (Zea mays). Indian Journalof Agronomy, 57(2): 162-167.

Choudhary, B. R., Gupta, A. K., Parihar, C. M., Jat, S. L., & Singh,D. K. (2011). Effect of integrated nutrient management onfenugreek (Trigonella foenum-graecum) and its residual effectof fodder pearl millet (Pennisetum glaucum). Indian Journal ofAgronomy, 56(3): 189-195.

Islam, M., & Munda, G. C. (2012). Effect of organic and inorganicfertilizer on growth, productivity, nutrient uptake and economicsof maize (Zea mays L.) and toria (Brassica compestris L.).Agricultural Science Research Journals, 2(8): 470-479.

Jat, S. L., Parihar, C. M., Singh, A. K., Jat, M. L., Sinha, A. K.,Mishra, B. N., Meena, H., Paradkar, V. K., Singh, C. S., Singh,D., & Singh, R. N. (2013). Integrated nutrient management inquality protein maize (Zea mays) planted in rotation with wheat(Triticum aestivum): Effect on productivity and nutrient useefficiency under different agro-ecological conditions. IndianJournal of Agricultural Sciences, 83(4): 391-396.

Jat, S. L., Parihar, C. M., Singh, A. K., Kumar, B., Singh, B., &Saveipune, D. (2017). Plant density and fertilization in hybridquality protein maize (Zea mays): Effects on the soil nutrientstatus and performance of succeeding wheat (Triticum aestivum)and productivity of cropping system. Indian Journal ofAgricultural Sciences, 87(1): 23-28.

Kumar, A. (2008). Productivity, economics and nitrogen useefficiency of specialty corn (Zea mays) as influenced by plantingdensity and nitrogen fertilization. Indian Journal of Agronomy,53(4): 306-309.

Mukharjee, D. (2014). Influence of integrated nutrient managementon Productivity, nutrient uptake and economics of maize (Zeamays) – yellow sarson (Brassica rapa) cropping system underrainfed mid hill condition. Indian Journal of Agronomy, 59(2):221-228.

Rao, A.S., Chand, S., & Srivastava, S. (2002). Opportunities forintegrated plant nutrient supply system for crops/ croppingsystem in different agro-eco regions. Fertilizer News, 47(12):75-78.

Sahoo, S. C., & Mahapatra, P. K. (2007). Response of sweet corn(Zea mayssaccharata) to plant population and fertility levelsduring rabi seasons. Indian Journal of Agriculture Sciences,77(11): 779-781.

Shanwad, U. K., Kumar, A., Hulihalli, B. N., Surwenshi, U. K.,Reddy, A. M., & Jalageri, B. R. (2010). Integrated nutrientmanagement (INM) in maize – Bengal gram cropping system inNorthern Karnataka. Research Journal of Agricultural Sciences,1(3): 252-254.


RESEARCH PAPER

Ganapati Mukri: [email protected]

1ICAR- Indian Agricultural Research Institute, New Delhi-110012

2ICAR-Indian Institute of Maize Research, Ludhiana-141004

Received: 21 September 2017/ Accepted: 31 October 2017© Maize Technologists Association of India 2017

Selection of high density responsive inbred lines for enhancing maizeproductivity

Ganapati Mukri1 · R. N. Gadag1 · Thirunavukkarsau Nepolean1 · Jayant S. Bhat1 · S.L. Jat2

Abstract: The concept of high density planting in maizeenvisages higher productivity by increased plant populationper unit area. It is expected that the high density responsiveinbred lines, when crossed would give high densityresponsive hybrids. In this context, 25 elite inbred lineswere evaluated under normal to high density ranging from66,666 plants/ha to 1,11,1111 plants/ha, during kharifseason-2016 at ICAR- Indian Agricultural ResearchInstitute, following split plot method. Genotypes understudy interacted with plant density and majority of thegenotypes showed yield penalty when we increased plantdensity from 66,666 plants/ha. Association studies indicatedthat the ear placement and cob length are the two majorfactors which decide the yield under different plant density.Inbred lines, viz, PML 93, PML 35, PML 50, PML 46,KRN 2-5-2 and UMI 1200 were responded well to highdensity stress with better yield under high plant density.Use of these inbreds in development of base population aswell as developing hybrids suited for high density plantinghas been suggested.

