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Allelopathic effects of crops (sorghum, sunflower and brassica) on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.)

By

RAZA ULLAH

M.Sc. (Hons.) Agronomy

2006-ag-1838

A thesis submitted in partial fulfilment of the requirements for the degree

DOCTOR OF PHILOSOPHY

I N A G R O N O M Y

DEPARTMENT OF AGRONOMY,FACULTY OF AGRICULTURE,

UNIVERSITY OF AGRICULTURE,FAISALABAD, PAKISTAN

2018

ii

Oh, Allah Almighty open our eyes,To see what is beautiful,

Our minds to know what is true,Our heart to love what is Allah

My Respected

PARENTS AND SUPERVISOR

Whose encouragement, spiritual inspiration, well wishes, sincere prayers and an atmosphere that initiate me to achieve high academic goals

ACKNOWLEDGEMENT

v

On the accomplishment of the present study, I would like to take this opportunity to extend

my deepest sense of gratitude and words of appreciation towards those, who helped me during the

pursuit of study. I deem it a proud privilege and feel immense pleasure to acknowledge all those who

are directly or indirectly involved.

I am thankful to the most Gracious, Merciful and Almighty ALLAH who gave me the health,

thoughts and opportunity to complete this work, I bow before my compassionate endowments to

HOLY PROPHET (SAW), who I sever a torch of guidance and knowledge for humanity as a whole.

Firstly, I would like to express my sincere gratitude to my supervisor Dr. Zubair Aslam,

Assistant Professor, Department of Agronomy, University of Agriculture, Faisalabad, for the

continuous support of my Ph.D. study and related research, for his patience, motivation, and immense

knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not

have imagined having a better advisor and mentor for my Ph.D. study.

Besides my supervisor, I would like to thank the rest of my thesis committee: Dr. Abdul

Khaliq, Professor, Department of Agronomy, University of Agriculture, Faisalabad and Dr. Zahir

Ahmad Zahir, Professor, Institute of soil and Environmental Science, University of Agriculture,

Faisalabad, for their insightful comments and encouragement, but also for the hard question which

incented me to widen my research from various perspectives.

My vocabulary utterly fails in expressing my accolade to my father, Ghulam Muhammad

who brought me to this stage, who dreamed me to perform best in life by manifesting eternal

characters and they prayed for my success every time.

Cordial love and thanks to my loving brothers viz. Atta Ullah, Sana Ullah, Zaka Ullah, Zia

Ullah, Sakha Ullah, sweet Sisters, loving nice viz. Atta-ul-Mohsin, Atta-ul-Momin, Muhammad

Hanzala, Muhammad Musabe, Qari Ameer Moavia and Ameer Abdullah who exhibited a

prolong patience for my studies and whose support always energized me to perform the best.

Last but not the least, I pay my cardinal and sincere feelings for my (Late) Mother, whose

prayers are accompanied in the journey of my life. May Allah rest her soul in peace for ever

(Aameen).

The financial support from Higher Education Commission Pakistan (HEC) under project no.

20-2014/NRPU/R&D/12/4188 in titled with “Influence of crop allelopathy on microbial diversity of

rhizosphere” is highly acknowledged. I owe my gratitude to all those who have extended the help and

support in a way or other, in completion of this task.

(Raza Ullah)

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Chapter-IV RESULTS AND DISCUSSION 424.1 Experiment-I: Effect of sorghum crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 42

4.1.1 Weed dynamics 424.1.2 Crop data 454.1.2.1 Emergence and morphological traits 454.1.2.2 Yield and yield components 484.1.3 Rhizosphere soil analysis 484.1.3.1 Rhizosphere soil properties and nutrients dynamics 484.1.3.2 Rhizosphere soil microbial population, activity and enzymes 534.1.4 Economic and marginal analysis 564.1.5 Discussion 584.1.6 Conclusion 61

4.2 Experiment-II: Effect of sunflower crop water extracts and residues on weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 62


4.3 Experiment-III: Effect of brassica crop water extracts and residues on weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 82


4.4 Experiment-IV: Isolation of allelochemical resistant strains of bacteria and determination of their active role in rhizosphere 102

4.4.1 Isolation and purification of bacterial strains 102

4.4.2 Test resistance of bacterial against synthetic allelochemicals and allelopathic crops water extracts 102

4.4.3 Biochemical characterization 1054.4.4 Bioassays for plant growth promoting traits 1054.4.5 Discussion 1114.4.6 Conclusion 112

Chapter-V SUMMERY 1135.1 Experiment-I: Effect of sorghum crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 113

5.2 Experiment-II: Effect of sunflower crop water extracts and residues on weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 114

5.3 Experiment-III: Effect of brassica crop water extracts and residues on weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) 115

5.4 Experiment-IV: Isolation of allelochemical resistant strains of bacteria and determination of their active role in rhizosphere 115

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5.5 Conclusion 1165.6 Future research priorities 116

BIBLIOGRAPHY 117APPENDICES 138

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LIST OF TABLES

SR.NO. TITLE PAGE NO.

3.1Response of soil properties, nutrient dynamics, soil enzyme activities and microbial populations of the experimental soil before sowing (2014 and 2015)

29

4.1 Mean square of weed dynamics affected by sorghum crop water extracts and residues 43

4.2 Effect of sorghum crop water extracts and residues on weed dynamics in mung bean 44

4.3 Mean square of final emergence and morphological traits of mung bean affected by sorghum crop water extracts and residues 46

4.4 Effect of sorghum crop water extracts and residues on final emergence and morphological traits of mung bean 47

4.5 Mean square of yield and yield components of mung bean affected sorghum crop water extracts and residues 49

4.6 Effect of sorghum crop water extracts and residues on yield and components of mung bean 50

4.7 Mean square of soil properties in the rhizosphere of mung bean affected by sorghum crop water extracts and residues 51

4.8 Effect of sorghum crop water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest 52

4.9Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by sorghum crop water extracts and residues

54

4.10Effect of sorghum crop water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean

55

4.11 Economics of mung bean grown in various allelopathic weed management strategies during 2014 and 2015 57

4.12 Marginal analysis for two years (2014-2015) 57

4.13 Mean square of weed dynamics affected by sunflower crop water extracts and residues 63

4.14 Effect of sunflower crop water extracts and residues on weed dynamics in mung bean 64

4.15 Mean square of final emergence and morphological traits of mung bean affected by sunflower crop water extracts and residues 66

4.16 Effect of sunflower crop water extracts and residues on final emergence and morphological traits of mung bean 67

4.17 Mean square of yield and yield components of mung bean affected by sunflower crop water extracts and residues 68

4.18 Effect of sunflower crop water extracts and residues on yield and components of mung bean 69

4.19 Mean square of soil properties in the rhizosphere of mung bean affected by sunflower crop water extracts and residues 71

4.20 Effect of sunflower crop water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest 72

4.21Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by sunflower crop water extracts and residues

74

4.22Effect of sunflower crop water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean

75


x


4.25 Mean square of weed dynamics affected by brassica crop water extracts and residues 83

4.26 Effect of brassica crop water extracts and residues on weed dynamics in mung bean 84

4.27 Mean square of final emergence and morphological traits of mung bean affected by brassica crop water extracts and residues 86

4.28 Effect of brassica crop water extracts and residues on final emergence and morphological traits of mung bean 87

4.29 Mean square of yield and yield components of mung bean affected by brassica crop water extracts and residues 88

4.30 Effect of brassica crop water extracts and residues on yield and components of mung bean 89

4.31 Mean square of soil properties in the rhizosphere of mung bean affected by brassica crop water extracts and residues 91

4.32 Effect of brassica crop water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest 92

4.33Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by brassica crop water extracts and residues

94

4.34Effect of brassica crop water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean

95



4.37 Test the resistance of bacterial isolates from mung bean rhizosphere against synthetic allelochemicals and allelopathic crop water extracts 103

4.38 Morphological characteristics of allelochemical resistant bacterial isolates from mung bean rhizosphere 106

4.39 Assessment of zinc activity of allelochemical resistant bacterial isolates from mung bean rhizosphere 107

4.40Assessment of cellulase enzyme, nitrogen fixation and phosphate solubilization activity of allelochemical resistant bacterial isolates from mung bean rhizosphere

109

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LIST OF FIGURES

SR.NO. TITLE PAGE NO.3.1 Weather data during the period of study (2014 and 2015) 29

xii

LIST OF ABBREVIATIONS AND SYMBOLS

ABBREVIATIONS AND SYMBOLS DESCRIPTIONSWE Sorghum water extractSR Sorghum residueSFWE Sunflower water extractSFR Sunflower residueBWE Brassica water extractBR Brassica residueg-1 Per gramg Gramm2 Meter squarecm Centimetercm-3 Per cubic centimeteroC Degree Celsiuskg Kilogramkg-1 Per kilogramha-1 Per hectare

RCBD Randomized complete block design

dS Deci semenµg Microgrammg MilligramEC Electrical conductivityhrs Hoursh-1 Per hourAK Available potassiumAP Available phosphorusN NitrogenOM Organic matterLSD Least significant differencemL Milli litterµL Micro litterL-1 Per litterL Littermin Minutesmm Millimeterm-2 Per meter squarem-1 Per meterNS Non-significantppm Parts per millionNP Naphthyl phosphateTPF Triphenylformazand-1 Per dayHI Harvest indexNM-92 NIAB MUNG-92T Ton

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W WeightV Volumecfu Colony forming unitmax Maximummin Minimumtemp Temperature@ At the rate of% Percentet al. And othersi.e. That isviz. Namelyd.f Degree of freedomDAS Days after sowingcv. CultivarDAP Diammonium phosphateSOP Sulphate of potashCO2 Carbon dioxideC Carbonnm NanometerM MolarmM Milli molarrpm Revolution per minuteOD Optical densityRs. Rupees$ US dollar

xiv

AbstractThe present research work was conducted to evaluate the effect of allelopathic crops like sorghum (Sorghum bicolor L.), sunflower (Helianthus annuus L.) and brassica (Brassica compestris L.) on weeds, productivity and rhizosphere of mung bean during 2014 and 2015. Laboratory trials were conducted in Plant and Microbial Ecology Lab, Department of Agronomy, University of Agriculture, Faisalabad. Three sets of field experiments were conducted and repeated at Student Research Farm, Department of Agronomy, University of Agriculture, Faisalabad. In all three field experiments, crop water extracts (10 and 20 L ha -1) were foliar applied at 15 DAS and residues incorporation (4 and 6 tons ha -1) was done before sowing. Among all the treatments residue incorporation at 6 tons ha-1 showed the highest suppression of weed density, fresh and dry weight (sorghum; 62, 67, 65%, sunflower; 57, 66, 61% and brassica; 52, 61, 56% respectively). In case of soil properties significant improvement was observed by the application of crop residues at 6 tons ha -1 as compared with control. In all three field experiments maximum mung bean seed yield was improved (37%, 36% and 33%) by the application of sorghum, sunflower and brassica crop residues, respectively at 6 tons ha-1 as compared with control. Sorghum, sunflower and brassica residues incorporation at 6 tons ha-1 had higher net benefits (306, 339 and 347 $ ha-1, respectively) followed by 4 tons ha-1 during both years. In the fourth experiment the bacterial strains 4-17HM, C-14HM, C-17HM and 10-10M isolated from rhizosphere soil of mung bean which was amended by allelopathic crop water extracts and residues, showed the highest resistance against synthetic allelochemicals and allelopathic crop water extracts and the maximum nitrogen fixing, zinc and phosphate solubilization activity. So, the whole study was concluded that the residues incorporation of different allelopathic crops (sorghum, sunflower and brassica) was more effective than their water extracts application in weed suppression, improvement in soil health and productivity of mung bean. Application of crop residues at 6 tons ha-1 was the most effective and economical treatment with highest net benefit and marginal rate of returns. Due to their resistance and active role of bacterial strains in the rhizosphere, as nitrogen fixer, zinc and phosphate solubilizer, they could be applied with the allelopathic crop water extracts and residues to manage weeds and improve soil health.

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Chapter-I

INTRODUCTION

In the pulse industry, the term ‘mung bean’ refers to mainly green-seeded types with

pods borne toward the top of the plant. Mung bean (Vigna radiata L.) is an important pulse

crop and in developing countries it is the best alternative to meet the need of food for

increasing population. Among pulse crops, V. radiata L. is a good source of vegetable

protein and can play vital role in the national economy. Mung bean seeds contain 51%

carbohydrate, 26% protein, 3% minerals, 10% moisture and 3% vitamins (Ali et al., 2010).

In spite of short duration, nitrogen fixing character and nutritional superiority, mung

bean faces the weeds as main competitor when cultivated in spring and autumn seasons.

Long season weed-crop competition reduces the green pod yield by 45.60% (Pandey and

Mishra, 2003). There are several methods for controlling weeds like cultural, manual and

chemical methods. Although hand weeding and herbicide application are considered

effective weed management practices (Cheema et al., 2003). The shair of herbicides is 15%

in the total pesticides used globally, (Gupta, 2004). In Pakistan, herbicide use was increasing

day by day due to the intry of mechanical tools in agriculture system. In underdeveloped

countries, the misuse of agro-chemicals are high as compared to developed one due to less

awareness about the safety measures of agrochemicals; low literacy rate and lack of skill to

handle and apply farm chemicals as per recommendations (Tariq et al., 2007). The misuse of

agro-chemicals caused degradation in environmental quality, tolerance in pests and

disturbance in human health. In agri industry worldwide 3 million tons of herbicides are

currently used annually. Due to the judicious use of herbicide, resistant weeds have become

more prolific (Shibayama, 2001). To overcome these problems, it is a need of the time to

think about alternatives for sustainable weed management that may help in improving quality

and production of crops by reducing herbicides use and burden of manual weeding (Farooq et

al., 2011a).

Improving in quality and yield of crops is an urgent task for agri-scientists of recent

era, to meet the need of food requirement of increasing global population that is projected to

exceed 9 billion by 2050 (Khanh et al., 2005; World Resources Institute, USA, 2014). To

1

provide food for this increasing population it must be possible through agronomic approaches

which improve crop yield and friendly for ecosystem. Crop allelopathy is an agronomic

approach for managing weeds which may improve yield without environmental risk, the

important concept for world younger scientists to work and secure the world’s food supply

for future generations.

Inhibition due to chemical compounds is well documented and it was the known

aspect of allelopathy. Directly or indirectly the inhibitory character of chemical compounds

has been used for managing weeds. In case of crop allelopathy, a lot of work has been done

to explore their inhibitory potential for managing weeds (Cheema et al., 2004; Farooq et al.,

2011b). Water extracts of sorghum and sunflower crop has ability to control weeds by 50%

and 40%, respectively (Naseem et al., 2010). Tawaha and Turk (2003) found a water soluble

allelochemicals in black mustard which cause inhibition in germination and growth of other

plants. Allelopathic crop residues have also ability to suppress weeds, improve soil health

and harvest better yield (Khaliq et al., 2015). Cheema and Khaliq (2000) stated that sorghum

residues incorporation after chopped into small pieces suppressed dry weight of weeds from

26 to 56% which increase yield from 6-17%. Khaliq et al. (2011) told that the dried residues

of crops (sorghum, sunflower and brassica) showed phytotoxicity against weeds.

Crop allelopathy has an important concern in weed research and accepted as an

important eco-logical phenomenon very recently. It has been studied exten-sively in recent

past as a possible tool to manage weeds in agro eco-systems. Many works has shown the

water spray of allelo-pathic crops can be used to reduce the dose of herbicides and by adding

in crop rotation or residue icorporation of such crops aslo help in weeds suppression. Release

of organic compounds (secondary metabolites) in rhizosphere is an important aspect of such

allelopathic crops but has not been investigated. Root exudation, residue degradation and

volatilization through allelopathic crops modifyied the soil rhizosphere and to significant

extant biotic transformations take place. High allelopathic nature of such crops (sorghum-

sunflower-brassica) and their affect on weeds and rhizo-sphere of soil etc. encouraged the

idea to check the feasibility of water extracts and residues of these crops using as bio

herbicides to manage weeds in mung bean crop in comparison with synthetic herbicides.

Bhowmik and Inderjit (2003) opinioned that whether the allelochemicals impact microbial

ecology and physiochemical status of soil or not, must be established before they will be used

2

on large scale to manage weeds in agro-ecosystems. Alternatively, allelochemicals would be

the final products in the market in the future as alternate of commercial herbicides. It is

therefore, hypothesized that to manage weeds in mung bean crop through allelopathic water

extracts and residues of crops may influence physical, chemical and biological traits of

rhizosphere. The main objectives of this work would be

1. To determine the allelopathic effect of water extracts and residues of sorghum, sunflower

and brassica on weed density and mung bean yield.

2. To study the physical, chemical and biological traits of rhizosphere as modified by the

application of allelopathic crop water extracts and residues.

3. To work out the economic efficiency of various treatments.

Chapter-II

3

REVIEW OF LITRATURE

2.1. Allelopathy and allelochemicalsThe word allelopathy is derived from two words “Allelon” and “Pathos”. These both

words are Greek words which mean “each other” and “to suffer i.e. the injurious effects of

one upon another” respectively. Mostly scientists used this term in a restricted sense. They

only describe its harmful effects (one higher plant upon another). It is a complex

phenomenon between phenolic compounds and allelochemicals concentrations. It has both

inhibitory as well as stimulatory effects, which may be decided by the concentration of

allelochemicals present in extract.

Chemical compounds that impose allelopathic effects are called allelochemics or

allelochemicals. These are chemicals produced by secondary metabolism or microbial

decomposition. These are non-nutritive substances and active group of allelopathy. Based on

chemical similarities the allelochemicals are classified into 14 categories (Rice, 1984). Plant

organs like plant roots, plant rhizomes, plant leaves, plant stem, plant bark, plant flowers,

plant fruits and plant seeds are the best source of chemical release in soil through leaching

volatilization, root exudates, and decomposition (Cecile et al., 2003). Crop and soil

researchers, weed experts and natural product chemists are ongoing to study this interesting

area and published data regarding allelopathy are increasing expo-nentially (Macias,` 2002).

`Allelopathy` have significant role in agri-industry to manage weeds by releasing

allelochemicals from plant parts (plant roots, plant leaves, plant flowers, plant stem and plant

seeds) (Uchino et al., 2012).

2.1.1. Concepts of scientific community related to allelopathy

Due to improvements in agri-science, allelopathy has become a well-known scientific

concept. Young scientists of current era got more attention to this interested concept of

allelopathy. In agriculture production system the use of synthetic/manmade chemicals

(insecticides, herbicides) are extensively increased by farmer community. This extensive use

is dangerous for public health as well as for whole environment. So, it is a need of time to

find novel and ecofriendly practices like allelopathy. Many scientists had studied regarding

Allelopathy of field crops. They reported that it is an asso`ciation between crop plants whose

4

affect may be positive or negative it depends on the concentration of allelo-chemicals. This

positive or negative effect is due to the release of bio-chemicals known as allelochemicals.

Firstly the research related allelopathy was started by Rick Willis, (1985). He presented three

evidence related allelopathy study. Firstly, he said that the plant must release chemicals.

Secondly, the accumulation of allelochemicals in soil does not disturb the water and nutrient

uptake. Thirdly, plant activities are not inhibited by biotic or other soil physical factors.

Mostly scientists related to allelo-pathy are working on it, to explore its charactiristics

more and more. Most of organic compounds are water soluble, possessing inhibitory effects.

After extraction in water or any other solvent they can be used as ‘nature’s own herbicides’.

Water extract of allelopathic crops is the best option to manage weeds under sustainable crop

production. The crop water extracts have significant effects in many studies (Shahid et al.,

2007; Iqbal and Cheema 2008; Jamil et al., 2009). Water extracts containing bio-chemicals

called allelochemicals have inhibitory or promotory effects on the growth of target organisms

(Narwal, 1994). The negative or positive effect of organic compounds is totally dependent

upon its concentrations. Its negative effects increased by increasing concentration and

decreased by decreasing concentration (Farooq et al., 2009b).

In agri industry the concept related to allelochemicals “Suppress or inhibit weed

growth at higher concentration” was firstly presented by Putnam and Duke (1974). They said

that this suppressive affect of` field crops can be used to manage weeds as cover crop,

intercropping and rotations in crops (Putnam and Duke, 1978). Crop allelopathy may affect

the growth of weeds, pathogens, insect, and pests (Huerta et al., 2010; Xuan et al., 2005;

Raghavendra et al., 2006; Ren et al., 2008; Jabran et al., 2010; Khanh et al., 2005; Joseph et

al., 2008).

So, that’s the reason why, the concept of allelopathy is gaining more attention by

researchers. At higher concentration it the best alternative of man made/synthetic chemicals

for managing weeds. The strong allelopathic effects of crops have potential which can fulfill

the need of synthetic/manmade agro-chemicals as traditional herbicides for controlling weeds

(Makoi and Ndakidemi, 2012).

2.1.2. Presence, nature, mode of release and action of allelochemicals

Plant parts i.e. leaves, stems, roots, rhizomes, seeds, flowers and pollens have

allelochemicals. Each plant part has different concentrations of allelochemicals (Kruse et al.,

5

2000). Mostly allelopathic crop plants store their protective allelochemicals within their

leaves, especially during fall. When the leaves separate from plant and come to the ground,

they decompose and release toxins which can affect neighbor plants. Maple, pine and

eucalyptus trees are great examples of release of allelopathins through their leaves. Some

crop plants also release their toxins by roots, which are then absorbed by other plants.

Due to hydrophobic nature these bio chemicals recognized as phenolics, alkaloides,

flavonoids and glyccosides e.t.c. in various environments (`Lu,`2011). Few decades past the

scientists have been concentrating to iso-late and iden-tify these bio chemicals. Gu-enzi and

Mc Calla (`1966`) isolated and identified bio chemicals (p-coumaric, ferulic, vannillic, p-

hydroxy-benzoic and syringic acids) in different crop residues (maize-wheat-sorghum). Chlo-

rogenic, iso-chlorogenic acid, scopolin and a--naphthol derivatives were isolated and

identified by Wilson and Rice (1968) in sunflower. From Oryza sativa L. coumaric, p-

hydroxybenzoic, ferulic and vanillic acids were isolated and identified (Ch-ou and Lin, 1976;

Man-dava, 1985). Ein-hellig et al. (1982) reported possɨble dervatіves like trans-cinnamic,

caffeic, p-coumarіc and ferulіc acіds. Bio chemicals (ferulіc and coumerіc acіds) secreted

from wheat has in-hibitory pro-perties against the weed plants (prіckly-sіda and mornіng-

glory) (Wor-sham, 1984). Sorghum root exudate (sorgoleone) was found to be phyto-toxіc to

several weed plants (Nctzyl and Butler, 1986).

Bio chemicals or met-abolites released through vola-tilization, leaching, exu-dation

and decom-position processes. Vola-tіlіzatіon, leachіng, adsor-ptіon and mіcrobіal-actіon

depends upon persis-tence of bio chemicals іn soіl. Releasing pathway of allelochemicals into

the environment varies from one plant species to another. The following are known

pathways: (1) Leachate and chemical runoff from plant stem and leaf by washing off by

rainfall. (2) Exudates from different parts of plants (Halligan, 1973); (3) Release of toxic

material by decomposition of material such as rye mulch. (4) Release of toxic compounds

from the plant roots. Living rice plants suppressed weed growth (Olofsdotter et al., 2002;

Chon et al., 2006). Allelopathins are very strong chemicals agents which target crucial

processes in plants to alter various physiological and biochemical processes like respiration,

transpiration, mineral uptakes, photosynthesis, etc. that result in plant growth inhibition,

stomata closure, alteration of mineral uptake, alteration in enzyme activities, induction of

moisture stress and hormonal imbalance (Gniazdowska and Bogatek, 2005). In case of

6

respiration, juglone disrupt the uptake of oxygen. Uncoupling of oxidative phosphorylation

and inhibition of electron transformation was caused by Alphapinene. Camphor, limonene

and other monoterpenes significantly affect the radicle and hypocotyl. They also inhibit

production of mitochondrial ATP, decreases mitochondrial transmembrane potential and

impairs mitochondrial energy metabolism (Hejl and Koster, 2004). During photosynthesis

allelochemicals mainly damage synthesis machinery and acceleration of decomposition

photosynthetic pigments. Due to this damage, photosynthetic pigment contents are decreased

consequently, block electron energy transfer, reduce activity of ATP synthesis enzymes, and

inhibit ATP synthesis, and stomatal conductance, which inhibit the process of photosynthesis

(Meazza et al., 2002; Yu et al., 2006; Wu et al., 2004). Further, allelochemicals are also

involved in altering the micro and ultra-structure of cells, altering protein and nucleic acid

biosynthesis, imbalance of antioxidant enzymes and influence on plant growth regulators.

Moreover, the effect of allelopathins on microbial community and ecological environment is

equally effective (Cheng and Cheng, 2015).

2.2. Factors affecting allelopathic compoundsAllelochemicals release, its types and amount into environment depends on plant as

well as environmental factors (Albuquerque et al., 2010). The plant factors consist of plant

species, variety, growth stages and various tissue organs (Iannucci et al., 2013). Zhang et al.

(2010) was conducted an experiment to check allelopathic effect of eucalyptus roots and its

associated rhizosphere soil on weed growth and germination. Their results show that water

extract of roots suppressed growth and emergence of Raphanus sativus plants. Plants of same

environment do not have similar production of secondary metabolites, and they may not

secrete same quantity and quality (Imatomi et al., 2013). Furness et al. (2008) reported that

Hounds tongue plants under high ultraviolet-B radiation increased their allelopathic

influence.

Soil properties and micro flora could influence allelopathy phenomenon in soil.

Under nutrient deficiency debris from Helianthus annuus L. plants showed to be more

effective than debris from control. Soil microbes greatly influence bio-activity and

availability of allelochemicals in soil environment (Inderjit, 2005). Accumulation of

allelopathins at phytotoxic levels in soil is largely determined by the presence of

microorganisms. Soil microbes commonly use allelopathic compounds as carbon sources

7

(Sene, 2000). Soil pH also affects adsorption, desorption and transport in soil and the

metabolism of allelochemicals which in turn could result in modification of expressed

response on growth of receiver plants (Kobayashi, 2004). Pramanil et al. (2000) reported that

synthesis of allelochemicals depends on light intensity, photoperiod and temperature. In

general, longer photoperiod and high temperature is favorable for the releasing of

allelochemicals.

2.3. Classification of potential allelopathic effectsA potential allelopathic effect was classified in two parts by Inderjit and Weiner

(2001). In-direct plant to plant allelopathic interference, A plant produce X compound, which

interfere with B plant. In-direct soil interactions can be further divided into three parts. First

part is indirect-allelopathy, A plant produces X compound which is degraded/transformed by

C microbes into Y compound, which interfere B plant. In induced-allelopathy A plant

produces X compound which released and induced D organism to produce Z compound

which interferes with B plant. Second part is indirect-toxicity X compound interacts with soil

and produces Z compound, which interferes with B plant. The third part is effect of indirect-

environment X compound causing a change in soil-environment, which affects soil nutrients.

It also reduces growth and reproductive output of B plant, without toxic effects.

2.4. Types of allelopathy2.4.1. Weed on crop

Weeds as they have very high allelopathic potential may affect growth and emergenc

of recipient plants. El-Khatib et al. (2004) stated that allelopathy has a major role in deciding

patterns and succession in any eco-systems. Firstly, human activities affect on dominance,

substitution or extinction of species, and secondly, allelopathy. The interactions exhibited

through allelopathy of weeds and play a vital role in normal as well as manipulated eco-

systems. The nature of any plants depends on the presence of various allelochemicals in it.

Since, agriculture origin, weeds are linked with field crops and reduced their yield. Many

scientists have described allelopathic potential of weeds on field crops. Inderjit (2004a,b) had

given a comprehensive biology and ecology of weeds in agricultural systems. These weeds

have evolved with indigenous or traditional agriculture e.g. Echinochloa evolved with rice

cultivation in Japan. Plant residues, root exudates; leaf and seed leachates/extracts of weeds

had inhibitory allelopathic effects on different crops (Ambika and Smitha, 2005; Batish et al.,

8

2005 and Tajuddin et al., 2002). Root extract of Parthenium hysterophorus test on cluster

bean, amaranthus and lady finger in lab. Results showed that all extracts inhibited

germination and dry matter in order to cluster bean > amaranthus > lady finger. The

variations in effects might be due to changes in plant composition and allelochmicals during

different stages of plant (Gupta, 2000).

Channappagoudar et al. (2003) check allelopathic potential of Cyperus rotundus,

Commelina bengahalensis, Parthenium hysterophorus, and Prosipus juliflora at two

concentrations (5 and 10%) on sorghum, wheat, green gram, soybean, sunflower and

groundnut. The results revealed that water extracts inhibit seed germination, growth of

seedling length and vigor index. Among the crops tested groundnut and wheat were more

resistant to allelopathic effect to weeds. A lab study was done by Sannigrahi and

chakraborthy (2005) to check allelopathy of different weeds. All these weed plants inhibit

germination of tomato crop. Water extracts of I. sepiania and C. odoratum leaves showed

highest inhibitory effect on tomato germination. Allelopathic activities of weeds were

assessed on vegetable crops. Incorporation of shoot residues reduced the growth of seedling

of all crops. Whereas, residues used as a mulch delayed emergence of seedlings (Obaid and

Qasem, 2005). The bioassay study showed that aqueous leachates of dry plant parts of nut

sedge (Cyperus rotundus) inhibited seedling emergence and growth of okra, bitter gourd and

onion. Perhaps due to the presence of phenolics acids (Ameena and George, 2002).

2.4.2. Weed on weed

Parthenium allelopathy is well documented by many researchers (Batish et al., 2002;

Singh et al., 2003; Singh et al., 2005) and plant parts i.e. pollens and plant tri-chomes have a

number of allelochemicals which are water soluble (Kohli et al., 2006). Kathiresan (2000)

found that parthenium (dry powder) was most effective against Eichhornia crassipes. The

adverse effect foliar spray of parthenium leachates against Ageratum conyzoides, Avena fatua

and Bidens pilosa are well acknowledged (Batish et al., 2002a). High concentration of

parthenium water extract significantly suppressed germination, length of seedlings of Avena

fatua (Batish et al., 2002a). Aslam et al. (2014) studied parthenium allelopathy against Avena

fatua and Phalaris minor germination and growth. They said that leaf extract inhibited

germination of test plant as compared with whole plant and root water extracts at all

concentrations.

9

Sinha and Singh (2004) rported that growth and emergence of Parthenium

hysterophorus was found restricted by 25% leaf extract of Xanthium stromanium. A

significant reduction was also recorded in root/shoot length; seedling vigour and vigour index

of Parthenium by leaf extract treatment of X. stromanium. The water extract of Lantana

camera (fresh and dry leaves) inhibited the growth of water hyacinth (Zhung et al., 2005).

The effects of cogon grass foliage and root residue extracts on emergence, radicle and

coleoptile growth of barnyard grass, brown top millet, Bermuda grass, hemp sesbania, Italian

ryegrass, and prickly sida were investigated in laboratory by Koger and Bryson (2004). The

results showed that cogon grass residues (foliage and root) extract inhibited bermuda grass

and Italian rye grass germination. Germination of Bermuda grass and Italian rye grass was

reduced by as much as 62% and radicle and coleoptile growth by as much as 96% at the

highest extract concentrations. Foliage and root residues extract reduced germination of

barnyard grass, brown top millet, and prickly sida 52 to 64% and seedling growth by as much

as 96%. Mengal et al. (2015) studied water extracts of weeds on weeds of wheat. They

reported that that Convolvulus arvensis at 60% resulted in a positive impact on weed number

and biomass. Penna et al. (2003) stated the suppressive effect of Chenopodium ambrosioides

on Bidens pilosa germination. The research on allelopathic properties of weeds, weed-weed

interactions, characterization of allelochemicals, mechanism of release and weed

management, is well documented.

2.4.3. Crop on weed

Many allelopathic crops have potential to produce various allelochemicals into the

environment which leads to suppress weed growth in their vicinity. Here we discussed only

essential crops which have been documented for their allelopathic effect on weeds. Sor-

ghum, rice-sunflower-brassica and maіze resіdues and extracts contaіn-many allelo-chemіcals

whіch are use-ful and cost-effective to manage weed plants. Cheema and Khalіq (2000)

investigated the impact of water extract and residues of sorghum to manage wheat weeds.

They showed that 2, 4, and 6 Mg ha-1 sorghum stalk incorporation significantly suppressed

weed dry biomass (42, 48, and 56%, respectively). In case of water extract spray of sorgaab

reduced weed dry weight (35 to 38%). Rice exudates and decomposed residues contain a

large number of allelochemicals and reduced growth of plant specіes present іn theіr vіcіnіty

(Rіmando and Duke, 2003).

10

Extracts of sunflower roots, stems and leaves caused sіgnіficant іnhіbіtіon іn dry

biomass of weed (Anjum et al., 2005). Residues of sunflower crop reduced Cyamopsis

tetragonoloba and Pennisetum americanum growth. This reduction was due to the phenolics

released through decomposition of sunflower tissues (Batish et al., 2002). Siemens et al.

(2002) evaluated that cultivated/naturally occurring brassica plants have allelopathic effect

for many years, mustards form comparatively pure stands. Narwal et al. (2004) indicated that

Brassica spp. caused highest decrease (75 to 82%) in germination and 75 to 98% weeds

density. Rape-seed residue incorporation in soil before sowing of cotton crop reduced the

germination of Amaranthus spp. (A. retroflexus and A. theopherasti) whіle germіnatіon of

cotton crop remaіned unaffected; thіs was due to release of secondary metabolites during

decomposition of rape seed (Younesabadi, 2005).

2.5. Allelopathy in crop productionThe chemical nature of bio products confirmed that many crop plants have strong

allelopathic potential. The allelo-chemicals present in those crop plants affect on growth-

promoting strategies to obtain better growth-development and graіn yіeld of crop. The several

crop-plants (sor-ghum, sun-flower, bra-ssіca, rіce, maіze, mul-berry and morіnga) have

maximum allelopathіc potentіal and keep various allelo-chemicals. When these are used as

mulch//cover crop found to be bene-ficial and eco-nomical in im-proving yield of field crops.

It also found that allelochemicals of these crops can be used for increasing crop-production

with good quality food (An et al., 2005). In previous studies, crop allel-opathy in the field of

agri-culture has been debated in detail (Put-nam and Duke, 1978; Weston, 1996; Singh et al.,

2001).

2.6. Allelopathy for managing weed floraWeeds are very problematic, damaging and harmful for field crops. These cause

many pro-blems in cropping systems. De-crease in crop-yields is the most vital and im-

portant features which is due to inter-ference i.e. com-petition and alle-lopathy or both. To get

higher yield farmers are depend upon artificial products to meet problems due to weeds

(Sadeghi et al., 2010). The term “allele-chemicals” originally coined for naturally found bio

chemicals in plant body by Whittakar in 1970, whіch can be used for weed-control. These

allele-chemicals affect plant growth it depends upon their con-centration. At higher con-

centrations these che-micals act as herbicides to manage weed plants (Cousens and Mortimer,

11

1995). In Pakistan, several methods are ad-apted to manage this issue (weed control) like use

of selective herbicides or mechanical control, but all these methods are failed to solve this

threat. Due to high prices of weedicides, gro-wers often want to trust on alternate app-roaches

to manage weeds.

In agri-culture, due to their allelopathic nature more than 250 weed species are

causing ver serious problem (Patterson, 1986). To tackle these issues herbicides are heavily

sprayed. It causes various environmental problems and also increased herbicide resistance in

weeds. Hence, a natural weed controll stratigies may be select in agricultural system. Almost

all parts of crop plants are allelopathic in nature and have inhibitory or positive effect for

weeds of barley and canola (Ashrafi et al., 2009; Asghari and Tewari, 2007; Niakan et al.,

2008; Zaji et al., 2009).

The trust on organic-substances in agri-culture production-systems has increased

manifold all over the world (Jamil et al., 2009). It is the need of time to find an alternative

practice to reduce dependence on traditional practices and synthetic chemicals for managing

weeds. (K-hanh, et al., 2005; Farooq et al., 2011-b). Alle-lopathy of crop plant has a great po-

tential to solve this-issue and used to manage weeds by different ways.

2.6.1. Intercropping of allelopathic crops

Inter-cropping of allelopathic crops has been found to be active and an alternate

method to manage weeds. Grow allelopathic crop at the same time in the same field cause

weeds suppression (Baumann et al., 2000). To prevent competition between both crops tose

crops are cultivated which have same physіo-logіcal, morpho-logіcal features. Vandermeer

(1989, 1992) said that these crops provіde chances to destroy weeds by compeeting for

resources (consume more portіon of present resources). Inter-cropping of allelopathic crops

may be favorable to decrease the use of herbicides and reduce the labor cost to manage

weeds (Chou, 1999). In maize, growing of groundnut and sweet potato as intercrops

suppressed weeds and yield losses was decreased. It also saved time which is compulsory for

managing weeds (Steiner, 1984).

Density of weeds reduced in inter-cropping as compare with sole crop due to releasing

of bio chemicals from the inter-crops (Liebman and Dyck, 1993). Inter-cropping of pea and

barley resulted more competition to weeds and in-organic soil nitrogen re-sultantly used for

the pro-duction of graіns as a subs-tіtute of weed-bіomass, as compare to sole crops (Hau-

12

ggaard-Nielsen et al., 2001). Brainard and Bellinder (2004) said that when crops grown

together as inter-crop it increased fertility of soil. It inhibits weeds but also reduced the іnsect,

pest and dіsease compitiotion. Cotton and black gram also sіgnіficantly improved yield when

grown as іnter-crop by managing weeds (Jayakumar et al., 2008).

2.6.2. Crop allelopathy as cover crop/mulch

Allelopathic crops as cover crop or mulch have an effective role for managing weeds

in sustainable agriculture. Residues of crops are used as cover crop producrd bio-chemicals

which inhibited the weed plants (Barnes and Putman, 1983, 1986). Crop residues as mulches

also produced allele-chemicals and showed greater effect against weed plants (Weston,

1996). Sorghum mulche (3.50, 7.00, and 10.50 tons ha-1) inhibited (22 to 62% and 56%,

respectively) total number of weeds (Cheema et al., 2000). In maize crop, weed reduction

(26-37%) was observed by sor-ghum-residues (10-15 Mg ha-1) used as-surface mulch

(Cheema et al., 2004). Ahmed et al. (2000) also reported 78% inhibition in weight of weed

when sorghum was used as surface mulch. Philarus minor density and biomass was

decreased (45-53%, res-pectively) due to sor-ghum-residues (15 tons ha-1) as surface mulch. It

suppressed 40% weed density and and 50% dry mass (Mahmood and Cheema, 2004).

Similarly, in organic farming system sun-flower mulch is also con-sidered as an

approach to manage weeds. Residues sunflower crop as mulch in legumes or wheat were

found to be very effective to controll weeds (Gr-wronski et al., 2002; Bernat et al., 2004).

Crop-residues (soybean, corn and sunflower) were used as mulch to compare with soy bean,

corn and sunflower grow as cover crop. The results showed that residues as mulch were more

effective than cover crop in controlling weeds (Barker and Bhowmik, 2001)

2.6.3. Water extracts of allelopathic crops

Water extracts of sorghum and sunflower crops have been found to be very-affective

to manage weeds (Cheema et al., 1997). Tri-anthema portula-castrum L. growth was

decreased when water extract of sorghum crop (100%) was used. Its germination was reduced

by 15-20%. At higher concentrations (75 and 100%) root and shoot length of T. portula-

castrum L. was also sig-nificantly reduced (Randhawa et al., 2002). Naseem et al. (2009) also

reported that by applying concentrated sunflower extract it suppressed the growth and

emergence of little seed canary grass (Phalaris minor L.) by 16.30% as compared to distilled

water. Similarly, Irshad and Cheema, (2004) said that fresh biomass inhibition (37-41%) of

13

Echinochloa crusgalli L. was observed by spray of sorghum extrat (sorgaab). Seedling

growth of Physalіs angulata L. inhibited by water extract of Brassіca-spp. (Ure-mіs et al.,

2005) and reductіon (38 to 41%) іn barn--yard grass dry bio-mass was noted by the spray of

sorgaab (sor-ghum water extract) (Irshad and Cheema, 2005).

2.6.4. Applіcatіon of allelopathic crop water extracts as a mixture

Allelopathіc crop water extracts mix wіth organіc materіal can іncrease the all

elopathіc potentіal (Blum, 1999). Allelo-chemical compounds lіke p-coumarіc and ferulіc

acіd have been reported to be more suppressіve. But іn combine application both compounds

reduced seed germination, shoot elongation. It also showed that the combine application is

more effective than individual (Rasmussen and Einhellig, 1977). It also noted that the

іnhіbіtory effect of trans-cіnnamіc acіd was іncreased by 17 times when applied іn combіne

form wіth bіcyclіc-sesquiterpene-dіaldehyde (Fujita and Kubo, 2003).

Chemical (herbicides) and manual weed control methods are more efficient as

compared to allelopathy but allelopathic weed control method is eco-friendly. Further, it was

also reported that combine application of allelo-chemicals and herbicides perform better to

inhibit weeds. Cheema et al. (2003b) described that the use of allopathic crop extracts with

lower dose of herbicide was an eco-nomical method to manage weeds. They noted that by

using sorgaab with herbicide can be reduced up to 67%. In cotton crop sorgaab (10 L ha -1)

with 1/3rd pendi-methaline and s-metolachlor dose may be used to manage weeds (Cheema et

al., 2003a).

2.6.5. Incorporation of allelopathic crops residues

Incorporation of allelopathic crops residues release allele-chemicals which interact

with microbes, generate biotic stresses and affect the growth of plants. These microbes used

minerals, H2O and O2 as energy sources during decomposition process of crop residues and

compete for food with plants (Javaid and Bajwa, 1999). Sorghum crop residues can be used

to manage weeds in field crops (rice, brassica, wheat, maize and mung bean). It is an in-

expensive and recognized practice in agricultural system (Mahmood and Cheema, 2004).

During decomposition sorghum crop residue released a large quantity of phe-nolics which

affected the upcoming crops (Sene et al., 2001).

14

2.7. Allelopathic potential of cropsIdentification of toxicity level of chemicals released from allelopathic plants is

important for controlling weeds. Putnam and Duke (1974) firstly utilized crop plant which ha

allelopathic potential to suppress weeds in agri industry. Later these allelopathic crops were

used in intercropping, cover-crop/mulch, or rotation in crops for managing weeds

(Worthington and Reberg Horton, 2013). Many crops have potential to produce various

allelochemicals in the environment which lead to suppressing the weed growth. In addition,

Farooq et al. (2013) reported that crop allelopathy was also used for mitigation of stress,

managing pests, and enhancement in crop production. Screening for best genotypes which

have high allelochemicals has been used in many studies under exogenous conditions, and

suggest to collected that crop which have different ability to inhibit weeds (Gupta, 2011).

Various attributes i.e. pattern of roots, vigor, size of leaf, and allelopathic potential can make

one cultivar more viable with one weed than other (Andrew et al., 2015).

Today, most secure and eco-friendly strategies to manage weeds are the need of

world. Various practices used in agri industry like minoring, minimum tillage, rotation,

mulch/cover crops, etc., need knowledge for previous allelopathic interactions that can occur

between the plant species involved. Variations in cropping patterns, problems in crop

replantation and fruits on or-chards trees or low production could be a negative indication of

allelopathy (Chon et al., 2006).

Present knowledge of plant bio-chemistry proved such crops which release

allelochemicals they may be manipulate to manage weeds to minimize the use of herbicides.

Allelopathic crops residues and water extracts contain various allele-chemicals which are

useful and cost effective to manage weeds. Many crops have been documented due to their

allelopathic action. This allelopathic action of crops depends on crop variety, fertility status

of soil, climatic conditions, weed competition type, and availability of water. Here, sorghum,

sunflower and brassica have been debated in following parts for their allelopathic potential

against weeds.

2.7.1. Sorghum allelopathy

Among the world’s cereals, sorghum (Sorghum bicolor L.) has 5th position in terms of

acre age. Primarily it is grown in Africa, India, China, South America, and stress prone areas

of the United States. In Asia and Africa, it is a staple food for over three hundred million

15

people (Able et al., 2001; Subudhi and Nguyen, 2000; Able et al., 2001). Due to its

allelopathic ability it is the most extensively studied crop as compared with others. Its

allelopathic potential depends upon age and genotype, location or environment, and cropping

system. All above factors affect the allelochemicals production which impact plant growth in

field, lab, or in greenhouse. In sorghum seven allelopathic compounds viz. p-

hydroxybenzoic, protocateuic, syringic, p-coumaric, gallic, benzoic and vanillic acids have

been identified (Iqbal and Cheema, 2008). Allelopathy of sorghum crop to manage weeds has

been exposed to wide studies with the objective of using this concept for managing weeds.

Various approaches have been used including sorghum water extracts, residue as mulch or

cover crop or incorporation in soil, and in rotations of crops (Weston et al., 2013). In this

part, potential of sorghum water extracts and residues for managing weeds is reviewed.

2.7.1.1. Sorghum crop water extract

Globally, a lot of work has been done to check the allelopathic ability of sorghum

water extracts. In Pakistan, water extracts of mature sorghum plants is known as sorgaab.

The formulation of sorgaab is to put the sorghum chopped residues in water (distilled) with

1:10 ratio of w/v for 24 hours. After that time filtration was done and filtrate was preserved

in clean bottles for further use (Cheema and Khaliq, 2000). The use of sorgaab to manage

weeds in several crops, especially in wheat, maize, mung bean and mustared have been

studied by different researchers i.e. Ahmad et al. (2000), Bahatti et al. (2000), Cheema et al.

(2001) and Cheema et al. (2002). The results showed that a significant increase in crop yield

was observed by foliar spray of sorgaab at different times after sowing which caused highest

weed suppression. The increase in yield was also depending upon both concentration and

number of foliar sprays. For example, one foliar spray of sorgaab resulted in 13.5% more

yield, while two foliar sprays increased 18.6% yield (Cheema and Khaliq, 2000).

Shah et al. (2016) assessed the allelopathic effect of sorghum crop to improve yield

of sunflower. The results showed that 93.7% reduction in weed density was noted by three

foliar sprays. The same reduction rate was observed by applying recommended dos of s-

metolachlor (1.6 L ha-1). In other study, interference of weeds and the role of allelopathic

aqueous extract for effective control of weed in maize were checked by Naeem et al. (2016).

The authors reported that combine application of sorghum and sunflower water extracts have

greater suppression for density, dry biomass and persistence index of weeds. Kandhro et al.

16

(2015) noted the effect of sorghum and sunflower extracts on weed mortality and cotton

yield. The authors reported that sorghum water extracts caused significant reduction of weeds

and increased seed cotton yield as compared to weedy check. The combined application of

sorghum @ 15 L ha-1 + Dual Gold @ 1.25 L ha-1 resulted in weeds mortality up to 66.6%.

Sabahie et al. (2014) also studied the impact of water extracts on germination of red root

pigweed (Amaranthus retroflexus L.). They showed that all sorghum water extract treatments

had a statistically significant effect on pigweed germination percentage and rate but water

extract of shoot at 100% concentration showed the maximum inhibitory effects on pigweed

germination. To compare the allelopathic effects of sorghum plant parts aqueous extracts

with manual weeding and chemical (herbicide) suppression of wheat weeds under rain fed

conditions, an experiment was conducted by Ashraf and Akhlaq (2007). The authors showed

that sorghum stem, leaf and root aqueous extracts was resulted more effective treatments but

leaf and root reduced weed density (20.72 and 22.23%), fresh weight (19.97 and 22.97%)

and dry weight (15.71 and 21.37%), respectively as compared to control. Combination

application of water extracts of sorghum plant parts, stem+root water extract had highest

influence than stem+leaf or root+leaf and decreased weed density (23.42 and 33.42%), fresh

weight (25.64 and 33.78%) and dry weight (21.71 and 33.70%) as compared to control

measured at 80 and 105 DAS, respectively. Sorghum water extracts significantly reduced

population, fresh and dry weight of Anagalis arvensis L., Chenopodium album L., Fumaria

indica L. Mubeen et al. (2012) evaluated the impact of water extracts of crops on emergence

and growth of rice crop and its associated weeds. They showed that when both aqueous

extracts was applied in combine form got highest inhibition on T50, MGT of Eleusine indica.