Keywords: Productivity · High density · Inbred line · Maize

Introduction

Achieving food security with limited arable land will presenta major challenge in the twenty-first century, owing to thegrowing world population and changing climate. The globalpopulation is expected to reach nine billion by 2050 (Sheldenand Roessner, 2013), representing an additional two billionpeople to feed. Globally, as additional land for maizeproduction is limited, it is essential to increase the maizeproductivity to cope with the increasing demand for grains.High-density planting is a practical approach for increasingmaize yield per unit area (Lee and Tollenaar, 2007). Forinstance, the average maize yield is approximately 6,000kg/ha with a planting density of 52,500–67,500 plants/hain China, both parameters are lower than those in the UnitedStates. If maize planting density is increased by 15,000plants/ha, the total maize yield is predicted to be enhancedby 20% (Gong et al., 2015). However, maize varieties/hybrids that are suitable for high-density planting are stilllacking in most of the developing countries due to variousfactors including non availability of suitable genotypes(Andrivon, 2013). Thus, identification of such varieties isan important task in enhancing productivity of maize.

Since single cross hybrids involve a set of two inbredlines as parents, any expected yield improvement shouldbe carried-out first in inbred lines followed by targetedselection of hybrids which enables higher productivity(Dass et al., 2009). Therefore, identification of efficientinbred lines coupled with their improvement is thesustainable approach in elevating maize productivity throughbreeding for “new traits” such as high plant densityresponse. It is essential that before breeding for stressresponsive hybrids one should identify stress tolerantparental lines which inherits the trait and expressed inhybrids. Hence, efforts were made to identify potentialinbred lines for high-density tolerance along with optimum

plant density for needed for their higher productivity. Suchinformation would facilitate breeders to develop highyielding, density responsive maize hybrids for elevatingproductivity.

Material and Methods

The study was conducted at ICAR-Indian AgriculturalResearch Institute, New Delhi, during Kharif 2016. A setof 25 potential inbred lines selected based on their per seyield performance were evaluated under different plantdensities. Inbred lines were evaluated in a split-plot designin a randomized complete block arrangement with tworeplications. The main plots of experiments were devotedto four plant densities (66,666 plants/ha (type I), 83,333plants/ha (type II), 88,888 plants/ha (type III) and 1,11,111plants/ha (type IV),) and sub plots were devoted togenotypes. Standard agronomic practices were followedto raise a healthy crop. Data were recorded from fivecompetent plants from each replication and each treatment.Data on plant height (cm), ear height (cm), number ofcob, cob length (cm), cob diameter (cm) and grain yield(kg/ha) were recorded and analysis of variance, correlation

studies and descriptive statistics were performed using SASpackage (9.3 v).

Results and Discussion

Analysis of variance for genotypes was highly significantfor all the traits indicating the existence of substantialvariability among the inbred lines studied (Table 1). Further,ANOVA was significant for both plant density and densityx genotype interaction for plant height, ear height and grainyield implying the differential response of inbred lines todifferent plant densities. The analysis of contribution oftraits for phenotypic variation revealed that the phenotypicvariation observed in replications is mainly coming fromgrain yield (63.5%) followed by ear placement (27.8%),cob length (24.8%) and plant height (20.4%), whereas theleast was recorded by cob girth (1.4%). On the other hand,density played a major role in deciding phenotypic variation(PV) through cob girth (71.0%) followed by plant height(44.0%) and ear placement (25.4%) where grain yield(5.9%) contributed the least. Genotypes had maximumcontribution to phenotypic variation through cob length(42.3%) followed by ear placement (36.6%) and plant height

Table 1. ANOVA for yield and yield component traits of maize inbred lines under different density

Source df Plant height Ear height Cob length Cob girth Yield

Replication 1 2224.44 752.72 11.23 0.06 2.35

Density 3 4802.77* 688.65* 6.76 2.89* 24.97**

Error (Density) 3 194.71 50.39 2.49 0.10 0.66

Genotype 24 3285.40** 990.94** 19.12** 0.77** 7.14**

Density × Genotype 72 258.26** 137.35* 2.55 0.11 1.59**

Error (Genotype) 96 149.04 90.45 3.06 0.13 0.34

Table 2. Contribution of traits through different components to percent phenotypic variation

Components Ear placement Plant height Cob girth Cob length Yield

Replication 27.8% 20.4% 1.4% 24.8% 63.5%

Density 25.4% 44.0% 71.0% 15.0% 5.9%

Genotype 36.6% 30.1% 19.1% 42.3% 1.7%

Density × Genotype 5.1% 2.4% 2.7% 5.6% 4.1%

Table 3. Differential (range) expression of genotype under different plant density

Density (Plants/ha) Characters

Plant height (cm) Ear height (cm) Cob length (cm) Cob girth (cm) Yield (kg/ha)

66,666 (Type I) 112.5-205.0 60.0-107.5 9.80-17.15 2.50-3.70 990-3160

83,333 (Type II) 100.0-192.5 47.5-102.5 9.55-15.85 1.90-3.20 560-3260

88,888 (Type III) 100.0-210.0 55.0-110.0 9.50-14.85 2.20-3.80 980-4330

1,11,111 (Type IV) 80.0-170.0 37.5-90.0 8.90-15.25 2.00-3.20 970-4170



(30.1%) and the least through grain yield (1.7%). In caseof density x genotype interaction also played role incontributing phenotypic variation mainly through earplacement (5.1%), cob length (5.6%) and grain yield (4.1%)(Table 2).