Elahi et al. (2011) used aqueous extracts of herbage of different crops for managing weeds in

wheat. The author showed that combination of 1/3rd dose of isoproturon with all aqueous

extracts of sorghum, sunflower, rice and brassica spray after twenty five days of sowing,

reduced density and dry biomass of broad leaf weed (94-97% and 96-99%, respectively). It

was same to re-commended dose of iso-proturon with 98-99% reduction in weed density.

2.7.1.2. Sorghum crop residues

The intensive work on weed control by sorghum crop residues was published by

many researchers (Kim et al., 1993; Weston, 1996; Kohli et al., 2001). The researchers

observed the toxicity of sorghum crop residues differs with genotypes, plant parts, age of

17

plants, environmental factors, and target weed species. Sorghum crop residues were reported

either by soil incorporation or by killing the crop chemically or mechanically and leaving the

crop residues as mulch/cover on soil surface. During early stage of sorghum residues

decomposition maximum quantity of allelochemicals were rereleased in field soil (Alsaadawi

et al., 2007). Decomposition of sorghum residues at early stages significantly suppressed the

weeds, but in later stages, it enhanced the growth of crops. Due to their temporal nature of

sorghum residues can successfully be used to manage weeds in agro ecosystems. For

example, the inhibition of wheat germination under field condition by sorghum residues was

avoided by increasing seed rate, delaying plantation of next crop after residue decomposition,

(Al-Bedairy et al., 2012). In another experiment Roth et al. (2000) said that tilled residues

incorporation of sorghum crop in soil delayed subsequent crop growth. Sorghum stover after

no-tilled had little effect on crop growth but decreased yield of wheat crop, because of slow

leaching of allelochemicals. Weston and Czarnota (2001) reported that sorghum crop

residues of spring season reduced 90% weed weight in no-till soybean during summer

season. Under lab condition sorghum crop were used to check the suppression of weed

growth. The result showed that 26-56% total weed dry weight was reduced and increase 6-

17% wheat yield over control (weedy check) (Cheema and Khaliq, 2000). Two field

experiments were conducted by Cheema et al. (2004) for two years during summer of 1997

and 1998. They observed that mature sorghum herbage at 10-15 tons ha-1 was applied as

mulch reduced weed up to 25-36% and maize yield increased (35-42%).

In terms of crop production, competition of weeds, and fit to environment, many

sorghum genotypes were introduced. A field opinion by Al-saadawi et al. (2007) shown that

weed growth and density were changeed among selective genotypes. The result showed that

crop residues largely reduced weed growth. In all test genotypes phytotoxicity are differed.

Another experiment was conducted in field which was infested with different weed plants.

They observed that weed biomass and density were reduced by incorporation of residues in

soil @ 3 and 6 g kg-1 of soil. The residues of Giza 15, Giza 113 and Enqath provided 67, 59,

and 63% reduction in weed density and 58, 66, and 58% reduction weed biomass,

respectively. But the Rabeh cultivars suppressed average weed density and biomass by 41

and 52%, respectively. Weed density were significantly decreased with increasing rate of

incorporation of residues of most allelopathic cultivars in soil. Integration of various crops

18

(sorghum, sunflower, brassica) residues incorporation mixed can get maximum weed

reduction than alone incorporation (Khaliq et al., 2011). In a field of direct seeded rice,

sorghum residue at 8 tons ha-1 incorporation get 50% decrease in weed number and dry

biomass (Riaz, 2010). Aslam (2010) reported that by incorporation of wheat straw maximum

suppression of germination, growth and photosynthesis in horse purslane (broadleaf weed).

Hozayn et al. (2011) studied allelopathic effects of chopped sunflower and sorghum shoots

and roots on growth and emergence of Sakha-69 wheat cultivar and its associated weeds.

The results showed that maximum inhibition of seedling number and dry weight of grassy

weed (canary grass) was observed by sorghum crop residues than sunflower. Shoot residues

of sorghum crop gave maximum reduction in seedling density (43.55% and 72.00%

respectively) and dry biomass (62.90% and 73.08%, respectively) of wild oats and canary

grass.

2.7.2. Sunflower allelopathy

As a member of oil seed family, the value of sunflower crop has increased over the

last few years. It is also a big source of edible oil and protein. Due to its allelopathic effect,

its residue suppresses weeds by releasing allelochemicals (Narwal, 2004). Sunflower

allelopathy was firstly reported in wild species (Leather, 1987). A simple phenolics,

triterpenes, steroids, sesquiterpenes (mainly germacronolides guaianolides), flavonides,

heliannuoles, heliespirones and helikauranoside were isolated and identified by Macias and

his assistants from different cultivars of sunflower (Macias et al., 2008). Different sunflower

plant parts have different allele-chemicals. Ghafar et al. (2001) said that sunflower leaves

water extracts had chlorogenic, caffeic, vanillic, syringic and ferulic acids and in case of

sunflower stem water extracts chlorogenic, ferullic and vanillic acids were present. But in

sunflower root water extracts only ferulic acid was observed. Allelo-chemicals are released

in the form of extracts, exudates and leachates. Pseudo microgravity reduced the synthesis

and release of the allele-chemicals (Tomita-Yokotani et al., 2005). In this part of study,

allelopathic effect of sunflower water extracts and residues on weeds is reviewed.

2.7.2.1. Sunflower crop water extract

Due to high allelopathic potential sunflower crop plants efficiently affect the growth

of neighboring plants. Most of secondary metabolites, which are present in sunflower water

extracts having inhibitory effect and can be used as a ‘nature’s own herbicide’. In such type

19

of formulation, water act as a carrier and medium to show allelopathic activity (Farooq et al.,

2011a). Although there are several studies that make sure that organic solvents are also use

for extraction of secondary compounds besides water (Iqbal, 2007). Chlorogenic, caffeic,

vanillic, syringic and ferulic acids are present in water extracts of sunflower leaves and

chlorogenic acid, ferullic acid and vanillic acid in water extracts of sunflower stem. But only

ferulic acid is present in water extracts of sunflower roots (Ghafar et al., 2001). Naseem et

al., (2009) noted that water extracts of different sunflower plant parts reduced emergence and

seedling growth of Bromus japonicus Cheema et al. (2003) said that sunflower leaf and stem

water extracts showed significant effect on weed germination. However water extracts from

dried leaves and stems completely inhibit weed growth. Wild mustard germination was

inhibited (75%) by undiluted water extracts from leaves.

In another study, Anjum and Bajwa (2008) screened different cultivars of sunflower

crop on the bias of their suppressive effect against different weeds of wheat crop. They

observed a statistically significant interaction in between cultivars of sunflower crop and

weed flora. Suncross-42 showed highest allelopathic effect against all weed flora of wheat

crop. The weed suppression was increased with increase its concentration. Kamal and Bano

(2008) studied that water extracts of sunflower inhibited growth and emergence of seedlings

of crop. Mehboob et al. (2000) pointed out that sunflower water extracts suppressed

emergence and growth of Linum usitatissimum. Water extract of root, stem and leaf of

sunflower showed highest reduction in Medicago polymorpha dry weight (Anjum et al.,

2005). Ghafar et al. (2000) reported that sunflower extracts suppressed both radicle and

shoots growth of wheat. Highest sunflower water extracts concentration (10%) completely

inhibited mustard growth (Bogatek et al., 2006). A significantly reduction in parthenium

growth was observed by water extracts sunflower, sorghum and rice. At 50 and 100%

concentrations a significant reduction of root biomass of Parthenium was noted (Javaid et al.,

2006).

2.7.2.2. Sunflower crop residues

Sunflower residues suppressed growth and emergence of succeeding crops; in this

regard, the crops which are sensitive by sunflower should not be grown after sunflower.

While on the other hand, they inhibit a large number of weeds. Sunflower crop residue

20

inhibited the growth of guvar bean and pearl millet. This was due to phe-nolics released

during decomposition of sunflower tissues (Batish et al., 2002). Kaya et al. (2006) evaluated

the bio-degradation products of sunflower-heads (BPSH) at different concentrations on some

growth and emergence traits of common bean, chick pea and bread wheat. Emergence

gradually increased up to 10% BPSH and decreased at 100%. Soil incorporation both roots

and shoots of sunflower reduced grrmination, height and weight of wild barley. In lab,

sunflower aquous extracts caused high reduction hypocotyl length (44%), weight (57%),

radicle weight (61%), germination (68%), and length (79%) of wild barley.

Sunflower used as cover-crop//mulch reduced groth of weeds upto 85% (Fujii, 2001).

In another study, Khaliq et al. (2010) stated that a combine application of allelopathic crop

residues at 7.5 tons ha-1 gave highest suppression in weed number and biomass of horse

puslane and purpal nutsedge. Adding further, Matloob et al. (2010) conducted a pot study to

investigate the allelopathy of crop residues for the suppression of purple nutsedge. Chopped

crop residues were incorporated @ 12 tons ha-1 into the soil. Incorporation of all crop

residues delayed tuber sprouting. Khaliq et al. (2011) checked the impact of crop residues

(sorghum, sunflower and brassica) on rice crop and jungle rice weed (Echinochloa coloma L.

Link). With a combine application of crop residues final emergence of E. coloma L. Link

was reduced up to 8 to 34%. Alsaadawi et al. (2011) evaluated eight genotypes of sunflower

against weeds and wheat crop which is customarily grown after sunflower in crop rotation in

Iraq. They are revealed that all genotypes significantly suppressed weed number and

biomass. Out of eight genotypes, Sin- Altheeb and Coupan, were the most allelopathic

potential cultivars with the reduction in total weed number by 47.25 and 86.81% of control

and weed biomass by 74.23 and 80.79% of the control respectively. Euroflor and Shumoos

were the least allelopathic potential genotypes with an inhibition in total weed number by

21.50 and 9.59% and weed biomass by 42.28 and 33.67% of the control respectively.

Subsequent field experiment indicated that the residues of sunflower incorporated into the

field soil significantly inhibited total number of weeds grown in wheat field by 24.51-74.52%

of control at low residues rate (3 g kg-1 soil) and by 49.05-75.47% at the high residues rate (7

g kg-1 soil). Weeds biomass significantly reduced with a range of 12.27-64.52% at low

residues rate and 40.33-66.75% at high residues rate.

2.7.3. Brassica allelopathy

21

Brassicaceae family or cruciferous plants have rapid growth, biomass production, and

nutrient ability (Clark, 2007). Crucifers consist 375 genera and 3,200 species. Out of all, 52

genera and 160 species are present in Australia. The genus brassica consists of 100 species

including species i.e brown mustard, black mustard, white mustard, leafy turnip, canola,

abyssinian mustard and broccoliare. Thee are commonly known as major oilseed crop of

winter season and documented as allelopathic or weed suppressive crop (Narwal, 2001).

They synthesize secondary metabolites in considerably high concentrations (Warwick, 2011).

These secondary metabolites can be toxic to pathogens born in soil and crop pests like

nematode, fungus, and weed plants. They produce isothayanates, isoprenoid and benzenoid

(Cheema et al., 2007). Glucosinolates are also produced by intact plants. But at damaging of

plants, myrosinases (β-t hioglucosidases) come in contact with glucosinolates. A cleavage of

the glucose sulfur bond was done by these enzymes, which intern allows to convert

glucosinolates to iso-thiocynate, which cause weed plants and disease inhibition (Norsworthy

et al., 2006). Bressan et al. (2009) stated that a competitive biocidal effect was started when

catabolic products like glucosinolate released to the rhizosphere of soil. The allelopathic

effect of brassica water extracts and residues on weeds is reviewed in the following section.

2.7.3.1. Brassica crop water extract

In agro ecosystems, use of crop water extract is a best way to employ allelopathy for

weed management (Jamil et al., 2009). In many studies, water extracts of brassica crop

showed best results. The impact of mustard crop on wild oat growth and germination was

studied by Turk and Tawaha (2003). The study showed that water extracts of whole mustard

plant and its parts inhibited germination of wild oat. A one more study by Ar-slan et al.

(2005) stated that rape-seed shoot and tur-nip root water extracts reduced the ger-mination of

ground-cherry. D’Abrosca et al. (2004) reported lignans; it showed significant reduction in

germination of lettuce plants. Narwal et al. (2004) reported that few species of Brassica

juncea and Brassica nigra gave maximum suppression (75 to 82%) at 75 DAE (days after

emergence) and 75-98% at harvesting (120 D) in winter weed densities. Mushtaq et al.

(2010) tested the efficacy of mixed water extracts crop plants on horse purslane weed. 100%

mixture of allelopathic crop plant water extracts significantly inhibits emergence and growth

of horse purslane. In pot study, two exogenous spray of water extracts of crop plants like

sorghum, sunflower, brassica and mulberry also reduced weed growth and its dry matter. In

22

another study, Awan et al. (2012) stated that a mixture of concentrated water extracts of

allelopathic crops, gave highest reduction in weeds at 45 and 75 days after sowing.

2.7.3.2. Brassica crop residues

Matloob et al. (2010) stated that crop residues application can pose an allelopathic as

well as a physical effect on the growth of succeeding crop and weed plants. In field

experiment, rapeseed residues incorporation after cotton reduced germination of Amaranthus

spp. while cotton germination remained unaffected; this was due to the release of organic

substances which have inhibitory effect by decomposition of rape seed plants (Younesabadi,

2005). A large amount of isothiocyanates (ITC) was released from surface mulch of Brassica

rapa which has been described by (Fahey et al., 2001). Petersen et al. (2001) check the

allelopathy of isothiocyanates (ITC). From chopped mulch of brassica six different ITCs

were identified. Aryl-ITCs compound showed maximum inhibition. Small seeded weed

floras were more sensitive to ITC than those which have large seeds. At higher concentration

of ITCs compound in chopped mulch caused high weed suppression in the field. Turk and

Tawaha (2003) studied an experiment to check the allelopathic potential of black mustard

against wild barley. They observed that wild barley growth was highly suppressed when

grown in soil which was incorporated with cropped black mustard. Soil incorporation of both

roots and shoots of black mustard reduced emergence, height and weight of wild barley as

compared with control (no residues).

2.8. Allelopathic effect of crop plants on rhizosphereA chemical interaction among plants which is facilitated by release of allelochemicals

in rhizosphere soil is known as allelopathy (Bertin et al., 2003). The ecology of rhizosphere

soil may influenced by root exudates, increasing population of certain micro flora, resulting

in a shifting of nutrients into available form and their uptake by plant community of eco-

system (In-derjit and Weston, 2003).

2.8.1. Allelopathic effects of crops on microbial diversity in rhizosphere soil

Fertility of soil is depended on presence of sufficient nutrients, number and diversity

of soil micro flora. Diversity in microbes is due to the occurrence of defferent types of

organic substrates in the soil. These diverse groups of organisms consist of unicellular

23

prokaryotes or eukaryotes like bacteria, cyano-bacteria, action-mycetes, fungi and algae.

These microbes perform a number of activities required for the proper functioning of the soil

as a dynamic system. The allelo-chemicals are important, but their effect on living organisms

in rhizosphere soil which is known as non-target organisms. The disturbance in non-target

organisms is of great concern because this poses a risk to entire ecosystem. Microbial

community is very diverse, and its composition varies in space and time. Among the soil

biota with relevance to allelopathy are the many free-living and symbiotic bacteria and fungi

that are present in rhizosphere and mycorrhizosphere (Johansson et al., 2004). Plant

allelopathy can be modified by the presence of microbial community, and sometimes

microbes are negatively affected by allelo-chemicals. It is known that plant species will

culture specific microbial communities in their rhizosphere that have consequent responses

on hetero-specific and con-specific individuals grown in same soil. This effect depedes on

the amount and form of carbon and other nutrients that are delivered by plant to the soil, but

direct positive and negative effects on soil microbes are also due to allelo-chemicals

(Reinhardt and Callaway, 2006).

2.8.2. Allelopathic effects of crops on enzymatic activity in rhizosphere soil

Activity of enzymes in rhizosphere soil is an important indicator of nutrient cycling

and fertility status, particularly in long term farming systems (Bohme et al., 2005). Roots of

plants have a various types of secondary metabolites like simple as well as complex

carbohydrate, long and short chain protein, vitamins and amino acids (Bacilio-Jimenez et al.,

2003). Few organic compounds which are exude by plant roots can act as allelochemicals.

They participate in interactions between crop plants and other living organisms in the

rhizosphere of soil (Bais et al., 2006). The soil enzymes activity and allelochemicals

concentration in rice field which was amended with different rice varieties, different growth

stages and moisture percentage. A higher level of soil enzymes like urease, invertase, de-

hydrogenase and polyphenol oxidase activity was observed in paddy field in which

allelopathic rice variety (PI312777) was sown as compared to paddy field with Liaojing-9

which is non-allelopathic rice variety (Gu et al., 2009). Zhang et al. (2000) reported that

poly-phenol oxidase correlate with the accumulation of phenolic acid and had high activity in

re-planted soybean soil than virgin soil, that was because the poly-phenol oxidase activity

was enhanced under the induction of phenolic acid, and the poly-phenol oxidase enhanced

24

the activity of growth hormone oxidase in the plant which could decompose the growth

hormone and affect the plant growth. Benzoic and ferulic acids are ubiquitous allelo-

chemicals responsible for rice allelopathy in paddy eco-system.

2.8.3. Allelopathic effects of crops on physicochemical properties in rhizosphere soil

Exudation of secondary metabolites (allelochemicals) through roots is a main way by

which allelochemicals are released into rhizosphere soil (Kobayashi, 2004). It is the fact that

30% of plant's photo assimilates are consumed in root exudates production, which affect the

environment of local soil (Bertin et al., 2003). A cellular transport system which depends

upon localized soil conditions, especially stress factors cause synthesis and release of

allelochemicals from root cells into the soil (Weston et al., 2012). Soil factors such as

mobility; movement and uptake of allelochemicals in the soil make the phenomenon of

allelopathy in the soil is more complicated because they are strongly linked with soil

properties, as well as soil moisture conditions, which in turn affect their adsorption and

degradation (Kobayashi, 2004). Characteristics (chemical, and biological) of the rhizosphere

soils can be very different from the bulk soils. Secondary metabolites in root exudates and

microorganisms contain various organic compounds which have low molecular weight and

their concentrations in exudates showed greater variations between cultivars. These

variations may be due to physiology of plant, roots growth, metabolism rate, root distribution

and micro flora. In one study, physic-chemical status of rhizosphere soil was analyzed from

the samples which were collected before sowing of sunflower crop and at harvesting of

sunflower as well as wheat crop. Kamal and Bano (2008) commonly observed that electric

conductivity (EC), calcium (Ca), phosphorus (P), lead (Pb) and soil moisture contents were

decreased, while the soil pH, manganese (Mn), iron (Fe), magnesium (Mg), potassium (K)

and zinc (Zn) were increased.

2.9. ConclusionGenerally, allelochemicals are naturally present in all field crops. These chemicals are

released into rhizosphere soil by various mechanisms like crop residues decomposition,

volatilization, and exudation through root. Use of herbicides and pesticides can be reduced

by proper use of crops which have allelopathic potential in agri industry. By application of

such crops can reduce cost, as well as food and environment pollution. It also improves soil

productivity as well as bio-diversity and sustainability in the agro-ecosystem. Sorghum,

25

sunflower and brassica crops have ability to manage weeds by releasing various allelopathic

compounds in the soil of rhizosphere. Crop allelopathy offers a new concept to discover

natural pesticides.

26

Chapter-III

MATERIALS AND METHODS

A series of trials were conducted under lab as well as in field to observe allelopathic

impacts of crops (sorghum, sunflower and brassica) on weed dynamics, rhizospheric

microbial population, enzymatic activities, nutrient dynamics and productivity of spring

planted mung bean (Vigna radiata L.). The detail information of site and materials which are

used during the course of these studies are given below.

3.1. General informationsThe detail of experiments is given in the following sections:

3.1.1. Experimental site, soil, treatments and statistical design

The experiment was con-ducted at Student Research Farm, Department of Agronomy,

University of Agriculture Faisalabad, Pakistan (Latitude = 31o-26' N, Longitude = 73o-06' E,

Altitude = 184.4 m). According to the climate data collected during both years of

experimentation (Figure 1), the total monthly rainfall of the area during experiment duration

ranges from 42 to 58 mm in 2014 and from 68 to 128 mm in 2015. Mean monthly maximum

temperature was 34oC and the minimum being 22oC during experiment duration of both

years. The mean relative humidity was 47% during experiment duration of both years.

The soil belonged to the Lyallpur series. According to USDA (US Department of

Agriculture) classification it was arid sol-fine-silty, hyper thermic Ustalfic, mixed and

Haplargid, but in FAO (Food and Agriculture Organization) classification system, it was

classified as Haplic Yermosols.

The treatments comprised of a control (plots with no crop residues or extract

application), sunflower water extract at 10 L ha-1, sunflower water extract at 20 L ha-1,

sunflower residue incorporation at 4 tons ha-1, and sunflower residue incorporation at 6 tons

ha-1.

The experiment was laid out in a randomized-complete-block-design (RCBD) under

factorial arrangement with 3 replications. The plot size was maintained at 3.0 m × 5.0 m.

27

3.1.2. Crop husbandry

To prepare seed bed two times ploughing with tractor mounted plough followed by

one planking was done. The seed of mung bean cv. “NM-92” was collected from National

Institute of Agriculture and Biology (NIAB), Faisalabad. Sowing was done at 15 th March and

20th March during 2014 and 2015, respectively. A recommended rate of mung bean seed (25

kg ha-1) was used to maintain plant population (350,000 plants ha-1) in 30 cm apart rows. Àll

other field practіces accept those which under study were kept same for all experimental

units.

3.1.3. Source and preparation of experimental treatments materials

The material of various crop plants as sorghum, sunflower and brassica were

collected from Student Research Farm of the Department of Agronomy, University of

Agriculture, Faisalabad and was used in residues and water extract preparation for field as

well as laboratory experiments.

Crop plants residues were prepared and used by following way:

A mature crop plants were harvested, dried under shady conditions and cut into 2-3

cm pieces with electric fodder cutter. These pieces of crop residues were incorporated prior

to sowing as per treatments (4 and 6 tons ha-1).

Crop plant extracts were pre-pared and used by following ways:

Water extracts of different crop plants were prepared and used by following way. The

pieces of crop residues were put in distilled water with 1:10 w/v ratio for twenty four hours.

The filtrate was used as fresh. Crop plant water extracts @ 10 and 20 L ha-1 were sprayed 15

days after sowing. The spray was performed with T-jet nozzle. The volume (300 L ha -1) of

spray which was used for spraying was determined by calibration.

3.1.4. Rhizosphere soil sampling

Soil sampling from mung bean rhizosphere was done after 20 days of crop sowing

and at the time of harvesting. Randomly 10 plants were selected from each plot. The whole

plant was carefully up rooted using a spade. The closely adhering soil was collected in plastic

bags. A representative sample for analysis was made by mixing all these randomly taken

samples from each treatment. Collected soil samples were handled thoroughly by drying,

grounding and sieving with 2 mm sieve and then analyzed for soil properties except

microbial population, alkaline phosphatase and dehydrogenase activity. For detecting

28

Table. 3.1. Response of soil properties, nutrient dynamics, soil enzyme activities and

microbial populations of the experimental soil before sowing (2014 and 2015)

Soil physical, chemical and biological properties 2014 2015Bulk density (BD) (g cm-3) 1.48 1.45Soil porosity (%) 43.1 44.3Soil pH 7.85 7.79Electrical conductivity (EC) (dS m-1) 1.11 1.19Soil organic matter (SOM) (%) 0.53 0.61Available phosphorus (AP) (mg kg-1) 6.74 6.95Available potassium (AK) (mg kg-1) 123 131Soil nitrogen (N) (g kg-1) 0.24 0.29Bacteria (cfu/g x 105) 35 45Fungi (cfu/g x 104) 5 8Microbial activity (mg CO2-C kg-1 d-1) 3.05 3.14Alkaline phosphatase activity (μg NP g-1 soil h-1) 135 143Dehydrogenase activity (μg TPFg-1 soil h-1) 21 25

March April May Jun July March April May Jun July2014 2015

0

20

40

60

80

100

120

140

0

10

20

30

40

50

Rain Fall Relative Humidity Max. Temp. Min. Temp.

Rai

n Fa

ll (m

m) R

elat

ive

Hum

idity

(%)

Max

. and

Min

Tem

pera

ture

oC

Figure. 3.1. Weather data during the period of study (2014 and 2015)

29

alkaline phosphatase, dehydrogenase as well as microbial population, the samples were

preserved at 4oC. Before starting experiment three samples (0-15 cm) were taken from

different places of each experimental unit and then mixed to make a test sample for soil

analysis. The initial properties of original experimental site were tested and were shown in

Table 1.

3.2. Details of experiments The study consists of a number of lab and field experiments. Detail of each experiment is

separately given as under:

3.2.1. Details of field experiments

Crop husbandry: Mung bean cultivar NM-92 was.sown in 30 cm spaced in rows.with the

help of drill on 15th and 20th of March in 2014 and 2015. Nitrogen (N), phosphorus (P) and

potash (K) were used at 23 kg (N), 58 kg (P2O5) and 63 kg (K2O) ha-1 in the form of Urea and

Di-ammonium Pho-sphate (DAP) and Sul-phate of Potash (SOP), res-pectively. Full re-

commended doses of P, K and one third of N in the form of D.A.P., S.O.P. and Urea,

respectively were drilled at sowing. Two third of the N was applied in two equal splits, i.e.

1//3rd at first irrigation and remaining 1//3rd as top dressing at second irrigation.

First irrigation (7.50 cm) was applied after 10 days of crop sowing. While, sub-

sequent irri-gations were applied as and when crop needed. To maintain plant to plant

distance (10 cm) and plant population thinning was done. Manually crop harvesting was

done when evidence of physical maturity appeared. Plants were tied into small piles and kept

it for sunn-drying to mini-mize moi-sture-contents of seeds and easy she-lling. After certain

period of sunn-drying, the pods were-shelled manually to separate seeds from pods.

Pro-cedure for field trials: Field ex-periments were conducted in a randomized-complete-

block-design (RCBD) under factorial arrangement with 3 re-plications having a net plot size

of 3×5 m2. Crop water extract were prepared 1:10 w/v ratio (Cheema.and Khaliq, 2000). For

foliar app-lication volume of spray (300 L ha-1) was calibrated by using or-dinary water.

Crop plant water extracts @ 10 and 20 L ha-1 were sprayed 15 days after sowing and crop

residues were incorporated prior to sowing as per treatments (4 and 6 tons ha-1) to manage

weeds in mung bean crop during 2014 and 2015. Spray was applied in the respective plots by

using the knap sack hand sprayer which was fitted with T-Jet Nozzle.

The experimental detailes are given as under:

30

Experiment-I: Effect of sorghum crop water extract and residues on weeds,

productivity and rhizosphere of mung bean (Vigna radiata L.)

The experiment was comprised of following treatments:

Treatments:

T1= Control (Plots with no crop residues or extract application)

T2= Sorghum water extract @ 20 L ha-1

T3= Sorghum water extract @ 10 L ha-1

T4= Sorghum residue @ 4 tons.ha-1

T5= Sorghum residue @ 6 tons ha-1

Experiment-II: Effect of sunflower crop water extract and residues on weeds,


The experiment was comprised of following treatments:

Treatments:


T2= Sunflower water extract @ 20 L ha-1

T3= Sunflower water extract @ 10 L ha-1

T4= Sunflower residue @ 4 tons ha-1

T5= Sunflower residue @ 6 tons ha-1

Experiment-III: Effect of brassica crop water extract and residues on weeds,


The experiment was comprised of following treatments:Treatments:


T2= Brassica water extract @ 20 L ha-1

T3= Brassica water extract @ 10 L ha-1

T4= Brassica residue @ 4 tons.ha-1

T5= Brassica residue @ 6 tons.ha-1

Observations:

The following observations were recorded during the experimentation:

Weed data:

The following observations on weed studies were recorded after thirty days of sowing:

31

Total weeds density (0.25/m2): Density of weeds (individual and total) was noted from

randomly selected area (0.25/m2). From each plot 2 samples were taken.

Total weed fresh weight (g/0.25 m2): For determining the individual and total fresh weight

of different weeds. Harvesting of weeds was done from ground. Weed plants were cleaned

and then record their weight. Two samples were taken from each experimental unit.

Total weed dry weight (g/0.25 m2): For determining the individual and total dry weight of

different weeds, weeds were harvested at ground level. From each exp-erimental unit 2

samples were taken and thent oven dried at 65oC to recorde their dry weights.

Crop data:

The following observations regarding crop were studied at the time of harvesting:

Emergence and morphological traits: The following observations on emergence and

morphological traits were recorded at the time of harvest:

Final emergence count per plot: On the completion of germination number of emerged

seedlings was counted from individual plots and average was worked out to get final

emergence per plot.

Plant he-ight at mat-urity (cm): Height of five plants was measured from base of the plans

up to growing tip of main stem. The average plant he-ight was cal-culated by taking the

mean of observation of five plants and expressed in cm.

Pod length (cm): Randomly five plants were selected from each plot for measuring pod

length. Pod length of each plant was measured. The average pod length was calculated by

taking the mean of observation of five plants and expressed in cm.

Number of branches per plant: Randomly five plants were selected from each plot for

counting number of branches. The branches of each plant were counted carefully and

averaged to obtain the number of branches per plant.

Number of nodules per plant: Five plants were sampled randomly from each plot. The

whole plant was carefully up rooted using a spade so as to obtain intact roots and nodules.

Uprooting was done by exposing the whole root system to avoid loss of nodules. The

adhering soil was removed by washing the roots with intact nodules gently with water over a

metal sieve. The number of nodules per plant was determined by counting the number of

nodules from all the five uprooted plants per plot and then averaged as per plant.

32

Stalk yield (kg/ha1): Stalk yield was obtained by sub-tracting the grain-yield from bio-

logical yield.

Yield and yield components:

The following observations on yield and yield components were recorded at the time of

harvesting:

Number of pods/plant: Total pods on the ran-domly selected five plants was counted and

averaged to obtain the number of pods per plant.

Number of seeds per pod: The ten randomly selected pods from each randomly selected

five plants per plot were taken out and a total seed was counted. Average number of seeds

was cal-culated and recorded.

Weight of 1000-seeds (g): Randomly selected 1000 seeds from the seed yield samples of

crop were counted from each plot and their combined weight was recorded to get weight of

1000-seeds.

Bіologіcal yіeld (kg ha-1): At maturity plants were harvested, tied into small bundles and left

for drying in their respective plots. After that weighed was done with help of a spring-

balance to determine the total sun dried biomass (biological yield) per plot. This was con-

verted into kg/ha.

Har-vest in-dex (%): Har-vest in-dex was cal-culated by dividing the e-conomic or

economic/seed yield by the bio-logical yield or total biomass and multiplying by 100.

Harvest index (%)=Economic∨seed yield (kg/ha❑)

Bio− logical yield∨total biomass (kg /ha❑)× 100

Yield (kg ha-1): The seed yield of net plot after cleaning and proper drying was recorded in

gram and converted to kg ha-1 by multiplying with appropriate conversion factor.

Rhizosphere soil analysis:

The following analyses were recorded from the samples of rhizosphere soil which were

collected after twenty days of sowing and at the time of harvesting.

Soil physical analysis:

The following traits on soil physical analysis were recorded from the samples of rhizosphere

soil which were collected at the time of harvesting:

Soil bulk density (g cm-3): Soil bulk density (BD) is the ratio of oven-dry weight of soil to

bulk-volume expressed in grams per cubic.cm (g.cm-3). It is an indicator of soil structure and

33

void space. It was determined by the method which was described by Blake and Hartge

(1986). Each bulk density core was split and weighs the cores with their soil. Put the core

with the foil in the drying oven at 105oC to constant weight. After drying, removed the cores

from the drying oven and then oven-dried weight was recorded and then determined the

volume of each cylinder.

The density of soil was calculated as follows:

BD ( g . cm−3 )=Weight of ovendry soil∈grams [ (c )− (a ) ]Volum of core∈cm3

Where (a) is the mass of each empty core and (c) is the mass of each core with its dry soil

Total soil porosity (%): The total soil porosity (TSP) was estimated using the relationship

between bulk density and particle density (Vomocil, 1965).

The total soil porosity was calculated as follows:

Total soil prosity (% )=[1−( BDPD

)]×100 %

Where BD (g cm-3) is bulk density and Pd is particle.density (g.cm-3).

Soil chemical analysis:

The following parameters of soil chemical analysis were recorded from the samples of

rhizosphere soil which were collected at the time of harvesting:

pH of saturated paste (pHs): A saturated.soil paste with 250 g soil and water was prepared

to measure the pH of soil. 1:2 ratio (soil/water) of suspension was used. After one hour

preparation of paste soil pH value was recorded by a pH meter (model Kent Eil 7015, US

Salinity Lab. Staff, 1954).

Electrical conductivity (dS m-1): For determining electrical conductivity (EC), extract of

soil paste with 1:2 ratio (soil/water) of suspension was obtained by using vacuum pump. EC

value was determined using a digital Jen way conductіvіty meter (modell 4,510, U.S. Salіnіty

Lab. Staff, 1954).

Total soil organic.matter (%): Total soil organic.matter was determined by a method which

was described by Walkley and Black, (1934). Add 5 mL of 0.4 N potassium dichromate

solution (K2Cr2O7) followed by 10 mL sulfuric acid (concentrated) in a fully dried samples of

known amount (20-50 mg). Soil solution was swirled and left for 16-18 hrs at room

temperature in a fume hood and then, add 100 mL of distilled water to solution. For di-

34

chromate excess potentio metrically back-titration was done with 0.2 N ferrous.ammonium

sulfate. (Fe(NH4)2(SO4)2 × 6H2O). At the beginning of the batch analysis blank-titration of

acidic di-chromate with ferrous ammonium sulfate was performed by using the same

procedure. One mL of ferrous ammonium sulfate (0.2 N) is equivalent to 9.81 mg of K2

Cr2O7 or 0.6 mg of carbon. Organic carbon content in the sample was calculated as:

M= 10V blank

Oxidizablorganic carbon(%)=[V blank−V sample ] ×0.3 × M

Wt

Total organiccarbon (% )=1.334 × Oxidizable organic carbon(%)

Organic carbon (% )=1.724 Total organic carbon(% )

M= molarity of (NH4)2 SO4 FeSO4 6H2O solution (about 0.5.M)

V= blank is volume of (NH4)2 SO4 FeSO4 6H2O solution required to titrate the blank (mL)

Wt= is weight of air dry soil (g)

0.3= is 3 × 10-3 × 100, where 3 is the equivalent weight of C

Total soil nitrogen (g kg-1): Total soil nitrogen was determined by sulfuric acid digestion

and Kjeldahl distillation (Bremner amd Mulvaney, 1982). Sample of 1 g soil was taken and

placed in K-jeldahl flask. Then copper sulphate (0.7 g), K2 SO4 (1.5 g) and H2 SO4 (30 mL)

were added. Heat the solution til frothing was ceased then boiled briskly until solution

became clear. After that continue digestion was done for 30 min. 50 mL H2O was added and

shifted to dis-tillation flask. In conical flask 20 to 25 mL standard acids (0.1 M HCl or 0.1 M

H2SO4) was taken accurately. Two to three dropes of indicator (methyl-red) was added. Then

30 mL (NaOH-35%) was added in dis-tilling flask. To distill ammonia, contents were heated

for 30-40 min. Receiving-flask removed and washed out-let tubes into re-ceiving flask with

di-stilled water. Excessive acid tit-rated in the dis-tillate with (NaOH-0.1 M). Blank on re-

agents determined using same quantity of standard acid in a r-eceiving flask.

The total soil nitrogen was calculated as follows:

Total soilnitrogen (gkg−1)=1.401 (V 1−V 2 M2 )−(V 3 M3−V 4 M 4 ) ×df

W

Where:

V1= mL of standard acid in r-eceiving flask

V2= mL of Na.OH used in tit-ration

35

V3= mL of stan-dard acid to r-eceiving flask (blank)

V4= mL of Na.OH for tit-rating (blank)

M1= standard-acid mo-larity

M2= NaOH mol-arity

W= sample weigh (1 g)

df= sample di-lution factor (if 1 g the di-lution factor will be 100)

Note= 1000 ml of HCl (0.1 M) or H2SO4 (0.1 M) = 1.401 g N

Available phosphorus (mg kg-1): Firstly we prepared a st-andard curve. A pure dry K-

H2PO4 (0.1916 g) was di-ssolved in 1-L DW (di-stilled water). This solution also con-tained

P2.O5 mL-1 (0.10 mg). Then this solution was preserved as a s-tock sol-ution of pho-sphate.

10-mL of stock sol-ution was taken and dil-uted it with 1-L of DW. This solution contained

1-µg P2O5 mL-1 (0.001 mg P2.O5 mL-1). 5 volumes i.e., 1-2-4-6 and 10 mL of this solution was

taken in separate flasks (25 mL). In each flask, add 5-mL.of extractant solution and 5-mL of

m-olybdate r-eagent. Then dilute it by adding 20-mL DW. 1 mL of SnCl2 (d-ilute solution)

was added and shook again dilute it up to 25 mL mark. After ten minutes, blue color of

solution was read on spectro-photometer (660 nm wave-length). The absorbance reading was

plotted against µg P2O5 and joined the points.

Known amount (5 g) soil was extracted with NaHCO3 (0.5 M) solution having 8.5 pH

8.5. Then 5 mL aliquot of c-lear fil-trate was taken in 25 mL vo-lumetric flasks and added 5

mL of colour de-veloping r-eagent. Volume was made up to mark with DW and reading was

recorded by spectro-photometer model-Nico-let-evo-lution-300, Ther-mo Co. Ltd. (O-lsen

and So-mmers, 1982).

Available potassium (mg kg-1): For s-tandard curve flamephoto-meter was set by the a-

tomizing “0” and “20” µg K/mL solutions a-lternatively to “0” and “100” reading. Inter-

mediate s-tandard solu-tions were atomized and recorde the reading. Reading was plotted

against respective potassium contents. The point connected with s-traight line to obtain a s-

tandard curve. Known amount of soil (5 g) was sat-urated with 50 mL of ammo-nium acetate

(1 M) solution having 7.0 pH. Same solution was used for extraction. Available K was

determined by flame photometer (Sherwood-410) (Helmke and Sparks, 1996).

Soil biological analysis:

36

Microbial populations: Microbial populations were determined by serial dilutions of each

soil sample on agar plates. Total number of culturable bacteria was determined on R2A (half-

strength) agar plates (Janssen et al., 2002; Wu et al., 2004; Aslam et al., 2008) and all

culturable fungi were plated on rose bengal potato dextrose agar (Martin, 1950). Colony

counts were carried out after 48 hours of culturing. Further purification was done by repeated

streaking. Pure bacterial strains were grown on R2A (half-strength) and preserved in 400-µL

glycerol and 600 µL nutrient broth-cultures in 1 mL Eppen-dorf at -40oC.

Microbial activity (mg CO2 C/kg/d): In the present study, microbial respiration of rhi-

zospheric soil was measured as inn-vitro static CO2-evolution. Inoculated soil was incubated

in conical flasks with 5% organic matter amendment. The CO2 evolution was measured by

acid-base titration and expressed as mg CO2-C kg-1 d-1 (Stozky, 1965).

Enzyme studies:

The following parameters on soil enzymes studies were recorded from the samples of

rhizosphere soil which were collected at the time of harvesting:

Phosphatase activity (alkaline) (μg NP g-1 soil h-1): Tabatabai and Bremner (1969) method

was used to estimate rhizosphere phosphatase activity (alkaline). For soil sampling plants

were uprooted and the soil closely attached with roots was collected by shaking roots over a

2 mm sieve and then it was stored at 4oC. Known amount of rhizosphere soil (1 g) was added

in Erlenmeyer flask (50 mL). After that add toluene (0.2 mL) followed by modified universal

buffer “MUB” (4 mL). For the preparation of MUB tris-hydroxymethyl amino methane (12.1

g), maleic acid (11.6.g), citric acid. (14.g) and boric acid. (6.3.g) was added in 488.mL of 1.N

NaOH.and dilute it to 1.L with water. The pH of solution was 11. After adding 1 mL of p-

nitrophenyl phosphate solution (pNPP) it incubated for 1 hr at 37°C in dark. After 1 hr, 1 mL

of CaCl2 (0.5.M) and 4.mL of NaOH (0.5.M) solutions were added. After that contents of

assay mixture were filtered (Whatman No.2). A spectro-photometer (Model at 400 nm) was

used to measure the absorbance of yellow colour of the filtrate. P-.nitrophenol content of

filtrate was calculated by calibration.graph plotted from results obtained with

standards.containing (0, 10, 20, 30, 40 and 50 µg p-nitrophenol). For control, same procedure

was followed, but addition of CaCl2 (0.5 M) and 4 mL of NaOH (0.5 M) (i.e. immediately

before filtration of soil-suspension). Phosphatase-activity was noted by following equation:

37

P−nitrophenol ( μg g−1 dwt h−1 )=C × Vdwt

× SW ×t

Where,

C= known p- nitrophenol (µg mL-1) concentration

Dwt= weight (dry) of 1g moist soil

V= soil suspension volum (mL)

SW= sample weight (1 g)

t= incubation time (1 h)

Dehydrogenase activity (μg TPF/g soil/h): De-hydrogenase activity determined as

described by Min et al. (2001). A 5 g soil was added in Erlenmeyer flask. After that 5 mL

Tri-phenyl Tetr-azolium Chloride (TTC) solution which contained 5 g TTC in Tris-HCl

buffer (0.2 M) having 7.4 pH was added in flask and then put it for incubation for 12 hrs at

37°C. After incubation period 2 drops of sulfuric-acid (concentrated) was added to end the

reaction. Then samples were mixed with 5 mL of toluene and shake them at 250 revolutions

per minuets (rpm) for 30 min, followed by centrifugation (4,500 rpm) for 5 min to extract

Tri-phenyl-forma-zon (TPF). O-ptical density (OD) of super-natant was measured by a

spectro-photometer (492 nm). De-hydrogenase-activity was expressed as μg TPF g-1 soil 12

h-1.

3.2.2. Details of laboratory experiments

Experiment-IV: Isolation of allelochemicals resistant strains of bacteria and

determination of their active role in rhizosphere

Materials and reagents: Soil samples for isolation of bacterial strains were obtained from

rhizosphere soil of mung bean field amended with water extracts and residues of allelopathic

crops (sorghum-sunflower-brassica) at Student Research Farm, Department of Agronomy,

University of Agriculture Faisalabad. Chemical compounds of p-coumaric, ferulic and

syringic acids are Sigma products (Sigma, St Louis, MO). For growth tests the seed of mung

bean cultivar “NM-92” was collected from National Institute of Agriculture and Biology

(NIAB), Faisalabad.

Isolation and purification of bacterial strains from soil samples: For isolation of

culturable bacteria, 1 g of soil was added to 10 mL of 50 mM phosphate buffer (pH 7) and

50% of the soil mixture was treated by sonication with an electronic homo-genizer (Bandelin

38

Sonoplus, Berlin, Germany) at 260 W cm-2 for 15 sec. After mixing both sonicated and non-

sonicated portions, serial dilutions were made. The diluted aliquots were spreaded on half-

strength R2A agar plates (Janssen et al., 2002; Wu et al., 2004; Aslam et al., 2008). A 100

µL aliquot was applied to a large poly-styrene petri-dish (15 cm diameter) for each dilution

of 10-4 and 10-5 and the plates were incubated at 28°C for 3-6 days. The bacterial isolation

media was supplemented with 40% (v/v) of soil extract and Ampho-tericin B at 50 µgmL-1 to

inhibit fungal growth. Six plates were used per dilution. The plates were dried in a laminar

flow cabinet for 1 hrs and then incubated. The colonies were selected on the basis of

morphology and isolates were sub-cultured on half-strength R2A agar. The single selected

colonies were transferred to small polystyrene petri-dishes and checked for their purity.

Further purification was done by repeated streaking. Pure bacterial strains were grown on

half-strength R2A and preserved in 400 µL glycerol and 600 µL half-strength R2A cultures in

1 mL Eppen-dorf at - 40oC.

Tests of bacterial resistance against synthetic allelochemicals and allelopathic crops

water extracts: For testing bacterial resistance against allelochemicals all bacterial isolates

were cultured in sterilized half-strength R2A agar medium in petri plates. 200 µL of each

bacterial isolate was inoculated on agar plates and then spread out on whole plate with help

of sterile glass spreader. Solutions of synthetic allelochemicals viz. p-cumaric, ferulic and

syringic acid were prepared as 1 g L-1 and allelopathic crops (sorghum, sunflower and

brassica) water extracts were prepared as 1:10 (w/v). Disc diffusion method was used to

apply the allelochemicals. 20 µL solution of each allelochemical and crop water extract was

deposited on a sterile paper disc (6 mm in diameter) and the discs were dried in an incubator

at 50oC. Then the discs were placed on the surface of petri plates containing bacterial

isolates. Each plate contained six discs, three containing the synthetic allelochemicals and the

other three containing allelopathic crops water extracts. The plates of bacteria were incubated

at 28oC for 48 hrs, and any zone of inhibition was interpreted as a sensitive and no zone of

inhibition was interpreted as resistant (Cetin, 1973).

Biochemical characterization:

The following observations regarding biochemical characterization were studied during the

experimentation:

39

Colony morphology: Colony morphology viz. size, color and shape were recorded after 24

hrs of growth on half-strength R2A agar plates at 28 ± 2oC (Wu et al., 2004; Aslam et al.,

2008).

Cellulase activity: To check the cellulase activity an aliquot of 100 µL of each bacterial

isolate was spot inoculated on agar plates containing CMC (10 g L-1), K2HPO4 (1 g L-1),

KH2PO4 (1 g L-1), MgSO47H2O (0.2 g L-1), NH4NO3 (1 g L-1), FeCl36H2O (0.05 g L-1), CaCl2

(0.02 g L-1), and agar (20 g L-1) (Atlas, 2004; Lo et al., 2009). The plates were incubated at

28oC for 96 hrs. After incubation period each plate was flooded with 0.1% Congo red for 15

to 20 min and then 2-3 time washed with NaCl (1 M). A clear zone around the colonies

showed there activity.

Bioassays.for plant.growth.promoting.traits:

Following bioassays.for plant growth.promoting traits were studied during the

experimentation:

Zinc solubilizing activity: For zinc (Zn) solubilizing activity 100 µL of each bacterial

isolate was inoculated on agar pates consist of glucose (10 g L-1); (NH4)2SO4 (0.5 g L-1); KCl

(0.2 g L-1); MgSO4 7H2O (0.1 g L-1); trace of MnSO4 and FeSO4, yeast.extract (0.5 g L-1); and

agar (15 g L-1) supplemented with 0.1% Zn (oxide and carbonate). After 7-10 days of

incubation (28 ± 2oC) formation of halo zone around the colonies were observed.

Phosphate solubilizing activity: Each bacterial.culture (100 µL) was spot.inoculated in the

center of agar plates containing tri-calcium phosphate {Ca3 (PO4)2} as insoluble phosphate

source with the following ingredients: glucose (10 g), (NH4)2SO4 (0.5 g), NaCl (30 g), KCl

(0.3 g), FeSO4 7H2O (0.03 g), MnSO4 4H2O (0.03 g), MgSO4 7H2O (0.3 g), Ca3 (PO4)2 (10

g), and agar (20 g), H2O (1000 mL), pH (7.0-7.5) (Pikovskaya, 1948). After 7-10 days of

incubation (28 ± 2oC) formation of halo zone around the colonies were observed.

Nitrogen fixation activity: For media preparation dissolved FeCl3 6H2O (0.05 g) in distilled

water (500 mL). Added K2HPO4 (2 g), MgSO4 7H2O (0.25 g) and glucose (10 g). After that

poured into a bottle containing CaCO3 (1 g) and agar powder (7.5 g). Mixed and autoclave at

121oC for 20 minutes. Before pouring plates, swirled to thoroughly mix the CaCO3 and agar.

100 µL of each pure bacterial culture was inoculated into NFA (Nitrogen Free Agar) plates.

After 7-10 days of.incubation (28 ± 2oC) formation of halo.zone.around the.colonies were

observed.