A wide variation for different traits was observed underall 4 plant densities. It could be noted that the extent ofexpression of each character was influenced by plant density(Table 3). For instance, plant height ranged from 80-170cm in type IV, 100-210 cm in type III density, 100-193 cmin type II density and 112.50-205 cm in type I density. Earheight ranged from 60-107 cm in type I density, 47.50-102.50 cm in type II density, 55-110 cm in type III densityand 37.50-90 cm in type IV density. It is also noticed thatthe range of ear height was proportional to the plant height.Besides, wide variation was observed for cob length. Coblength ranged from 9.80-17.15 cm under type I density,9.55-15.85 cm in type II density, 9.50-14.85 cm in typeIII density and 8.90-15.25 cm in type IV density. However,there was no significant variation in cob girth and it rangedfrom 2.5-3.70 cm in type I density, 1.90-3.20 cm in typeII density, 2.20-3.80 cm in type III density and 2.00-3.20cm in type IV density. The range for grain yield was veryhigh, which ranged from 990-3160 kg/ha in type I, 560-3260 kg/ha in type II, 980-4330 kg/ha in type III and 970-4170 kg/ha in type IV (Table 3). I was noticed that genotypesunder study interacted significantly with the environmentand plant densities. Similar results were reported earlier byDong et al. (2006), in cotton. As the density stressincreased from type I to type IV, plant height, ear height,cob length and grain yield recorded progressively higherrange; some genotypes failed to express their yieldpotentiality which may be due to barrenness, less nutrientefficiency, photosynthetic inability etc. Sangoi (2001)observed that when the number of individuals per unit areais increased beyond the optimum plant density, there is aseries of consequences that are detrimental to ear ontogenythat result in barrenness. However in present study fewgenotypes tolerated density stress and yielded bettercompared to other genotypes overcoming negative effectof density stress.

To find out component traits responsible for highyielding ability under stress, association study between yieldand its component traits under different plant density wasundertaken (Table 4). Correlation between component traitsunder different plant density indicated that, plant heightwas significantly correlated with ear height. Ear height iscorrelated positively with yield in all densities. Cob diameterhad highly significant correlation with the grain yield in all Ta

ble

4. C

orre

lati

on b

etw

een

diff

eren

t yi

eld

com

pone

nt t

rait

s ac

ross

dif

fere

nt p

lant

den

siti

es

Ear

hei

ght (

cm)

Cob

leng

th (c

m)

Cob

dia

met

er (c

m)

Yie

ld (t

/ha)

Cha

ract

ers

Typ

e I

Type

II

Typ

e II

ITy

pe I

VT

ype

IT

ype

IIT

ype

III

Type

IV

Typ

e I

Typ

e II

Typ

e II

ITy

pe I

VT

ype

IT

ype

IIT

ype

III

Type

IV

Pla

nt h

eigh

t (cm

)0.

856

0.79

80.

847

0.89

80.

1706

0.15

870.

2664

0.13

300.

4597

0.26

560.

1288

0.19

660.

4694

0.30

120.

6020

0.43

17<

.000

1<

.000

1<

.000

1<

.000

10.

4148

0.44

860.

1980

0.52

620.

0208

0.19

940.

5392

0.34

610.

0179

0.14

330.

0015

0.03

12

Ear

hei

ght (

cm)

0.25

789

0.14

408

0.09

269

0.06

086

0.54

846

0.11

457

0.17

075

0.23

869

0.49

421

0.12

673

0.54

290

0.44

514

0.21

330.

4920

0.65

940.

7726

0.00

450.

5855

0.41

450.

2505

0.01

200.

5461

0.00

500.

0258

Cob

leng

th (c

m)

0.34

222

-0.0

547

0.06

378

-0.0

011

0.42

245

0.12

835

0.36

508

0.23

524

0.09

400.

7950

0.76

200.

9958

0.03

540.

5409

0.07

270.

2577

Cob

dia

met

er (c

m)

0.64

615

0.60

049

0.39

975

0.42

242

0.00

050.

0015

0.04

770.