40

3.3. Statistical analysisA statistical analysis was made to determine significance between treatment means

and to draw valid conclusion. It was done by using Statistix 8.1 (Analytical computer

software, Statistix 8.1; Tallahassee, F.L., U.S.A., 1985-2003) using randomized complete

block design (RCBD) with factorial arrangement by considering year as a factor. Data

obtained from various observations were subjected to statistical analysis by adopting

appropriate method of “Analysis of Variance”. To compare the treatments means least

significance difference (LSD) test at 5% probability was applied (Steel et al., 1997).

3.4. Economics and marrginal analysisBy subtract total cost from total benefits the net benefits were calculated for each

combination of treatments. Cost (in-put and out-put) of each combination was changed

into $/ha. For the analysis of marginal values, marginal net benefit and marginal rate of

returns was calculated. Marginal values were carried out a-ccording to the procedure

used by C.I.M.M.Y.T (1988).

41

Chapter-IV

RESULTS AND DISCUSSION

An attempt has been made to ascertain the degree of variation exhibited by various

weed, crop and rhizosphere soil parameters due to treatments in the experiment entitled

“Allelopathic effects of crops (sorghum, sunflower and brassica) on weeds, productivity and

rhizosphere of mung bean (Vigna radiata L.)”. The data collected during the course of

investigation have been statistically analyzed and presented in Tables. The analysis of

variance calculated for various characters has been given. The treatment effects have been

described in this chapter in light of statistical interpretation.

4.1. Experiment-I: Effect of sorghum crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.)To assess the effect of different treatments on weed dynamics (density, fresh and dry

weight), crop productivity traits (final emergence count per plot, height of mature plants,

number of seeds/pod, pod length, number of pods/plant, number of nodules/plant, weight of

1000 seeds, biological yield, seed yield, stalk yield and harvest index) and rhizosphere soil

parameters (soil bulk density, total soil porosity, soil pH, soil EC, total soil organic matter,

total soil nitrogen, available phosphorus, available potassium, microbial community,

microbial activity, alkaline phosphatase activity and dehydrogenase activity) were recorded

in this experiment.

Results are presented and discussed below:

Results:

4.1.1. Weed dynamics

Horse purslane (Trianthema portulacastrum) and purpule nut sedge (Cyperus

rotundus) both were dominant in each experimental unit during both years (2014 and 2015)

of study. This study indicated that the density, fresh and dry weight of horse purslane was

significantly differed with various allelopathic weed management strategies. Total weed

42

density, fresh and dry weight were also significantly differed with various allelopathic weed

management strategies. However, fresh and dry weight of purpule nutsedge was non-

significant among

43

Table. 4.1. Mean square values of weed dynamics affected by sorghum crop water extracts and residues

a)SOV b)DF c)HPD d)HPFW e)HPDW f)PND g)PNFW h)PNDW i)TWD j)TWFW k)TWDW

Treatments (T) 4 807.64* 10950.81* 1103.66* 50.36* 92.28NS 116.03NS 1262.21* 12917.41* 1301.86*

Years (Y) 1 215.47* 133.42* 13.44* 4.14* 2.98NS 59.02NS 236.60* 176.35* 17.76*

T×Y 4 35.83 267.70NS 26.98NS 1.49NS 2.98NS 7.26NS 29.27* 238.42NS 24.02NS

Error 18 9.624 136.93 13.80 0.806 6.28 2.642 9.88 141.95 14.30

a)SOV= source of variation, b)DF= degree of freedom, c)HBD= horse purslane density, d)HPFW= horse purslane fresh weight, e)HPDW= horse purslane dry weight, f)PND= purpul nutsedge density, g)PNFW= purpul nutsedge fresh weight, h)PNDW= purpul nutsedge dry weight, i)TWD= total weed density, j)TWFW= total weed fresh weight, k)TWDW= total weed dry weight, *= indicate significant at p ≤ 0.05, NS= non-significant

44

Table. 4.2. Effect of sorghum water extracts and residues on weed dynamics in mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T) 2014 2015 Mean (T)

Hors purslan density (0.25/m2) Hors purslan fresh weight (g per 0.25 m2) Hors purslan dry weight (g/0.25 m2)a)Control 41 40 40 A 156 147 152 A 49 47 48 Ab)SWE @ 10 L ha-1 41 32 36 B 135 126 130 B 40 43 41 BSWE @ 20 L ha-1 38 27 32 C 118 99 108 C 37 32 34 Cc)SR @ 4 tons ha-1 22 16 19 D 82 65 73 D 26 21 23 DSR @ 6 tons ha-1 13 12 13 E 48 43 46 E 15 13 14 EMean e)(Y) 31 A 26 B 104 A 100 B 33 A 31 BLSD (p≤0.05) T=3.76; Y=2.38 T=14.19; Y=3.15 T=4.51; Y=1.85

Purpl nutsedg density (0.25/m2) Purpl nutsedg fresh weight (g per 0.25 m2) Purpl nutsedg dry weight (g per 0.25 m2)Control 10 10 10 A 13 13 13 4 4 4SWE @ 10 L ha-1 9 7 8 B 10 6 8 3 2 3SWE @ 20 L ha-1 7 7 7 C 6 6 6 2 2 2SR @ 4 tons ha-1 6 5 5 D 3 3 3 1 1 1SR @ 6 tons ha-1 3 2 3 E 3 3 3 1 1 1Mean (Y) 7 A 6 B 7 6 2 2LSD (p≤0.05) T=1.09; Y=0.68 NS NS

Total weeds density (0.25/m2) Total weeds fresh weight (g per 0.25 m2) Total weeds dry weight (g per 0.25 m2)Control 57.15 a 56.55 a 56.85 A 175.18 165.94 170.56 A 58.05 55.12 56.58 ASWE @ 10 L ha-1 52.93 ab 49.61 bc 51.27 B 147.90 141.61 144.76 B 49.39 47.39 48.39 BSWE @ 20 L ha-1 45.77 c 36.55 d 41.16 C 130.28 112.08 121.18 C 43.79 38.02 40.91 CSR @ 4 tons ha-1 35.98 d 25.78 e 30.88 D 88.16 78.70 83.93 D 31.37 26.15 28.76 DSR @ 6 tons ha-1 24.35 e 18.42 f 21.39 E 59.78 52.68 56.23 E 20.78 19.16 19.97 EMean (Y) 43.12 A 37.50 B 120.26 A 110.20 B 39.69 A 38.15 BLSD (p≤0.05) T=3.81; Y=2.41; T × Y=5.39 T=14.44;Y=8.13 T=4.59;Y=0.55

a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)T= treatments; e)Y= year

45

various allelopathic weed management strategies (Table 4.1). The year effect was significant

for all weed parameters except fresh and dry weight of purpul nutsedge (Table 4.1). The

interaction of allelopathic weed management strategies and year was significant for total

weed density but non-significant for total fresh and dry weight and also for density, fresh and

dry weight of horse purslane and purpul nutsedge (Table 4.1).

The lowest horse purslane (13) and purpul nutsedge (3) density were recorded with

sorghum residues at 6 tons ha-1 compared to control (40 and 10, respectively). The lowest

values were observed in control (Table 4.2). Total weed density, fresh and dry weight was

decreased over times and the minimum values were observed during 2nd year (Table 4.2). In

case of horse purslane, minimum fresh (46 g per 0.25 m2) and dry.weight (14 g per 0.25 m2)

was observed with sorghum residues at 6 tons per ha followed by sorghum residues at 4 tons

ha-1 (Table 4.2). The maximum value of fresh (152 g per 0.25 m2) and dry weight (48 g per

0.25 m2) was observed in control (Table 4.2). In case of total weeds density the interactive

affect of allelopathic weed management strategies and year showed statistically significant

effect. The minimum total weed density (18.42) was recorded with sorghum residues at 6

tons ha-1 during 2nd year as compared to control (56.55). The lowest total weed fresh and dry

weight (56.23 and 19.97 g/0.25 m-2 respectively) was noted with sorghum residues at 6 tons

ha-1 and the maximum total weed density (56.85) was recorded in control (Table 4.2).

4.1.2. Crop data

4.1.2.1. Emergence and morphological traits

Final emergence count per plot was non-significant but all morphological traits

(height of mature plants, pod length, numbers of branche/plant, numbers of nodule/ plant and

stalk yield) differed significantly among the various allelopathic weed management strategies

(Table 4.3). Likewise, the year effect was significant for stalk yield but non-significant for

final emergence count per plot, height of mature plants, pod length, numbers of

branche/plant, numbers of nodule/plan (Table 4.3). The interaction of allelopathic weed

management strategies and year was non-significant for all traits (Table 4.3).

The results indicated that the maximum height of mature plants, pod length, numbers

of branche/plant, numbers of nodule/plan and stalk yield (Table 4.4; 48.3 cm, 9.0 cm, 14, 11

and 3087 kg ha-1, respectively) were noted with sorghum residues at 6 tons ha-1 as

46

Table. 4.3. Mean square of final emergence and morphological traits of mung bean affected by sorghum crop water extracts and residues

a)SOV b)DF c)FEC d)PH e)PL f)NB g)NN h)SY

Treatments (T) 4 37.12NS 39.71* 0.58* 23.03* 25.05* 331549*

Years (Y) 1 19.23NS 1.28NS 0.16NS 2.37NS 2.70NS 37827*

T×Y 4 0.55NS 1.36NS 0.004NS 0.03NS 0.45NS 15512NS

Error 18 10409.62 50.06 1.48 2.48 2.19 383338

a)SOV= source of variation, b)DF= degree of freedom, c)FEC= final emergence count, d)PH= plant height, e)PL= pod length, f)NB= number of branches, g)NN= number of nodules, h)SY= stalk yield, *= indicate significant at p ≤ 0.05, NS= non-significant

47

Table. 4.4. Effect of sorghum water extracts and residues on final emergence and morphological traits of mung bean

Treatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)Final emergence count per plot Plant height at maturity (cm)

a)Control 557 558 558 40.7 41.2 40.9 Cb)SWE @ 10 L ha-1 558 560 559 42.5 42.7 42.6 CSWE @ 20 L ha-1 560 562 561 42.6 43.7 43.1 Cc)SR @ 4 tons ha-1 561 562 562 45.5 46.6 46.0 BSR @ 6 tons ha-1 563 565 564 47.7 49.0 48.3 AMean e)(Y) 44.0 44.4LSD (p≤0.05) NS T=2.1

Pod length (cm) No of branches per plantControl 8.3 8.4 8.3 C 9 9 9 CSWE @ 10 L ha-1 8.3 8.4 8.4 C 9 10 10 CSWE @ 20 L ha-1 8.5 8.6 8.5 C 10 11 10 BCSR @ 4 tons ha-1 8.7 8.9 8.8 B 12 12 12 ABSR @ 6 tons ha-1 8.9 9.1 9.0 A 14 14 14 AMean (Y) 8.5 8.7 11 11LSD (p≤0.05) T=0.1 T=1

Number of nodules per plant Stalk yield (kg ha-1)Control 5 5 5 C 2445 2489 2462 CSWE @ 10 L ha-1 7 8 8 B 2545 2589 2580 CSWE @ 20 L ha-1 7 8 8 B 2695 2715 2700 BSR @ 4 tons ha-1 9 9 9 AB 2743 2787 2732 BSR @ 6 tons ha-1 10 11 11 A 2989 3018 3087 AMean (Y) 2677 B 2748 ALSD (p≤0.05) T=1.79 T=119.37; Y=65.45a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)T= treatments; e)Y= year

48

compared to control (Table 4.4; 40.9 cm, 8.3 cm, 9, 5, 2462 kg ha -1 respectively). A linear

increase in height of mature plants, pod length, numbers of branche/plant, numbers of

nodule/plan was noted over time and stalk yield had significant increase in values during 2nd

year of study (Table.4.4).

4.1.2.2. Yield and yield components

Yield.and.yield components (no. of pods/plant, no. of seed/pod, weight of 1000 seeds,

biological yielde and harvst indx) differed significantly among the various allelopathic weed

management strategies (Table 4.5). Likewise, the year effect was significant for weight of

1000 seeds, biological yield, harvest index and yield but non-significant for no. of pods per

plant and no. of seeds per pod. The interaction of allelopathic weed management strategies

and year was significant only for yield. However, the interaction was non-significant for no.

of pods/plant, no. of seed/pod, weighte of thousand seeds, biological yield and harvest index

(Table 4.5).

The results indicated that the maximum value of no. of pods per plant (24.63) no. of

seed per pod (9.92), weight of 1000 seeds (55.33 g), biological yield (4106 kg ha -1), harvest

index (26.01%) and yield (1019.3 kg ha-1) were recoded with sorghum residues at 6 tons ha-1.

The minimum value of no. of pods per plant (14.55), no. of seed per pod (5.90), weight of

1000 seeds (50.25 g), biological yield (3206 kg ha-1), harvest index (22.74%) and yield

(744.3 kg ha-1) were observed in control (Table 4.6). A linear increase in no. of pods/plant,

no. of seed/pod, weighte of thousand seeds, biological yield, harvest index and yield was

noted over time and all above observations had significant increase in values during 2nd year

of study (Table 4.6).

4.1.3. Rhizosphere soil analysis

4.1.3.1. Rhizosphere soil properties and nutrients dynamics

At the end of experiment soil physical indicators of soil health like soil porosity and

bulk density were significantly differed among various allelopathic weed management

strategies (Table 4.7). The year effect was also statistically significant for soil physical

indicators but the interaction (allelopathic weed management strategies × year) was non-

significant (Table 4.7). In case of chemical indicators of soil health like EC (electrical

conductivity), SOM (soil organic matter), N (nitrogen), available K (potassium)

49

Table. 4.5. Mean square of yield and yield components of mung bean affected by sorghum crop water extracts and residues

a)SOV b)DF c)NPP d)NSP e)WTS f)BY g)HI h)Y

Treatments (T) 4 88.71* 12.93* 23.62* 696175* 4.85* 70580.21*

Years (Y) 1 114.35NS 13.00NS 15.83* 53679* 0.29* 1383.50*

T×Y 4 6.82NS 0.22NS 0.38NS 17259NS 0.57NS 214.22*

Error 18 4.683 0.943 10.897 386485 18.28 328.111

a)b)DF= degree of freedom, c)NPP= number of pods per plant, d)NSP= number of seed per plant, e)WTS= weight of thousand seed, f)BY= biological yield, g)HI= harvest index, h)Y= yield, *= indicate significant

50

Table. 4.6. Effect of sorghum water extracts and residues on yield and yield components of mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)

No. of pods per plant No. of seed per poda)Control 13.76 15.33 14.55 D 5.43 6.37 5.90 Eb)SWE @ 10 L ha-1 17.00 19.09 18.05 C 6.55 7.80 7.17 DSWE @ 20 L ha-1 19.45 21.19 20.32 BC 6.95 8.02 7.48 Cc)SR @ 4 tons ha-1 20.03 23.99 22.32 AB 7.07 9.01 8.04 BSR @ 6 tons ha-1 23.55 25.72 24.63 A 9.24 10.61 9.92 AMean e)(Y) 17.96 21.86 7.05 8.36LSD (p≤0.05) T=2.62 T=0.25

Weight of 1000-seeds (g) Biological yield (kg ha-1)Control 49.95 50.54 50.25 E 3196 3216 3206 ESWE @ 10 L ha-1 52.58 54.03 53.31 D 3351 3410 3380 DSWE @ 20 L ha-1 53.25 54.90 54.08 C 3525 3587 3556 CSR @ 4 tons ha-1 53.76 55.66 54.71 B 3660 3670 3665 BSR @ 6 tons ha-1 54.49 56.16 55.33 A 3970 4242 4106 AMean (Y) 52.81 B 54.26 A 3540 B 3625 ALSD (p≤0.05) T=0.59; Y=1.45 T=105.07; Y=75.92

Harvest index (%) Yield (kg ha-1)Control 22.62 22.85 22.74 C 741.9 e 746.7 e 744.3 ESWE @ 10 L ha-1 22.48 23.07 22.78 C 789.2 d 811.5 d 800.4 DSWE @ 20 L ha-1 23.95 24.15 24.05 B 844.1 c 867.6 c 855.8 CSR @ 4 tons ha-1 24.26 24.74 24.50 B 931.2 b 934.2 b 932.7 BSR @ 6 tons ha-1 25.67 26.35 26.01 A 1009.1 a 1029.4 a 1019.3 AMean (Y) 23.80 B 24.23 A 863.70 B 877.29 ALSD (p≤0.05) T=0.39; Y=0.41 T=21.97; Y=11.45; T × Y=31.07a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)T= treatments; e)Y= year

51

Table. 4.7. Mean square of soil properties and nutrient dynamics in the rhizosphere of mung bean affected by sorghum crop water extracts and residues

a)SOV b)DF c)SBD d)TSP pH e)EC f)SOM g)TN h)AK i)AP

Treatments (T) 4 0.04* 68.96* 0.26* 0.07* 0.44* 0.05* 9264.35* 11.80*

Years (Y) 1 0.03* 13.97* 0.04 NS 0.01* 0.10* 0.004* 171.36NS 1.60*

T×Y 4 0.007NS 2.00NS 0.01NS 0.0001NS 0.03* 0.002* 55.03NS 0.55*

Error 18 0.004 1.447 0.111 0.011 0.007 0.0005 48.310 0.117

a)SOV= source of variation, b)DF= degree of freedom, c)SBD= soil bulk density, d)TSP= total soil porosity, e)EC= soil electric conductivity, f)SOM= soil organic matter, g)TN= total soil nitrogen, h)AK= available potassium, i)AP= available phosphorus, *= indicate significant at p≤ 0.05, NS= non-significant

52

Table. 4.8. Effect of sorghum water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest

Treatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)Soil bulk density (g cm-3) Total soil porosity (%)

a)Control 1.48 1.46 1.47 A 42.82 43.73 43.27 Cb)SWE @ 10 L ha-1 1.47 1.46 1.46 A 43.53 44.10 43.82 CSWE @ 20 L ha-1 1.47 1.46 1.46 A 43.80 44.11 43.96 Cc)SR @ 4 tons ha-1 1.41 1.29 1.35 B 48.50 50.39 49.44 BSR @ 6 tons ha-1 1.38 1.23 1.30 C 48.66 51.79 50.22 AMean e)(Y) 1.44 A 1.38 B 45.46 B 46.83 ALSD (p≤0.05) T=0.04; Y=0.06 T=0.75; Y=0.92

Soil pH Soil EC (dS m-1)Control 7.75 7.72 7.73 A 1.07 1.11 1.09 CSWE @ 10 L ha-1 7.75 7.70 7.73 A 1.11 1.14 1.12 CSWE @ 20 L ha-1 7.74 7.69 7.72 A 1.12 1.14 1.13 CSR @ 4 tons ha-1 7.44 7.41 7.43 B 1.23 1.27 1.25 BSR @ 6 tons ha-1 7.38 7.18 7.28 C 1.32 1.36 1.34 AMean (Y) 7.61 A 7.54 B 1.17 B 1.20 ALSD (p≤0.05) T=0.12; Y=0.04 T=0.07; Y=0.02

Total soil organic matter (%) Total soil nitrogen (g kg-1)Control 0.67 d 0.69 d 0.68 C 0.22 d 0.21 d 0.22 CSWE @ 10 L ha-1 0.68 d 0.69 d 0.69 C 0.22 d 0.21 d 0.22 CSWE @ 20 L ha-1 0.69 d 0.71 d 0.70 C 0.22 d 0.21 d 0.22 CSR @ 4 tons ha-1 0.96 c 1.24 ab 1.10 B 0.32 c 0.38 b 0.35 BSR @ 6 tons ha-1 1.12 b 1.37 a 1.25 A 0.38 b 0.45 a 0.42 AMean (Y) 0.82 B 0.94 A 0.27 B 0.29 ALSD (p≤0.05) T=0.10; Y=0.06; T × Y=0.14 T=0.03; Y=0.01; T × Y=0.03

Available potassium (mg kg-1) Available phosphorous (mg kg-1)Control 121.45 121.93 121.69 C 6.74 d 6.77 d 6.76 CSWE @ 10 L ha-1 121.52 121.64 121.58 C 6.77 d 6.77 d 6.78 CSWE @ 20 L ha-1 121.52 122.00 122.76 C 6.77 d 6.80 d 6.79 CSR @ 4 tons ha-1 178.85 190.00 184.43 B 8.09 c 9.28 b 8.69 BSR @ 6 tons ha-1 195.00 206.65 200.83 A 9.25 b 10.31 a 9.78 AMean (Y) 147.67 152.45 7.53 B 7.99 ALSD (p≤0.05) T=8.43 T=0.41; Y=0.26; T × Y=0.58

a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)T= treatments; e)Y= year

53

and P (phosphorus) were significantly differed among various allelopathic weed management

strategies (Table 4.7). The year effect was also statistically significant for all soil chemical

indicators except soil pH and available K. The interaction (allelopathic weed management

strategies × year) was statistically significant for SOM, N and available P. But for soil pH,

EC and available K interaction was non-significant (Table 4.7).

The lowest bulk density (1.30 g cm-3) and highest soil porosity (50.22%) were

observed in treatments when sorghum residues @ 6 tons ha-1 were applied as compared to

control while the lowest bulk density (1.23 g cm-3) and highest soil porosity (51.79%) were

observed in 2nd year of experiment (Table 4.8). In case of SOM, N and available P the

highest values (1.37%, 0.45 g kg-1, 10.31 mg kg-1, respectively) were observed during 2nd

year when sorghum residues @ 6 tons ha-1 were applied as compared to control (0.69%, 0.21

g kg-1, 6.77 mg kg-1, respectively). Among all allelopathic weed management strategies the

statistically highest values of soil EC (1.34 dS m-1) and available K (200.83 mg kg-1) were

obtained with the application of sorghum residues @ 6 tons ha-1. The statistically lowest

values for all parameters given above were observed in control which was statistical same

with sorghum water extracts @ 10 and 20 L ha-1 (Table 4.8). A linear increase in soil EC and

available K was observed over time and these parameters (soil EC and available K) had

highest values during the 2nd year of experiment (Table 4.8). In case of soil pH decreasing

trend was observed. Lowest soil pH (7.28) was observed with the application of sorghum

residues @ 6 tons ha-1 and highest soil pH (7.73) was observed in control which was

statistically similar with sorghum.water.extract @ 10 and 20.L ha-1 (Table 4.8).

4.1.3.2. Rhizosphere soil microbial population, activity and enzymes

Microbiological and biochemical indicators are also use full indicators of soil health.

They are more susceptible than physical and chemical attributes to changes imposed to the

environment. Microbiological indicators like population of bacteria, fungi and microbial

activity at 20 days after sowing and at harvesting differed significantly among various

allelopathic weed management strategies (Table 4.9). The year effect was also significant for

all above parameters (Table 4.9). The interactive effect of allelopathic weed management

strategies and year was significant for population of fungi but non-significant for population

of bacteria at 20 days after sowing and at harvesting (Table 4.9). In case of biochemical

54

Table. 4.9. Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by sorghum crop water extracts and residues

a)SOV b)DF c)B (20 d)DAS) B (e)AH) f)F (20 DAS) F (AH) g)MA (20 DAS) MA (AH) h)AP i)DG

Treatments (T) 4 1463.92* 504.53* 283.95* 149.20* 4.36* 2.89* 3947.11* 401.11*

Years (Y) 1 158.70* 28.03* 40.83* 76.80* 0.14* 0.11* 90.20* 64.59*

T×Y 4 30.28NS 8.53NS 6.08* 11.80* 0.001NS 0.001NS 37.08* 14.55*

Error 18 15.12 3.344 1.641 0.989 0.03 0.05 10.051 3.235

a)SOV= source of variation, b)DF= degree of freedom, c)B= Bacteria, f)F= fungi, g)MA= microbial activity, d)DAS= days after sowing, e)AH= after harvesting, h)AP= alkaline phosphatase, i)DG= dehydrogenase, *= indicate significant at p≤0.05, NS= non-significant

55

Table. 4.10. Effect of sorghum water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean

Treatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)Bacteria (cfu/g x 105) 20 f)DAS Fungi (cfu/g x 104) 20 DAS

a)Control 43 44 43 D 7 d 8 d 8 Cb)SWE @ 10 L ha-1 46 48 47 CD 8 d 8 d 8 CSWE @ 20 L ha-1 48 49 48 C 8 d 9 d 9 Cc)SR @ 4 tons ha-1 64 74 69 B 15 c 20 b 18 BSR @ 6 tons ha-1 74 83 79 A 21 b 25 a 23 AMean e)(Y) 55 B 60 A 12 B 14 ALSD (p≤0.05) T=4.72; Y=2.98 T=1.55; Y=0.98; T × Y=2.20

Bacteria (cfu/g x 105) g)AH Fungi (cfu/g x 104) AHControl 21 22 22 C 5 f 6 f 6 DSWE @ 10 L ha-1 23 24 24 C 6 f 7 f 7 CDSWE @ 20 L ha-1 24 25 25 C 6 f 9 e 8 CSR @ 4 tons ha-1 35 40 38 B 11 d 17 b 14 BSR @ 6 tons ha-1 40 44 42 A 14 c 20 a 17 AMean (Y) 29 B 31 A 9 B 12 ALSD (p≤0.05) T=2.21; Y=1.40 T=1.21; Y=0.76; T × Y=1.71

Microbial activity (mg CO2-C kg-1 d-1) 20 DAS Microbial activity (mg CO2-C kg-1 d-1) AHControl 3.63 3.77 3.70 C 2.99 3.13 3.06 CSWE @ 10 L ha-1 3.65 3.78 3.71 C 3.05 3.15 3.10 CSWE @ 20 L ha-1 3.72 3.81 3.77 C 3.08 3.17 3.13 CSR @ 4 tons ha-1 4.88 5.05 4.97 B 3.99 4.11 4.05 BSR @ 6 tons ha-1 5.45 5.58 5.52 A 4.50 4.65 4.58 AMean (Y) 4.26 B 4.40 A 3.52 B 3.64 ALSD (p≤0.05) T=0.20; Y=0.13 T=0.28; Y=0.10

Alkaline phosphatase (μg NP/g soil/h) Dehydrogenase (μg TPF/g soil/h)Control 135.47 e 135.50 e 135.48 C 22.68 d 23.33 d 23.00 CSWE @ 10 L ha-1 135.48 e 135.55 e 135.52 C 23.09 d 23.59 d 23.34 CSWE @ 20 L ha-1 135.78 e 135.85 e 135.82 C 23.33 d 24.19 d 23.76 CSR @ 4 tons ha-1 167.26 d 173.46 c 170.36 B 32.00 c 38.00 b 35.00 BSR @ 6 tons ha-1 185.24 b 196.22 a 190.73 A 37.33 b 44.00 a 40.67 AMean (Y) 151.85 B 155.32 A 27.69 B 30.62 ALSD (p≤0.05) T=3.85; Y=2.43; T × Y=5.44 T=2.18; Y=1.37; T × Y=3.08

a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)T= treatments; e)Y= year; f)DAS= days after sowing; g)AH= after harvesting

indicators like soil enzymes (alkaline phosphatase and dehydrogenase) differed significantly

among various allelopathic weed management strategies at harvest (Table 4.9).

56

Interaction (allelopathic weed management strategies × year) was significant for

fungal population. The highest fungal population (25 cfu/g × 104 and 20 cfu/g × 104,

respectively) was recorded with the application of sorghum residues @ 6 tons ha -1 at both

stages i.e 20 days after sowing and at harvesting of 2nd year of experiment. But highest

bacterial population (79 cfu/g × 104 and 42 cfu/g × 104, respectively) and microbial activity

(5.52 mg CO2-C kg-1 d-1 and 4.58 mg CO2-C kg-1 d-1, respectively) was recorded with the

application of sorghum residues @ 6 tons ha-1 at both stages i.e 20 days after sowing and at

harvesting. The lowest microbial activity (3.70 mg CO2-C kg-1 d-1) and population of both

bacteria (22 cfu/g × 105) and fungi (6 cfu/g × 104) were observed in control (Table 4.10). A

linear increase in bacterial population at 20 days after sowing and at harvesting was observed

over times and the highest bacterial population was observed during second year (Table

4.10). In case of soil enzymes interactive effect of allelopathic weed management strategies

and year were resulted significant effect on activity of both enzymes like alkaline

phosphatase and dehydrogenase. Highest value (196.22 μg NP/g soil/h and 44.00 μg TPF/g

soil/h, respectively) was observed with the application of sorghum residues @ 6 tons ha -1

during 2nd year which was followed with same treatment in 1st year. The lowest value (135.50

μg NP/g soil/h and 23.33 μg TPF/g soil/h, respectively) was recorded in control (Table 4.10).

4.1.4. Economics and marrginal analysis

By subtract total cost from total benefits the net benefits were calculated for each

combination of treatments. Cost (in-put and out-put) of each combination was changed into

$/ha. Among all treatments sorghum residue at 6 tons ha-1 gave maximum economical returns

(306 $) during both years. While, minimum net benefit was obtained from control.

In case of marginal analysis (Table 4.12), application of sorghum residue at 6 tons ha -

1 had maximum MRR (161 $). However, rest of treatments was dominated due to less benefit

and higher cost that vary and these were uneconomical treatments. So, sorghum residue at 6

tons ha-1 may be the choice to get better economic returns.

Table. 4.11. Economics of mung bean grown in various allelopathic weed management strategies during 2014 and 2015

57

Treatments Yield(kg ha-1)

Adjusted yield(kg ha-1)

Gross incomee)$ ha-1

Total cost$ ha-1

Net benefits$ ha-1 Benefit cost ratio

a)Control 744 670 750 615 135 0.22b)SWE @ 10 L ha-1 800 720 806 628 179 0.29

SWE @ 20 L ha-1 856 770 863 633 230 0.36c)SR @ 4 tons ha-1 933 840 940 688 252 0.37

SR @ 6 tons ha-1 1019 917 1027 721 306 0.42

Remarks $ 44.67/40 kg

a)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)PKR= Pakistani rupees; e)$= US dollar

Table. 4.12: Marginal analysis for two years (2014-2015)

Treatments

f)Cost that vary

($ ha-1)

Net benefits

($ ha-1)

g)Marginal cost

($ ha-1)

h)Marginal net benefits

($ ha-1)

Marginal rate of returns

(%)

a)Control 0 135 - - -b)SWE @ 10 L ha-1 12 179 12 44 i)D

SWE @ 20 L ha-1 17 230 17 96 Dc)SR @ 4 tons ha-1 73 252 73 117 161

SR @ 6 tons ha-1 106 306 106 171 161

Remarksa)Control= (plots with no crop residues or extract application); b)SWE= sorghum water extract; c)SR= sorghum residues; d)PKR= Pakistani rupees; e)$= US dollar; i)D= Dminated treatment = Treatment which has higher costs but lower net benefits

4.1.5. Discussion

58

Residues incorporation of allelopathic crop is a green approach for improvement of

soil properties and manages weeds in field crops. Our results showed a significant

improvement in soil properties and weed suppression potential of sorghum residues

incorporation and water extract. This approach had maximum reduction in weed density,

fresh weight and dry weight of weed species in mung bean (Table 4.2). This reduction is due

to phenolics (Dhurrin, p-hydroxybenzaldehyde, sorgoleone, vanillic acid, p-hydroxybenzoic

acid, p-hydroxybenzaldehyde, p-coumaric acid, ferulic acid) with wide spectrum of

biological activities including allelopathy (Sene et al., 2001; Czarnota et al., 2003). In case of

field crops, sorghum had highest allelopathic potential which has been reported by many

researchers (Alsaadawi et al., 2007). Inhibitory activity of sorghum allelochemicals on grassy

and broad leaved weeds has been reported (Alsaadawi et al., 2007). Cheema and Khaliq

(2000) investigated that 35-49% weed density and weed biomass was reduced by using water

extract of mature sorghum crop plants as compared with control. The sorghum residues

treatments showed highest suppression of weeds as compared to sorghum water extracts

treatments (Table 4.2) By adding sorghum at 2 too 6 tons ha-1 to soil it r-educed the weeds bi-

omass by 40 too 50%. Crop residues may change the weed frequency, distribution and may

cause the suppression of weeds (Essien et al., 2009; Khaliq et al., 2015). Zaji and Majd

(2011) showed that the fresh weight and dry weight of different weed biota viz. red root pig

weed (Amaranthus retroflexu), palmer amaranth (Amaranthus palmeri), black nightshade or

wonder berry (Solanum nigrum) and curled dock (Rumex crispus) were decreased severely

by the impact of residues of canola crop. The growth suppression of dominant weed biota in

this experiment might have observed due to physical resistance by sorghum residues

incorporation or release of chemicals from these residues. Allelochemicals release through

different parts of plants are depended on many factors i.e. applied crop family, size and dose

of mulching, decomposition rate, moisture contents, texture of soil and soil micro biota

(Kamara et al., 2000; Khaliq et al., 2014). Weed suppression level is directly related to the

dose of allelopathic product (Khanh et al., 2005; Khaliq et al., 2010, 2011). Mostly, by

incorporation of higher amount of crop residues greater weed suppression was observed. A

two year field experiment was conducted by Alsaadawi et al. (2013). They stated that

sorghum residues incorporation significantly reduced weed number and got high yield of

broad bean than weedy check.

59

Incorporation of sorghum residues have positive effect in case of weed population

and biomass reduction, but also improve nodulation and nitrogen fixation processes,

physical, chemical and nutritional status of field soil. Our results indicate that increased

quantities of crop residues have decreased bulk density and increase total porosity of soil

over time (Table 4.8). Shaver (2010) reported that soil porosity is directly related to soil bulk

density because as soil bulk density decreases, soil porosity increases. In case of soil

properties sorghum residues as allelopathic weed management strategy improved the SOM,

N, available K and P in soil (Table 4.8). Crop residues are good sources of nutrients and are

the primary source of organic material added to soil. It increases the nutrient availability and

water holding capacity of the soils (Krishna et al., 2004). Moisture retention is the main

benefit of residues incorporation. It is caused by decrease in runoff and evaporation of water

from soil. (Verhulst et al., 2011). The improvement in nutrient accumulation (especially P

and K) might be attributed to the fact the enhanced moisture retention within the soil due to

residue incorporation increased the solubility of these nutrients within the soil (Zhou et al.,

2002). Improved moisture availability due to residue incorporation also indicated that the soil

water holding capacity was improved and the soil moisture was available for longer times to

support plant growth (Jin et al., 2013). This increase in moisture retention properties might

decrease the irrigational requirement of the crops, which should be investigated in future

studies. In a study, Raut et al. (2010) stated that incorporation of sunflower straw at 4 t ha -1 +

RDF at (125% N + 100% P) in green gram recorded significantly higher soil N, K, and P

content in green gram-sunflower sequence. In another study, Santamarıa et al. (2008) showed

that with the incorporation of sunflower hulls, residues of oil industries, in the upper layer of

soil as organic amendment. In our study a linear decrease in the soil pH was observed by the

application of sorghum residues as allelopathic weed management strategies (Table 4.8).

Gong et al. (2008) reported that crop residue incorporation in soil decreases the soil pH. The

Parthenium hysterophorus residues in soil also changed the chemistry of soil. It revealed that

pH of P. hysterophorus infested soil decreased, whereas the EC of soil increased (Batish et

al., 2002a,b).

Using sorghum residues as allelopathic weed management strategies in mung bean

improved the microbial population and enzymatic activities of soil (Table 4.10). Microbial

abundance and soil enzymes are soil biological activities which are one of the important

60

indicators of soil quality (Dick, 1994). The residues incorporation of different crops in soil

modified the bio-chemical attributes i.e. soil microbial population and soil enzymatic activity

(Doran, 1980; Dick et al., 1983). Soil enzymes and micro biota play a key role in availability

of nutrients. Dehydrogenase enzyme is an important for the oxidation of soil organic matter

(SOM). It transfers the hydrogen and electrons from substrates to acceptors. The activity of

soil enzymes viz. dehydrogenase and phosphatase depends on type of residues incorporated

in soil. It also depends on the moisture contents and temperature of soil. It affects the activity

of dehydrogenase by changing the oxidation reduction status of soil (Brzezinska et al., 1998).

Incorporation of crop residues viz. tobacco and sunflower in soil increased the activities of

most of the soil enzymes, while the residues of tomato crop only increased the activity of

amylase and phosphodiesterase. At Akola, Maharashtra, Ravankar et al. (2000) reported that

incubation of soil with 1% organic residues of cotton stalk, safflower straw, sorghum stubble,

soybean stover, wheat straw, sugar cane trash, ground nut husk, sunflower straw, green gram

stover, parthenium with seed, grass complex with seed, and Xanthium with seed mixed

showed wide variation in the rate of decomposition, C:N ratio and microbial population at

different intervals. Fungal, bacterial, and actinomycetes populations increased at 30 days of

incubation. Bacteria were predominant over fungi and actinomycetes.

In our study, more than 37% increase in mung bean yield was done through effective

allelopathic weed management strategies. This increase in crop yield might be done due to

the improvement of soil properties and reduced weed competition during critical period of

crop growth. The effective reduction of weeds also increases the obtainability of re-sources

such as light, moisture, nutrients and gap (Kruidhof et al., 2008). A recent research on wheat

residues application in Mediterranean environment by Stagnari et al. (2014) resulted that soil

moisture conservation ability was improved especially during critical growth period of test

crop. The residues which are decomposed completely in soil not provide allelochemicals but

also participant in nutrition for crop plants. It provides nitrogen by release in rhizosphere soil

of tested crop plant. By the application of residues as a biological weed management it

immobilizes the nitrogen and it reduce the immediate source of nitrogen (Khaliq et al.,

2015). But, at later stages of crop growth the obtainability of nitrogen was improved by

mineralization, so this sustained supply of nitrogen is a nonstop source of nutrition for test

crop as well as next crops.

61

So, sorghum residues incorporation improve in soil properties viz. moisture retention,

restoring physical properties, enhance nutrient cycling, microbial activity (Alam et al., 2014;

Adugna and Abegaz, 2016; Nawaz et al., 2016) and suppression in weeds due to physical

hindrance by residues, reduced light penetration and suppressing ability of allelochemicals

which released from these plant residues (Kamara et al., 2000; Khaliq et al., 2014) harvested

better seed yield and achieve higher profitability in spring planted mung bean.

4.1.6. Conclusion

Allelopathic crop residues are magical source of nutrition and have efficient role in

controlling weeds, which is essential for sustainable crop production. In our study,

differential ability to suppress weeds was observed among various sorghum residue and

water extract application treatments. A high suppression of weed density, fresh and dry

weight was observed when sorghum residues at 6 tons ha -1 were incorporated into soil.

Apparently, the residues favorably affected the soil properties viz. nutrient dynamics,

microbial populations, soil enzyme activities. The improvement in soil properties and

suppression in weeds harvested better seed yield and achieve higher profitability in spring

planted mung bean. In short, sorghum residue incorporation may provide a better weed

control along with enhancing soil health and seed yield of spring planted mung bean.

4.2. Experiment-II: Effect of sunflower crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiate L.)

62

To assess the effect of different treatments on weed dynamics (density, fresh and dry

weight), crop productivity traits (final emergence count per plot, height of mature plants, no.

of seeds/pod, pod length, no. of pods/plant, no. of nodules/plant, weifgt of thousand seeds,

biological yield, seed yield, stalk yield and harvest index) and rhizosphere soil parameters

(bulk density, porosity, soil pH, soil EC, total soil organic matter, total soil nitrogen,

available phosphorus, available potassium, microbial community, microbial activity, alkaline

phosphatase activity and dehydrogenase activity) were recorded in this experiment.


Results


Horse purslane (Trianthema portulacastrum) and purpule nut sedge (Cyperus

rotundus) both were dominant in each experimental unit during both year of study. This

study indicated that the density, fresh and dry weight of horse purslane was significantly

differed with various allelopathic weed management strategies. Total.weed.density, fresh and

dry.weight were also significantly differed with various allelopathic weed management

strategies. However, fresh and dry weight of purpule nutsedge was non-significant among

various allelopathic weed management strategies (Table 4.13). The year effect was

significant for all weed parameters except fresh and dry weight of purpul nutsedge (Table

4.1). The interaction of allelopathic weed management strategies and year was significant for

total weed density and density of (horse purslane and purpul nutsedge) but non-significant

for total fresh and dry weight and also for fresh and dry weight of purpul nutsedge (Table

4.13).

The lowest horse purslane (13) and purpul nutsedge (3) density were recorded with

sunflower residues at 6 tons ha-1 in 2nd year as compared to control (42 and 10, respectively).

The lowest values were observed in control which was statistically same with sunflower

water extracts at 10 L ha-1 in 1st year (Table 4.14). Weed density, fresh and dry weight was

decreased over times and the minimum values were observed during 2nd year (Table 4.14). In

63

Table. 4.13. Mean square of weed dynamics affected by sunflower crop water extracts and residues


Treatments (T) 4 617.34* 9718.02* 979.38* 33.14* 41.67* 119.20NS 925.80* 10919.45* 812.26*

Years (Y) 1 51.17* 662.70* 66.78* 0.05NS 5.83 NS 56.60NS 61.03* 662.78* 18.49NS

T×Y 4 13.77NS 159.95 NS 16.12NS 0.11NS 1.29 NS 5.50NS 26.77* 159.92 NS 14.77NS

Error 18 9.532 175.97 17.734 0.2297 5.84 0.715 13.772 144.57 8.811

a)SOV= source of variation, b)DF= degree of freedom, c)HBD= horse purslane density, d)HPFW= horse purslane fresh weight, e)HPDW= horse purslane dry weight, f)PND= purpul nutsedge density, g)PNFW= purpul nutsedge fresh weight, h)PNDW= purpul nutsedge dry weight, i)TWD= total weed density, j)TWFW= total weed fresh weight, k)TWDW= total weed dry weight, *= indicate significant at p≤0.05, NS= non-significant

64

Table. 4.14. Effect of sunflower water extracts and residues on weed dynamics in mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T) 2014 2015 Mean (T)

Hors purslan density (0.25/m2) Hors purslan fresh weight (g per 0.25 m2) Hors purslan dry weight (g per 0.25 m2)a)Control 42 a 42 a 42 A 156 152 154 A 48 50 49 Ab)SFWE at 10 L ha-1 38 a-c 37 bc 38 B 149 135 142 B 43 43 43 BSFWE at 20 L ha-1 35 c 33 c 34 C 124 99 112 C 37 37 36 Cc)SFR at 4 tons ha-1 26 d 16 e 21 D 85 60 72 D 29 21 25 DSFR at 6 tons ha-1 17 e 13 e 15 E 56 40 48 E 19 14 16 EMean e)(Y) 32 A 28 B 114 A 97 B 35 A 33 BLSD (p≤0.05) T=3.27; Y=2.07; T × Y=4.62 T=11.59; Y=10.49 T=0.92; Y=1.33

Purpl nutsedg density (0.25/m2) Purpl nutsedg fresh weight (g per 0.25 m2) Purpl nutsedg dry weight (g per 0.25 m2)Control 10 ab 10 a 10 A 6 5 6 3 3 2SFWE at 10 L ha-1 9 ab 9 b 9 A 6 5 5 2 2 2SFWE at 20 L ha-1 8 c 7 d 8 B 5 5 5 2 2 2SFR at 4 tons ha-1 6 e 6 f 6 C 4 4 4 1 1 2SFR at 6 tons ha-1 5 f 3 g 4 D 4 4 4 1 1 2Mean (Y) 8 A 7 B 5 5 2 2LSD (p≤0.05) T=0.59; Y=0.37; T × Y=5.07 NS NS

Total weeds density (0.25/m2) Total weeds fresh weight (g per 0.25 m2) Total weeds dry weight (g per 0.25 m2)Control 57.29 a 57.89 a 57.59 A 167.03 163.36 165.19 A 57.05 55.88 56.48 ASFWE at 10 L ha-1 52.78 b 52.27 b 52.53 B 156.78 142.90 149.84 B 53.79 49.39 51.59 BSFWE at 20 L ha-1 44.10 c 43.88 c 43.99 C 131.49 107.08 119.29 C 45.77 38.02 41.89 CSFR at 4 tons ha-1 37.32 d 27.09 e 32.20 D 94.34 69.70 82.02 D 33.97 26.15 30.06 DSFR at 6 tons ha-1 27.35 e 21.76 f 24.56 E 65.19 47.68 56.44 E 24.72 19.16 21.94 EMean (Y) 43.77 A 40.58 B 122.23 A 106.88 B 42.83 A 37.95 BLSD (p≤0.05) T=3.58; Y=2.27; T × Y=1.63 T=15.33; Y=9.70 T=4.87; Y=3.08

a)Control= (plots with no crop residues or extract application); b)SFWE= sunflower water extract; c)SFR= sunflower residues; d)T= treatments; e)Y= year

65

case of horse purslane, minimum fresh (48 g per 0.25 m2) and drry weight (16 g per 0.25 m2)

was obserrved with sunflower residues at 6 tons/ha followed by sunflower residues at 4 tons

ha-1 (Table 4.14). The maximum value of fresh (154 g per 0.25 m) and drry weight (49 g per

0.25 m) was observed in control (Table 4.14). In case of horse purslane, purple nutsedge and

total weed density the interactive effect of allelopathic weed management strategies and year

showed statistically significant effect. The minimum horse purslane, purple nutsedge and

total weed density (13, 3 and 21.76 respectively) was recorded with sunflower residues at 6

tons ha-1 during 2nd year as compared to control (42, 10 and 57.89 respectively). The lowest

total weed fresh and dry weight (56.44 and 21.94 g/0.25 m-2, respectively) was noted with

residues at 6 tons ha-1 and the maximum total weed density (57.59) was recorded in control

(Table 4.14).

4.2.2. Crop data



(height of mature plants, pod length, no. of branche/plant, no. of nodule/plant and stalk yield)

differed significantly among the various allelopathic weed management strategies (Table

4.15). Likewise, the year effect was significant for stalk yield but non-significant for final

emergence count per plot, height of mature plants, pod length, no. of branch/plant, no. of

nodule/plant (Table 4.15). The interaction of allelopathic weed management strategies and

year was non-significant for all traits (Table 4.15).

The results indicated that the maximum height of mature plants, pod length, no of

branche/plant, o of nodule/plant and stalk yield (table 5; 48.6 cm, 8.8 cm, 14, 11 and 3027 kg

ha-1 respectively) were noted with sunflower residues at 6 tons ha-1 as compared to control

(Table 4.16; 41.8 cm, 8.2 cm, 9, 6, 2434 kg ha -1 respectively). A linear increase in height of

matur plants, pod length, no. of branche/plant, no. of nodule/plant was noted over time and

stalk yield had significant increase in values during 2nd year of study (Table 4.16).