0354

Type

I-

66,6

66 p

lant

s/ha

, T

ype

II-

83,3

33 p

lans

/ha,

Typ

e II

I- 8

8,88

8 pl

ants

/ha,

Typ

e IV

-1,1

1,11

1 pl

ants

/ha


Table 5. Selected inbred lines and their per se grain yield (t/ha) under different plant density

Genotypes 66666 Plants/ha 83333 Plants/ha 88888 Plants/ha 1,11,111 Plants/ha

PML-3 1.05 3.07 2.26 1.94

PML-35 2.29 3.26 4.07 1.65

PML-46 2.82 3.06 3.80 3.54

PML-48 1.08 2.81 2.40 2.01

PML-50 3.16 2.36 4.33 4.17

PML-76 1.23 1.44 3.61 2.04

PML-93 1.95 2.10 3.54 3.44

KRN-2-5-2 2.52 2.26 3.85 3.33

KRN-3-12-1 1.53 2.92 3.20 1.83

UMI 1200 2.63 2.31 3.83 3.28

Mean yield (t/ha) 1.82 2.11 2.82 2.15

Std Div 0.59 0.65 0.69 0.85

densities. Remaining traits viz., plant height, ear diameterand cob length showed poor association with yield. Henceear height and cob diameter were the most important yieldattributing traits which was not affected much by differentdensity stress. Based on the yield per se, some of the highyielding lines could be selected across the densities (Table5). Among the tested lines, PML 35, PML 46, PML 50,PML 76, PML 93, KRN-2-5-2, and UMI 1200 significantlyout yielded over the trial mean at type III density. Theselines can be designated as density tolerant lines for rainyield. Both increased and decreased density stress thantype III resulted in decreased grain yield of inbred lines,apparently it indicating that type III density is appropriatefor screening materials suitable for high density planting.Among the tolerant line for density stress, PML 35 andPML 50 showed high yielding ability (4.07 and 4.33 t/ha,respectively). Hence they could be a potential geneticresource for future maize breeding of developing hybridssuitable for high density. However, these conclusions arebased on small plot experiments. Detailed studies onphysiological reason for high density tolerance, geneticsof high density tolerance etc, may be studied. The per seyield performance of selected high density tolerant inbredlines may be confirmed by multi location and multiyearexperimentation.

References

Andrivon, D., Giorgetti, C., Baranger, A., Calonnec, A., Cartolaro,P., Faivre, R., Guyader, S., Lauri, P. E., Lescourret, F., Parisi,L., Ney , B., Tivoli, B., & Sache, I. (2013). Defining and designingplant architectural ideotypes to control epidemics?, Eur. J. PlantPathol., DOI 10.1007/s10658-012-0126-y.

Dass, S., Kaul, J., Manivannan, Singode, A., & Chikkappa, G. K.(2009) Single cross hybrid maize – A viable solution in thechanging climate Scenario, Indian J. Genet., 69(4): 331-334.

Gong, F., Wu, X., Zhang, H., Chen, Y., & Wang, W. (2015). Makingbetter maize plants for sustainable grain production in a changingclimate. Front. Plant Sci., 6: 835. doi: 10.3389/fpls.2015.00835

Lee, E. A., & Tollenaar, M. (2007). Physiological basis of successfulbreeding strategies for maize grain yield, Crop Sci., 47: S202–S215. doi:10.2135/cropsci2007.04.0010IPBS.

Shelden, M. C., & Roessner, U. (2013). Advances in functionalgenomics for investigating salinity stress tolerance mechanismsin cereals. Front. Plant Sci., 4: 123. doi: 10.3389/fpls.2013.00123

Dong, H. Z., Li, W. J., Tang, W., Li, Z. H., & Zhang, D. M. (2006).Effects of genotypes and plant density on yield, yieldcomponents and photosynthesis in Bt transgenic cotton, J.Agron. & Crop Sci., 192: 132-139.

Sangoi, L. (2001). Understanding plant density effects on maizegrowth and development: an important issue to maximize grainyield. Ciencia Rural., 31(1): 159-168.

Maize Journal (April & October 2017) 6(1&2): 56

Author index

Atkare Anand P., 9

Avni, 9

Bhat Jayant S., 52

Bisht Sunaina, 1

Chouhan D., 27

Darekar Ashwini, 22

Gadag R. N., 52

Gogoi Robin, 1

Jain D., 27

Jat S.L., 52

Karjagi Chikkappa G., 9

Kumar Arvind, 1

Maloo S. R., 27

Mehra Rakesh, 1

Mukri Ganapati, 52

Nepolean T., 52

Parmar L. D., 35

Parmar P. K., 40

56

Patel J. A., 35

Patel J. M., 35

Patel K. H., 40

Patel M. B., 40

Patel Nilesh, 35

Rathod D. M., 40

Reddy A. Amarender, 22

Shekhar Meena, 1

Shukla P. K., 9

Singh D., 27

Singh Ishwar, 9

Singh S.K., 40

Singh Vimla, 1

Sinha A. K., 47

Thakker B. N., 40

Thakor D. M., 35

Yadava Pranjal, 9

aize ournal...pranjal yadava dr. krishan kumar dr. ashok kumar dr. chikkappa, g.k. dr. s.l. jat dr....

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