4.2.2.2. Yiield and yiield componnents

Yiield and yiield componnents (no. of pod/plant, no. of seed/pod, weight of thousand

seeds, biological yield and harrvest iindex) differed significantly among the various

allelopathic weed management strategies (Table 4.17). Likewise, the year effect was

66

Table. 4.15. Mean square of final emergence and morphological traits of mung bean affected by sunflower crop water extracts and residues


Treatments (T) 4 17.55NS 36.18* 0.39* 18.43* 20.55* 294371*

Years (Y) 1 19.20NS 2.08NS 0.08NS 1.51NS 1.20NS 26725*

T×Y 4 0.45NS 0.14NS 0.001NS 0.11NS 0.45NS 8318NS

Error 18 7478.77 53.97 1.58 3.40 1.53 320194

a)SOV= source of variation, b)DF= degree of freedom, c)FEC= final emergence count, d)PH= plant height, e)PL= pod length, f)NB= number of branches, g)NN= number.of nodules, h)SY= stalk yield, *= indicate significant at p≤0.05, NS= non-significant

67

Table. 4.16. Effect of sunflower water extracts and residues on final emergence and morphological traits of mung bean


a)Control 597 599 598 41.7 41.8 41.8 Cb)SFWE @ 10 L ha-1 599 601 600 42.2 42.8 42.5 CSFWE @ 20 L ha-1 600 602 601 44.5 44.9 43.7 Cc)SFR @ 4 tons ha-1 601 602 602 45.8 46.6 46.2 BSFR @ 6 tons ha-1 602 603 603 48.2 49.0 48.6 AMean e)(Y) 600 601 44.5 45.0LSD (p≤0.05) NS T=2.15

Pod length (cm) No of branches per plantControl 8.2 8.3 8.2 C 9 9 9 CSFWE @ 10 L ha-1 8.3 8.4 8.3 C 10 10 10 BCSFWE @ 20 L ha-1 8.4 8.5 8.4 C 11 11 11 BCSFR @ 4 tons ha-1 8.6 8.7 8.6 B 11 12 12 ABSFR @ 6 tons ha-1 8.7 8.8 8.8 A 13 14 14 AMean (Y) 8.4 8.5 11 11LSD (p≤0.05) T=0.1 T=2

Number of nodules per plant Stalk yield (kg ha-1)Control 5 6 6 C 2414 2429 2434 DSFWE @ 10 L ha-1 8 8 8 B 2513 2529 2560 CSFWE @ 20 L ha-1 9 9 9 AB 2655 2712 2685 BSFR @ 4 tons ha-1 9 9 9 AB 2717 2768 2706 BSFR @ 6 tons ha-1 10 11 11 A 2829 2898 3027 AMean (Y) 8 9 2652 B 2712 ALSD (p≤0.05) T=1.50 T=120.37; Y=55.15


68

Table. 4.17. Mean square of yield and yield components of mung bean affected by sunflower crop water extracts and residues


Treatments (T) 4 73.88* 11.07* 17.17* 636090* 5.02* 68569.01*

Years (Y) 1 67.80* 3.09* 1.84* 31493* 0.74* 195.51*

T×Y 4 4.79* 0.05NS 0.23NS 9061NS 0.50NS 117.41*

Error 18 1.804 0.4426 6.903 319373 17.07 147.800

b)DF= degree of freedom, c)NPP= number of pods per plant, d)NSP= number of seed per plant, e)WTS= weight of thousand seed, f)BY= biological yield, g)HI= harvest index, h)Y= yield, *= indicate significant at p≤0.05, NS= non-significant

69

Table. 4.18. Effet of sunflower water extracts and residues on yield and yield components of mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)

No. of pods per plant No. of seed per poda)Control 14.13 f 15.33 ef 14.73 E 5.48 6.37 5.93 Cb)SFWE at 10 L ha-1 17.01 de 18.38 d 17.70 D 6.55 7.13 6.84 BSFWE at 20 L ha-1 18.45 d 21.25 bc 19.85 C 6.95 7.35 7.15 Bc)SFR at 4 tons ha-1 19.03 cd 24.11 a 21.57 B 7.07 7.68 7.38 BSFR at 6 tons ha-1 21.55 b 26.13 a 23.84 A 9.24 9.96 9.60 AMean e)(Y) 18.03 B 21.04 A 7.06 B 7.70 ALSD (p≤0.05) T=1.63; Y=1.03; T × Y=2.30 T=0.81; Y=0.51

Weight of 1000-seeds (g) Biological yield (kg ha-1)Control 50.26 50.54 50.40 D 3155 3205 3180 DSFWE at 10 L ha-1 52.58 53.35 52.97 C 3335 3375 3355 CSFWE at 20 L ha-1 53.25 53.67 53.46 B 3522 3546 3534 BSFR at 4 tons ha-1 53.76 53.76 53.76 B 3631 3640 3636 BSFR at 6 tons ha-1 54.49 55.48 54.99 A 3941 4121 4042 AMean (Y) 52.87 B 53.36 A 3517 B 3582 ALSD (p≤0.05) T=0.35; Y=0.37 T=170.49; Y=60.54

Harvest index (%) Yield (kg ha-1)Control 23.75 24.05 23.90 D 745.3 e 746.7 e 746.0 ESFWE at 10 L ha-1 24.08 24.18 24.13 C 789.2 d 801.5 d 795.4 DSFWE at 20 L ha-1 24.35 24.69 24.52 C 844.1 c 854.4 c 849.3 CSFR at 4 tons ha-1 24.92 26.03 25.48 B 934.2 b 925.4 b 929.8 BSFR at 6 tons ha-1 25.75 26.34 26.05 A 1009.1 a 1019.4 a 1014.3 AMean (Y) 24.57 B 25.06 A 864.38 B 869.49 ALSD (p≤0.05) T=0.41; Y=0.39 T=14.75; Y=4.33; T × Y=20.85


70

significant for all yield and yield components (Table 4.17). The interaction of allelopathic

weed management strategies and year was significant for number of pods per plant and yield

(Table 4.17). However, the interaction was non-significant for no. of seed per pod, weight of

1000 seeds, biological yield and harvest index (Table 4.17).

The results indicated that the maximum (26.13) numbers of pods per plant were noted

with sunflower residues at 6 tons ha-1 during 2nd year as compared to control (15.33). Among

allelopathic weed management strategies, the maximum value of no. of seed per pod (9.60),

weight of 1000 seeds (54.99 g), biological yield (4042 kg ha -1), harvest index (26.05%) and

yield (1014.3 kg ha-1) were recoded with sunflower residues at 6 tons ha-1. The minimum

value of no. of seed per pod (5.93), weight of 1000 seeds (50.40 g), biological yield (3180 kg

ha-1), harvest index (23.90%) and yield (746.0 kg ha-1) were observed in control (Table 4.18).

A linear increase in no. of pod/plant, no. of seeds/pod, weight of thousand seeds, biological

yiield, harvest index and yield was noted over time and all above observations had significant

increase in values during 2nd year of study (Table 4.18).



Soil properties and nutrients status are best indication of soil health. This study

showed that soil porosity, bulk density, pH, EC (electrical conductivity), SOM (soil organic

matter), N (nitrogen), available K (potassium) and P (phosphorus) were significantly differed

among various allelopathic weed management strategies (table 2). The year effect was also

statistically significant for soil porosity, bulk density, EC, SOM, N, available K and P (Table

4.19). The interaction (allelopathic weed management strategies × year) was statistically

significant for soil porosity, bulk density, SOM, N and available P. But for soil pH, EC and

available K interaction was non-significant (Table 4.19).

Interaction (allelopathic weed management strategies × year) was significant for soil

porosity, bulk density, SOM, N and available P. The lowest bulk.density (1.26 g cm-3) and

highest soil.porosity (48.49%) were observed during 2nd year when sunflower residues at 6

tons ha-1 were applied as compared to control. The statistically highest soil bulk density (1.48

g cm-3) was observed in control which was statistical same with sunflower water extracts at

10 and 20 L ha-1 (Table 4.20). In case nutrient dynamics the highest value of SOM (1.32%),

N (0.42 g kg-1) and available P (10.18 mg kg-1) was observed during 2nd year when sunflower

71

Table. 4.19. Mean square of soil properties and nutrient dynamics in the rhizosphere of mung bean affected by sunflower crop water extracts and residues


Treatments (T) 4 0.03* 16.42* 0.27* 0.05* 0.35* 0.04* 7782.47* 10.73*

Years (Y) 1 0.02* 11.87* 0.04 NS 0.02* 0.10* 0.01* 206.88* 1.53*

T×Y 4 0.01* 1.35* 0.01NS 0.01NS 0.03* 0.002* 47.45NS 0.52*

Error 18 0.004 0.871 0.108 0.010 0.007 0.0004 45.251 0.117

a)SOV= source of variation, b)DF= degree of freedom, c)SBD= soil bulk density, d)TSP= total soil porosity, e)EC= soil electric conductivity, f)SOM= soil organic matter, g)TN= total soil nitrogen, h)AK= available potassium, i)AP= available phosphorus, *= indicate significant at p≤0.05, NS= non-significant

72

Table. 4.20. Effect of sunflower water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest


a)Control 1.49 a 1.47 a 1.48 A 43.15 c 44.06 c 43.61 Cb)SFWE at 10 L ha-1 1.48 a 1.47 a 1.48 A 43.87 c 44.43 bc 44.15 CSFWE at 20 L ha-1 1.48 a 1.47 a 1.48 A 44.13 c 44.45 bc 44.29 Cc)SFR at 4 tons ha-1 1.44 ab 1.32 bc 1.38 B 45.76 b 47.72 a 46.74 BSFR at 6 tons ha-1 1.41 ab 1.26 c 1.33 C 45.96 b 48.49 a 47.22 AMean e)(Y) 1.46 A 1.40 B 44.57 B 45.83 ALSD (p≤0.05) T=0.04; Y=0.05; T × Y=0.11 T=1.53; Y=0.72; T × Y=1.60

Soil pH Soil EC (dS m-1)Control 7.78 7.75 7.77 A 1.07 1.11 1.09 CSFWE at 10 L ha-1 7.78 7.74 7.76 A 1.11 1.14 1.13 BCSFWE at 20 L ha-1 7.77 7.73 7.75 A 1.13 1.16 1.15 BCSFR at 4 tons ha-1 7.47 7.44 7.46 B 1.21 1.25 1.23 ABSFR at 6 tons ha-1 7.41 7.21 7.31 C 1.30 1.34 1.32 AMean (Y) 7.64 7.57 1.17 B 1.20 ALSD (p≤0.05) T=0.13 T=0.12; Y=0.02

Total soil organic matter (%) Total soil nitrogen (g kg-1)Control 0.68 d 0.69 d 0.69 C 0.21d 0.21d 0.21CSFWE at 10 L ha-1 0.68 d 0.69 d 0.69 C 0.21d 0.21d 0.21CSFWE at 20 L ha-1 0.69 d 0.71 d 0.70 C 0.21d 0.21d 0.21CSFR at 4 tons ha-1 0.91 c 1.19 ab 1.05 B 0.29c 0.35b 0.32BSFR at 6 tons ha-1 1.08 b 1.32 a 1.20 A 0.35b 0.42a 0.39AMean (Y) 0.81 B 0.92 A 0.25B 0.28ALSD (p≤0.05) T=0.09; Y=0.06; T × Y=0.14 T=0.02; Y=0.02; T × Y=0.04

Available potassium (mg kg-1) Available phosphorous (mg kg-1)Control 122.12 123.26 122.69 C 6.75 d 6.78 d 6.77 CSFWE at 10 L ha-1 122.18 123.33 122.76 C 6.78 d 6.80 d 6.79 CSFWE at 20 L ha-1 122.18 123.33 122.76 C 6.79 d 6.81 d 6.80 CSFR at 4 tons ha-1 173.85 185.00 179.43 B 7.99 c 9.15 b 8.57 BSFR at 6 tons ha-1 190.00 201.67 195.83 A 9.15 b 10.18 a 9.67 AMean (Y) 146.07 B 151.32 A 7.46 B 7.95 ALSD (p≤0.05) T=8.16; Y=5.16 T=0.42; Y=0.26; T × Y=0.59


73

residues at 6 tons ha-1 were applied as compared to control (0.69%, 0.21 g kg-1 and 6.78 mg

kg-1, respectively). Among all allelopathic weed management strategies the statistically

highest values of soil EC (1.32 ds m-1) and available K (195.83 mg kg-1) were obtained with

the application of sunflower residues at 6 tons ha-1. The statistically lowest (1.09 ds m-1 and

122.69 mg kg-1 ) values for all parameters given above were observed in control which was

statistical same with sunflower water extracts at 10 and 20 L ha -1 (Table 4.20). A linear

increase in soil EC and available K was observed over time and these parameters (soil EC

and available K) had highest values during the 2nd year of experiment (Table 4.20). In case of

soil pH decreasing trend was observed. The lowest soil pH (7.31) was observed with the

application of sunflower residues at 6 tons ha-1 and the highest soil pH (7.77) was observed in

control whiich was statisttically simiilar with sunnflower water exttract at 10 L/ha and 20

L/ha (Table 4.20).


Microbial abundance, activity and soil enzymes are robust indicators of soil fertility.

Microbial populations and activity at 20 days after sowing and at harvesting, soil enzymatic

activities (alkaline phosphatase and dehydrogenase) differed significantly among various

allelopathic weed management strategies (Table 4.21). The year effect was also significant

for all above parameters (Table 4.21). The interactive effect of allelopathic weed

management strategies and year was significant for alkaline phosphatase and dehydrogenase

activity and was non-significant for population and activity of microbes (Table 4.21).

Among the all allelopathic weed management strategies highest bacterial (75 cfu/g ×

105 and 39 cfu/g × 105, respectively), fungal (21 cfu/g × 104 and 15 cfu/g × 104, respectively)

population and microbial activity (5.26 mg CO2-C kg-1 d-1 and 4.37 mg CO2-C kg-1 d-1,

respectively) was recorded with the application of sunflower residues at 6 tons ha-1 at both

stages i.e 20 days after sowing and at harvesting. The lowest population and microbial

activity was observed in control whiich was statisttically simiilar with sunnflower water

extracts at 10 and 20 L/ha (Table 4.22). A linear increase in bacterial, fungal population and

microbial activity at 20 days after sowing and at harvesting was observed over times and the

highest bacterial, fungal population and microbial activity was observed during second year

(Table 4.22). In case of soil enzymes interactive effect of allelopathic weed management

strategies and year resulted significant effect on activity of both alkaline phosphatase and

74

Table. 4.21. Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by sunflower crop water extracts and residues of sunflower crop


Treatments (T) 4 1277.70* 454.05* 219.00* 105.30* 3.14* 2.05* 3231.33* 270.13*

Years (Y) 1 145.20* 30.00* 43.20* 67.50* 0.15* 0.21* 90.20* 67.56*

T×Y 4 32.70NS 8.25NS 5.70NS 12.00NS 0.001NS 0.001NS 37.08* 15.51*

Error 18 14.88 2.833 1.033 1.011 0.45 0.34 11.96 3.236

a)SOV= source of variation, b)DF= degree of freedom, c)B= Bacteria, f)F= fungi, g)MA= microbial activity, d)DAS= days after sowing, e)AH= after harvesting, h)AP= alkaline phosphatase, i)DG= dehydrogenase, *= indicate significant at p≤0.05, NS= non-significant

75

Table. 4.22. Effect of sunflower water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean


a)Control 43 44 44 C 7 8 8 Cb)SFWE at 10 L ha-1 43 44 44 C 7 8 8 CSFWE at 20 L ha-1 44 45 45 C 8 9 9 Cc)SFR at 4 tons ha-1 60 70 65 B 13 18 15 BSFR at 6 tons ha-1 70 79 75 A 19 23 21 AMean e)(Y) 52 B 56 A 11 B 13 ALSD (p≤0.05) T=4.68; Y=2.96 T=1.23; Y=0.78

Bacteria (cfu/g x 105) g)AH Fungi (cfu/g x 104) AHControl 20 21 21 C 5 6 6 DSFWE at 10 L ha-1 21 21 21 C 6 6 6 CDSFWE at 20 L ha-1 22 22 22 C 6 8 7 CSFR at 4 tons ha-1 32 37 35 B 9 15 12 BSFR at 6 tons ha-1 37 41 39 A 12 18 15 AMean (Y) 26 B 28 A 8 B 11 ALSD (p≤0.05) T=2.04; Y=1.29 T=1.22; Y=0.77

Microbial activity (mg CO2-C kg-1 d-1) 20 DAS Microbial activity (mg CO2-C kg-1 d-1) AHControl 3.61 3.72 3.67 C 2.95 3.15 3.05 CSFWE at 10 L ha-1 3.65 3.78 3.72 C 3.05 3.17 3.11 CSFWE at 20 L ha-1 3.73 3.85 3.79 C 3.09 3.19 3.14 CSFR at 4 tons ha-1 4.65 4.81 4.73 B 3.78 3.95 3.87 BSFR at 6 tons ha-1 5.18 5.35 5.26 A 4.25 4.48 4.37 AMean (Y) 4.16 B 4.30 A 3.42 B 3.59 ALSD (p≤0.05) T=0.50; Y=0.11 T=0.41; Y=0.15

Alkaline phosphatase (μg NP/g soil/h) Dehydrogenase (μg TPF/g soil/h)Control 135.14 e 135.16 e 135.15 C 22.35 d 23.00 d 22.67 CSFWE at 10 L ha-1 135.15 e 135.22 e 135.18 C 22.76 d 23.26 d 23.01 CSFWE at 20 L ha-1 135.45 e 135.52 e 135.48 C 23.00 d 23.85 d 23.43 CSFR at 4 tons ha-1 162.25 d 168.45 c 165.35 B 29.00 c 35.00 b 32.00 BSFR at 6 tons ha-1 180.25 b 191.23 a 185.74 A 34.00 b 41.00 a 37.50 AMean (Y) 149.65 B 153.12 A 26.22 B 29.22 ALSD (p≤0.05) T=4.20; Y=11.05; T × Y=5.93 T=2.18; Y=1.38; T × Y=3.09

a)Control= (plots with no crop residues or extract application); b)SFWE= sunflower water extract; c)SFR= sunflower residues; d)T= treatments; e)Y= year; f)DAS= days after sowing; g)AH= after harvesting

76

dehydrogenase enzyme. The highest value of alkaline phosphatase (191.23 µg NP g-1 soil h-1)

and dehydrogenase (41.00 µg TPF g-1 soil h-1) was observed with the application of

sunflower.residues at 6 tons ha-1 during 2nd year which was followed with same treatment in

1st year. The lowest value of alkaline phosphatase (135.15 µg NP g -1 soil h-1) and

dehydrogenase (23.00 µg TPF g-1 soil h-1) was recorded in control (Table 4.22).

4.2.4. Economics and marrginal analysis

Economics and margiinal annalyses were per-formed to compaere the net and

margiinal returrns and domiinance of indiividual treatment (Table 4.23). Among all

treatments sunflower residue at 6 tons ha-1 gave maximum economical returns (339 $) during

both years. While, minimum net benefit was obtained from control.

In case of marginal analysis (Table 4.24), application of sunflower residue at 6 tons

ha-1 had maximum MRR (308 $). However, rest of treatments was dominated due to less

benefit and higher cost that vary and these were uneconomical treatments. So, sunflower

residue at 6 tons ha-1 may be the choice to get better economic returns.


77

TreatmentYiield(kg/ha)

Adjusted yiield(kg/ha)

Gross iincomee)$/ha

Total cost$/ha

Neet benefits$/ha

Benefit costt ratiio

a)Control 746 671 752 615 137 0.22b)SFWE at 10 L ha-1 795 716 802 625 177 0.28

SFWE at 20 L ha-1 849 764 856 628 229 0.36c)SFR at 4 tons ha-1 930 837 937 663 274 0.41

SFR at 6 tons ha-1 1014 913 1022 683 339 0.50


a)Control= (plots with no crop residues or extract application); b)SFWE= sunflower water extract; c)SFR= sunflower residues; d)PKR= Pakistani rupees; e)$= US dollar

Table. 4.24: Marginal analysis for two years (2014-2015)

Treatments

f)Cost that vary

($ ha-1)

Net benefits

($ ha-1)

g)Marginal cost

($ ha-1)


($ ha-1)


(%)

a)Control 0 135 - - -b)SFWE @ 10 L ha-1 10 181 10 47 i)D

SFWE @ 20 L ha-1 12 235 12 101 Dc)SFR @ 4 tons ha-1 48 277 48 143 297

SFR @ 6 tons ha-1 68 344 68 209 308

Remarksa)Control= (plots with no crop residues or extract application); b)SFWE= sunflower water extract; c)SFR= sunflower residues; d)PKR= Pakistani rupees; e)$= US dollar

4.2.5. Discussion

Residues incorporation of allelopathic crop is a green approach for improvement of

soil health and managing weeds in field crops. Our results showed a significant improvement

in soil health and weed suppression potential of sunflower residues incorporation and water

extract. This approach had maximum reduction in weed number and dry biomass of weed

78

species in mung bean (Table 4.14) owing to presence of phenolic compounds (cholorogenic,

caffiec, syringic, vanilic and ferulic acid) and terpinoids (sesquiterpene.lactones) with wide

spectrum of biological.activities including.allelopathy (Anjum and Bajwa, 2005; Marsni et

al., 2015). In case of field crops, sunflower had highest allelopathic potential which has been

reported by many.researchers (Alsaadawi et al., 2007). Kandhro et al. (2015) reported

maximum reduction.in weed.density (25.26%) and dry weight (14.60%) due water extract.

The results of Farooq et al. (2011) who observed statistically significant decrease in weed

number by using crop water extract are also supporting our results. The sunflower residues

treatments showed highest suppression of weeds as compared to sunflower water extracts

treatments (Table 4.14). The weed suppression of weeds in the field of wheat due to

sunflower residues by releasing allelochemicals and its effective involvement in nutrient

cycling (Table 4.14 Reberg-Horton et al., 2005; Alsaadawi et al., 2007). Crop residues may

change the weed frequency, distribution and may cause the suppression of weeds (Khaliq et

al., 2015; Essien et al., 2009). Previously, Zaji and Majd (2011) showed that the weight

(fresh and dry) of different weed biota viz. red root pig weed (Amaranthus retroflexu),

palmer amaranth (Amaranthus palmeri), black nightshade or wonder berry (Solanum nigrum)

and curled dock (Rumex crispus) were decreased severely by the impact of residues of canola

crop. The growth suppression of dominant weed biota in this study might have observed due

to physical resistance by sunflower residues incorporation or release of chemicals from these

residues. Allelochemicals release through different parts of plants are depended on many

factors i.e. applied crop family, size and dose of mulching, decomposition rate, moisture

contents, texture of soil and soil micro biota (Kamara et al., 2000; Khaliq et al., 2014).

Our results indicate that increased quantities of crop residues have decreased bulk

density and increase total porosity of soil over time (Table 4.20). Shaver (2010) reported that

soil porosity is directly related to soil bulk density because as soil bulk density decreases, soil

porosity increases. In case of soil properties sunflower residues as allelopathic weed

management strategy improved the SOM, N, available K and P in soil (Table 4.20). Crop

residues are good sources of nutrients and are the primary source of organic material added

to soil. It increases the nutrient availability and water holding capacity of the soils (Krishna et

al., 2004). Moisture retention is the main benefit of residues incorporation. It is caused by

decrease in runoff and evaporation of water from soil (Verhulst et al., 2011). The

79

improvement in nutrient accumulation (especially P and K) might be attributed to the fact the

enhanced moisture retention within the soil due to residue incorporation increased the

solubility of these nutrients within the soil (Zhou et al., 2002). Improved moisture

availability due to residue incorporation also indicated that the soil water holding capacity

was improved and the soil moisture was available for longer times to support plant growth

(Jin et al., 2013). This increase in moisture retention properties might decrease the

irrigational requirement of the crops, which should be investigated in future studies. In a

study, Raut et al. (2010) stated that incorporation of sunflower straw at 4 t ha-1 + RDF at

(125% N + 100% P) in green gram recorded significantly higher soil N, K, and P content in

green gram sunflower sequence. In another study, Santamarıa et al. (2008) showed that with t

he incorporation of sunflower hulls, residues of oil industries, in the upper layer of soil as

organic amendment. In our study a linear decrease in the soil pH was observed by the

application of sunflower residues as allelopathic weed management strategies (Table 4.20).

Gong et al. (2008) reported that sunflower oil residue incorporation in soil decreases the soil

pH. The Parthenium hysterophorus residues in soil also changed the chemistry of soil. It

revealed that pH of P. hysterophorus infested soil decreased, whereas the EC of soil

increased (Batish et al., 2002a,b).

Using sunflower residues as allelopathic weed management strategies in mung bean

improved the microbial population and enzymatic activities of soil (Table 4.22). Microbial

abundance and soil enzymes are soil biological activities which are one of the important

indicators of soil quality (Dick, 1994). The residues incorporation of different crops in soil

modified the bio-chemical attributes i.e. soil microbial population and soil enzymatic activity

(Doran, 1980; Dick et al., 1983). Soil enzymes and micro biota play a key role in availability

of nutrients. Dehydrogenase enzyme is an important for the oxidation of soil organic matter





Incorporation of crop residues viz. tobacco and sunflower in soil increased the activities of

most of the soil enzymes, while the residues of tomato crop only increased the activity of

amylase and phosphodiesterase. At Akola, Maharashtra, Ravankar et al. (2000) reported that

80

incubation of soil with 1% organic residues of cotton stalk, safflower straw, sorghum stubble,

soybean stover, wheat straw, sugar cane trash, ground nut husk, sunflower straw, green gram

stover, parthenium with seed, grass complex with seed, and Xanthium with seed mixed

showed wide variation in the rate of decomposition, C:N ratio and microbial population at

different intervals. Fungal, bacterial, and actinomycetes populations increased at 30 days of


In our study, more than 36% increase in mung bean yield was done through effective

allelopathic weed management strategies (Table 4.18). This increase in crop yield might be

done due to the improvement of soil health and reduction in weed competition during critical

crop growth period. The reduction of weeds also increases the obtainability of resources such

as light, moisture, nutrients and space (Kruidhof et al., 2008). A recent research on wheat

residues application in Mediterranean environment by Stagnari et al. (2014) resulted that soil

moisture conservation ability was improved especially during critical.growth.period of test

crop. The residues which are decomposed completely in soil provide allelo-chemicals but

also contribute in crop.nutrition. It provides nitrogen through release.in.rhizosphere soil of

tested crop plant. By the application of residues as a biological weed management it

immobilizes the nitrogen which causes reduction in supply of nitrogen (Khaliq et al., 2015).

But, at later stages of crop growth the obtainability of nitrogen was improved

through.mineralization, .so this.prolonged.supply of nitrogen is continuous.source of

nutrition for test crop as well as next crops.

So, sunflower residues incorporation improve in soil properties viz. moisture

retention, restoring physical properties, enhance nutrient cycling, microbial activity (Alam et

al., 2014; Adugna and Abegaz, 2016; Nawaz et al., 2016) and suppression in weeds due to

physical hindrance by residues, reduced light penetration and suppressing ability of

allelochemicals which released from these plant residues (Kamara et al., 2000; Khaliq et al.,

2014) harvested better seed yield and achieve higher profitability in spring planted mung

bean.

4.2.6. Conclusion

Allelopathic crop residues are magical source of nutrition and have efficient role in

controlling weeds, which is essential for sustainable crop production. In our study,

differential ability to suppress weeds was observed among various sunflower residue and

81

water extract application treatments. A high suppression of weed density fresh and dry

weight was observed when sunflower residues at 6 tons ha-1 were incorporated into soil.

Apparently, the residues favorably affected the soil properties viz. nutrient dynamics,

microbial populations, soil enzyme activities. The improvement in soil properties and

suppression in weeds harvested better seed yield and achieve higher profitability in spring

planted mung bean. In short, sunflower residue incorporation may provide a better weed

control along with enhancing soil health and seed yield of spring planted mungbean.

82

4.3. Experiment-III: Effect of brassica crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiate L.)To assess the effect of different treatments on weed dynamics (density, fresh and dry

weight), crop productivity traits (final emergence count per plot, height of mature plants, no.

of seed/pod, pod length, no. of pod/plant, no. of nodules/plant, 1000-seed weight, biological

yield, seed yield, stalk yield and harvest index) and rhizosphere soil parameters (bulk density,

porosity, soil pH, soil EC, total soil organic matter, total soil nitrogen, available phosphorus,

available potassium, microbial community, microbial activity, alkaline phosphatase activity

and dehydrogenase activity) were recorded in this experiment.


Results:


The dominant weed flora of study site in both years, assessed 30 days after sowing of

spring planted mung bean, consisted primarily of horse purslane (Trianthema portulacastrum

L.) which is broad leaved weed and purple nutsedge (Cyperus rotundus L.) which belongs to

sedges. According to this study horse purslane and purple nut sedge density, fresh and dry

weight showed significant difference with various allelopathic weed management strategies

(Table 4.25) but purple nut sedge fresh and dry weight was non-significant. The year effect

was also significant for all above parameters of horse purslane but non-significant for purple

nutsedge (Table 4.25). The interactive (allelopathic weed management strategies and year)

effect of both weeds was non-significant (Table 4.25). Inn casee of tottal weeds densiity,

fresh and drry weight the study showed significant difference with various allelopathic weed

management strategies (Table 4.25). The year effect was also significant for total weed

density, fresh and dry weight (Table 4.25). The interaction of allelopathic weed management

strategies and year was significant for total weed density but non-significant for total fresh

and dry weight (Table 4.25).

The lowest horse purslane and purple nutsedge density (18 and 5, respectively), fresh

(60 g/0.25 m2 and 3 g/0.25 m2, respectively) and dry weight (49 g/0.25 m2 and 1 g/0.25 m2,

respectively) were recorded with brassica residues @ 6 tons ha-1 as compared to control. The

highest values were observed in control (41, 10, respectively) which was statistically same

83

Table. 4.25. Mean square of weed dynamics affected by brassica crop water extracts and residues


Treatments (T) 4 617.34* 9718.02* 979.38* 33.14* 41.67* 119.20NS 925.80* 10919.45* 812.26*

Years (Y) 1 51.17* 662.70* 66.78* 0.05NS 5.83 NS 56.60NS 61.03* 662.78* 18.49NS

T×Y 4 13.77NS 159.95 NS 16.12NS 0.11NS 1.29 NS 5.50NS 26.77* 159.92 NS 14.77NS

Error 18 9.532 175.97 17.734 0.2297 5.84 0.715 13.772 144.57 8.811

a)SOV= source of variation, b)DF= degree of freedom, c)HBD= horse purslane density, d)HPFW= horse purslane fresh weight, e)HPDW= horse purslane dry weight, f)PND= purpul nutsedge density, g)PNFW= purpul nutsedge fresh weight, h)PNDW= purpul nutsedge dry weight, i)TWD= total weed density, j)TWFW= total weed fresh weight, k)TWDW= total weed dry weight, *= indicate significant at p≤0.05, NS= non-significant

Table. 4.26. Effect of brassica water extracts and residues on weed dynamics in mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T) 2014 2015 Mean (T)

Hors purslan density (0.25/m2) Hors purslan fresh weight (g per 0.25 m2) Hors purslan dry weight (g per 0.25 m2)a)Control 41 41 41 A 155 152 153 A 49 48 49 Ab)BWE @ 10 L ha-1 40 39 39 A 146 144 145 A 46 46 46 ABWE @ 20 L ha-1 36 35 35 B 130 128 129 B 41 41 41 B

84

c)BR @ 4 tons ha-1 27 20 24 C 96 74 85 C 31 24 27 CBR @ 6 tons ha-1 20 16 18 D 70 50 60 D 22 16 19 DMean e)(Y) 33 A 30 B 119 A 110 B 38 A 35 BLSD (p≤0.05) T=3.74; Y=2.37 T=16.09; Y=8.35 T=5.11; Y=2.35

Purpl nutsedg density (0.25/m2) Purpl nutsedg fresh weight (g per 0.25 m2) Purpl nutsedg dry weight (g per 0.25 m2)Control 10 10 10 A 9 10 10 3 3 3BWE @ 10 L ha-1 10 10 10 A 9 9 9 2 2 2BWE @ 20 L ha-1 8 8 8 B 7 7 7 2 2 2BR @ 4 tons ha-1 6 7 6 C 5 5 5 1 1 1BR @ 6 tons ha-1 5 4 5 D 3 3 3 1 1 1Mean (Y) 8 8 6 6 2 2LSD (p≤0.05) T=0.59 NS NS

Total weeds density (0.25/m2) Total weeds fresh weight (g per 0.25 m2) Total weeds dry weight (g per 0.25 m2)Control 57.25 a 57.8 a 57.55 A 165.46 163.20 164.33 A 56.55 55.83 56.19 ABWE @ 10 L ha-1 54.19 a 53.93 a 54.06 A 153.50 152.24 152.87 A 52.75 52.35 52.55 BBWE @ 20 L ha-1 45.80 b 45.46 b 45.63 B 137.79 135.65 136.72 B 47.77 47.09 47.43 CBR @ 4 tons ha-1 40.30 b 30.96 c 35.62 C 100.60 78.80 89.70 C 35.96 29.04 32.50 DBR @ 6 tons ha-1 30.35 c 25.42 c 27.89 D 74.54 55.00 64.77 D 27.69 21.48 24.59 EMean (Y) 45.60 A 42.73 B 126.38 A 116.98 B 44.14 A 41.16 BLSD (p≤0.05) T=4.45; Y=2.81; T × Y=6.29 T=14.58;Y=9.22 T=3.60; Y=2.93

a)Control= (plots with no crop residues or extract application); b)BWE= brassica water extract; c)BR= brassica residues; d)T= treatments; e)Y= year

85

with brassica water extracts @ 10 L ha-1 (Table 4.26). In case of total weed density the

interactive effect of allelopathic weed management strategies and year showed statistically

significant effect. The minimum total weed density (25.42) was recorded with brassica

residues @ 6 tons ha-1 during 2nd year as compared to control (Table 4.26). The lowest total

weed fresh and dry weight (64.77 g/0.25 m2 and 24.59 g/0.25 m2, respectively) was noted

with brassica residues @ 6 tons ha-1 and the maximum total weed density (57.55) was

recorded in control (Table 4.26).

4.3.2. Crop data



(height of mature plants, pod length, no. of branche/plant, no of nodule/plant and stalk yield)

differed significantly among the various allelopathic weed management strategies (Table

4.27). Likewise, the year effect was significant for stalk yield but non-significant for final

emergence count per plot, height of mature plants, pod length, no. of branche/plant, no. of

nodule/plant (Table 4.27). The interaction of allelopathic weed management strategies and

year was non-significant for all traits (Table 4.27).

The results indicated that the maximum heigh of matur plants, pod length, no. of

branche/plant, no. of nodule/plant and stalk yield (Table 4.28; 48.4 cm, 8.7 cm, 13, 13 and

3111 kg ha-1, respectively) were noted with brassica residues at 6 tons ha-1 as compared to

control (Table 4.28; 41.6 cm, 7.9 cm, 9, 7, 2401 kg ha-1, respectively). A linear increase in

height of mature plants, pod length, no. of branche/plant, no of nodule/plant was noted over

time and stalk yield had significant increase in values during 2nd year of study (Table 4.28).

4.3.2.2. Yield and yield components

Yield and yield components like numbers of pod/plant, no. of seeds/pod, thousand

seeds weight, biological yiield and harrvest iindex differed significantly among the various

allelopathic weed management strategies (Table 4.29). Likewise, the year effect was

significant for all yiield and yiield componnents except 1000-seed weiight, biological yield

and harvest index (Table 4.29). The interaction of allelopathic weed management strategies

and year was significant for yield (Table 4.29). However, the interaction was non-significant

for

86

Table. 4.27. Mean square of final emergence and morphological traits of mung bean affected by brassica crop water extracts and residues


Treatments (T) 4 22.25NS 41.22* 0.60* 23.70* 31.95* 419159.1*

Years (Y) 1 19.38NS 1.37NS 0.07NS 1.81NS 2.70NS 19182.3*

T×Y 4 2.78NS 0.26NS 0.006NS 0.33NS 0.45NS 9273.5NS

Error 18 148.05 1.95 1.18 48.45 3.71 320232

a)SOV= source of variation, b)DF= degree of freedom, c)FEC= final emergence count, d)PH= plant height, e)PL= pod length, f)NB= number of branches, g)NN= number of nodules, h)SY= stalk yield, *= indicate significant at p≤0.05, NS= non-significant

87

Table. 4.28. Effect of brassica water extracts and residues on final emergence and morphological traits of mung bean


a)Control 595 599 602 41.4 41.5 41.6 Db)BWE @ 10 L ha-1 597 598 601 44.2 44.4 44.4 CBWE @ 20 L ha-1 599 600 600 45.4 45.6 45.5 BCc)BR @ 4 tons ha-1 600 601 598 46.6 47.7 47.1 ABBR @ 6 tons ha-1 601 602 597 48.1 48.6 48.4 AMean e)(Y) 598 600 45.1 45.6LSD (p≤0.05) NS T=1.69

Pod length (cm) No of branches per plantControl 7.9 7.9 7.9 D 8 9 9 CBWE @ 10 L ha-1 8.1 8.2 8.2 C 10 10 10 BCBWE @ 20 L ha-1 8.3 8.4 8.3 C 11 12 12 ABBR @ 4 tons ha-1 8.4 8.5 8.5 B 13 13 13 ABR @ 6 tons ha-1 8.7 8.8 8.7 A 13 14 13 AMean (Y) 8.3 8.4 11 12LSD (p≤0.05) T=0.1 T=1

Number of nodules per plant Stalk yield (kg ha-1)Control 6 7 7 C 2412 2425 2401 DBWE @ 10 L ha-1 8 8 8 C 2511 2529 2729 CBWE @ 20 L ha-1 9 9 9 C 2653 2675 2781 CBR @ 4 tons ha-1 10 11 11 B 2725 2787 2935 BBR @ 6 tons ha-1 12 13 13 A 2925 2994 3111 AMean (Y) 9 10 2766 B 2817 ALSD (p≤0.05) T=1.50 T=140.97; Y=45.15a)Control= (plots with no crop residues or extract application); b)BWE= brassica water extract; c)BR= brassica residues; d)T= treatments; e)Y= year

88

Table. 4.29. Mean square of yield and yield components of mung bean affected by brassica crop water extracts and residues


Treatments (T) 4 53.38* 10.77* 17.61* 781917* 3.32* 61461.7*

Years (Y) 1 37.81* 3.18* 1.66NS 29016NS 0.08NS 1014.0*

T×Y 4 2.13NS 0.04NS 0.36NS 8412NS 0.76NS 195.8*

Error 18 0.794 0.1764 3.368 322005 12.40 49.5

b)DF= degree of freedom, c)NPP= number of pods per plant, d)NSP= number of seed per plant, e)WTS= weight of thousand seed, f)BY= biological yield, g)HI= harvest index, h)Y= yield, *= indicate significant at p≤0.05, NS= non-significant

89

Table. 4.30. Effect of brassica water extracts and residues on yield and yield components of mung beanTreatments 2014 2015 Mean d)(T) 2014 2015 Mean (T)

No. of pods per plant No. of seed per poda)Control 14.03 15.17 14.60 E 5.29 6.17 5.73 Db)BWE @ 10 L ha-1 16.92 18.18 17.55 D 6.35 7.03 6.69 CBWE @ 20 L ha-1 17.67 21.27 19.47 C 6.55 7.18 6.87 BCc)BR @ 4 tons ha-1 19.29 22.74 21.01 B 7.04 7.68 7.36 BBR @ 6 tons ha-1 21.25 23.03 22.14 A 9.14 9.56 9.35 AMean e)(Y) 17.83 B 20.08 A 6.87 B 7.53 ALSD (p≤0.05) T=1.08; Y=0.68 T=0.51; Y=0.32

Weight of 1000-seeds (g) Biological yield (kg ha-1)Control 50.14 50.26 50.20 D 3134 3185 3159 EBWE @ 10 L ha-1 52.28 53.19 52.74 C 3514 3526 3520 CBWE @ 20 L ha-1 53.06 53.26 53.16 B 3625 3632 3628 CBR @ 4 tons ha-1 53.66 53.70 53.68 B 3835 3885 3860 BBR @ 6 tons ha-1 54.29 55.38 54.84 A 4023 4214 4118 AMean (Y) 52.69 53.16 3626 3688LSD (p≤0.05) T=0.53 T=353

Harvest index (%) Yield (kg ha-1)Control 22.14 23.05 22.60 D 743.2 f 773.9 e 758.5 EBWE @ 10 L ha-1 22.63 23.10 22.87 D 785.1 de 795.9 d 790.5 DBWE @ 20 L ha-1 23.51 23.84 23.68 C 842.0 c 852.7 c 847.4 CBR @ 4 tons ha-1 24.28 24.07 24.19 B 923.6 b 924.9 b 924.3 BBR @ 6 tons ha-1 25.30 24.34 24.82 A 1005.0 a 1009.7 a 1007.3 AMean (Y) 23.57 23.68 859.78 B 871.41 ALSD (p≤0.05) T=0.53 T=8.53; Y=5.40; T × Y=12.07


90

no. of pods/plant, no. of seed/pod, thousand seeds weight, biological yiield and harrvest

iindex (Table 4.29).

The results indicated that the maximum yield (1009.7 kg ha-1) was noted with brassica

residues @ 6 tons ha-1 during 2nd year as compared to control (Table 4.30). Among

allelopathic weed management strategies, the maximum values of no. of pods per plant

(22.14), no. of seed per pod (9.35), 1000-seed weight (54.84), biological yield (4118) and

harvest index (24.82) were recoded with brassica residues @ 6 tons ha -1. The minimum

values of no. of pods per plant (14.60), no. of seed per pod (5.73), 1000-seed weight (50.20),

biological yield (3159) and harvest index (22.60) were observed in control (Table 4.30). A

linear increase in no. of pods/plant, no. of seeds/pod, thousand seeds weight, biological yiield

and harrvest iindex were noted over time and no. of pods per plant, no. of seed per pod, and

yield had significant increase in values during 2nd year of study (Table 4.30).



Soil properties and nutrients status are best indication of soil health. This study

showed that soil porosity, bulk density, pH, EC. (electrical conductivity), SOM. (soil organic

matter), .N (nitrogen), available K (potassium) and P (phosphorus) were significantly

differed among various allelopathic weed management strategies (Table 4.31). The year

effect was also statistically significant for soil porosity, bulk density, EC, SOM and available

P but non-significant for total soil N and available K (Table 4.31). The interaction

(allelopathic weed management strategies × year) was statistically significant for SOM and

available P. But for soil porosity, bulk density, soil pH, EC and available K interaction was

non-significant (Table 4.31).

Interaction (allelopathic weed management strategies × year) was significant for

SOM and available P. The highest value of SOM (1.15%) and available P (9.60 mg kg -1) was

observed during 2nd year when brassica residues at 6 tons ha-1 were applied as compared to

control (0.68% and 6.73 mg kg-1, respectively). Among all allelopathic weed management

strategies the statistically highest values soil EC (1.30 ds m-1) and available K (190.85 mg kg-

1) were obtained with the application of brassica residues at 6 tons ha -1. The statistically

lowest (1.09 ds m-1 and 119.59 mg kg-1) values for all parameters given above were observed

91

Table. 4.31. Mean square of soil properties and nutrient dynamics in the rhizosphere of mung bean modified by brassica crop water extracts and residues


Treatments (T) 4 0.02* 16.95* 0.32* 0.05* 0.35* 0.04* 7396.51* 10.45*

Years (Y) 1 0.02* 11.76* 0.21 NS 0.02* 0.10* 0.02 NS 293.78 NS 1.44*

T×Y 4 0.01NS 1.20 NS 0.04 NS 0.01NS 0.03* 0.01 NS 61.55 NS 0.55*

Error 18 0.003 1.014 0.110 0.009 0.009 0.002 105.181 0.107

a)SOV= source of variation, b)DF= degree of freedom, c)SBD= soil bulk density, d)TSP= total soil porosity, e)EC= soil electric conductivity, f)SOM= soil organic matter, g)TN= total soil nitrogen, h)AK= available potassium, i)AP= available phosphorus, *= indicate significant at p≤0.05, NS= non-significant

92

Table. 4.32. Effect of brassica water extracts and residues on soil properties and nutrient dynamics in the rhizosphere of mung bean at harvest


a)Control 1.48 1.47 1.47 A 42.82 44.06 43.44 Cb)BWE @ 10 L ha-1 1.47 1.47 1.47 A 43.52 44.10 43.81 CBWE @ 20 L ha-1 1.48 1.47 1.47 A 43.83 44.12 43.97 Cc)BR @ 4 tons ha-1 1.45 1.34 1.41 B 45.40 46.99 46.19 BBR @ 6 tons ha-1 1.42 1.28 1.34 C 45.96 48.51 47.23 AMean e)(Y) 1.46 A 1.40 B 44.30 B 45.56 ALSD (p≤0.05) T=0.06; Y=0.04 T=1.02; Y=0.77

Soil pH Soil EC (dS m-1)Control 7.77 7.74 7.76 A 1.07 1.10 1.09 CBWE @ 10 L ha-1 7.77 7.73 7.75 A 1.09 1.13 1.11 CBWE @ 20 L ha-1 7.77 7.73 7.75 A 1.11 1.14 1.12 CBR @ 4 tons ha-1 7.49 7.46 7.48 B 1.19 1.24 1.21 BBR @ 6 tons ha-1 7.46 7.26 7.36 C 1.29 1.32 1.30 AMean (Y) 7.65 A 7.58 B 1.15 B 1.19 ALSD (p≤0.05) T=0.09; Y=0.06 T=0.05; Y=0.03

Total soil organic matter (%) Total soil nitrogen (g kg-1)Control 0.68 d 0.69 d 0.68 C 0.19 0.20 0.19 CBWE @ 10 L ha-1 0.67 d 0.69 d 0.68 C 0.20 0.20 0.20 CBWE @ 20 L ha-1 0.68 d 0.70 d 0.69 C 0.20 0.20 0.20 CBR @ 4 tons ha-1 0.89 c 1.18 ab 1.03 B 0.28 0.33 0.30 BBR @ 6 tons ha-1 1.04 b 1.26 a 1.15 A 0.30 0.38 0.34 AMean (Y) 0.80 B 0.91 A 0.23 0.26LSD (p≤0.05) T=0.11; Y=0.07; T × Y=0.15 T=0.03

Available potassium (mg kg-1) Available phosphorous (mg kg-1)Control 118.38 121.95 119.59 C 6.72 d 6.75 d 6.73 CBWE @ 10 L ha-1 119.55 120.66 120.66 C 6.75 d 6.76 d 6.76 CBWE @ 20 L ha-1 119.52 121.00 120.76 C 6.77 d 6.78 d 6.77 CBR @ 4 tons ha-1 170.35 181.47 175.95 B 7.93 c 9.09 b 8.51 BBR @ 6 tons ha-1 183.33 198.33 190.85 A 9.09 b 10.12 a 9.60 AMean (Y) 142.22 148.48 7.45 B 7.89 ALSD (p≤0.05) T=12.44 T=0.39; Y=0.25; T × Y=0.56


93

in control (Table 4.32). A linear increase in soil EC and available K was observed over time

and these parameters (soil EC and available K) had highest values during the 2nd year of

experiment (Table 4.8). In case of soil density and soil pH decreasing trend was observed.

The lowest soil density (1.34 g cm-3) and soil pH (7.36) was observed with the application.of

brassica residues.at 6 tons ha-1 and the highest soil density (1.47 g cm-3) and soil pH (7.76)

was observed in control whiich was stattistically siimilar with brassica water exttract at 10

L/ha and 20 L ha-1 (Table 4.32).


Microbial population, activity and soil enzymes are robust indicators of soil fertility.

Microbial populations and activity at 20 days after sowing and at harvesting, soil enzymatic

activities (alkaline phosphatase and dehydrogenase) differed significantly among various

allelopathic weed management strategies (Table 4.33). The year effect was also significant

for all above parameters (Table 4.33). The interactive effect of allelopathic weed

management strategies and year was significant for fungal population after 20 days of sowing

and at harvesting. Dehydrogenase activity was also significant and was non-significant for

bacterial population and microbial activity after 20 days of sowing and at harvesting (Table

4.33).

Among the all allelopathic weed management strategies highest bacterial (71 cfu/g ×

105 and 35 cfu/g × 105, respectively) and microbial activity (5.09 mg CO2-C kg-1 d-1 and 4.18

mg CO2-C kg-1 d-1, respectively) was recorded with the application of brassica residues at 6

tons ha-1 at both stages i.e. 20 days after sowing and at harvesting. The lowest population and

microbial activity was observed in control whiich was statisttically sitmilar with brtassica

water extracts at 10 and 20 L/ha (Table 4.10). A linear increase in bacterial, fungal

population and microbial activity at 20 days after sowing and at harvesting was observed

over times and the highest bacterial, fungal population and microbial activity was observed

during second year (Table 4.34). In case of soil enzymes effect of allelopathic weed

management strategies resulted significant effect on activity of both alkaline phosphatase and

dehydrogenase enzyme. The highest value of alkaline phosphatase (180.96 µg NP g-1 soil h-1)

and dehydrogenase (35.00 µg TPF g-1 soil h-1) was observed with the application of brassica

residues at 6 tons ha-1. The lowest value of alkaline phosphatase (134.78 µg NP g-1 soil h-1)

94

Table. 4.33. Mean square of microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean affected by brassica crop water extracts and residues


Treatments (T) 4 965.97* 289.92* 66.87* 216.03* 2.67* 1.53* 2648.14* 137.62*

Years (Y) 1 154.13* 19.20* 61.63* 79.02* 0.25* 0.14* 69.98* 43.20*

T×Y 4 31.13NS 5.28NS 9.63* 12.26* 0.01NS 0.004NS 33.93NS 4.78*

Error 18 55.726 29.189 1.4704 4.642 0.45 0.27 28.831 2.400

a)SOV= source of variation, b)DF= degree of freedom, c)B= Bacteria, d)DAS= days after sowing, e)AH= after harvesting, h)AP= alkaline phosphatase, i)DG= dehydrogenase, *= indicate significant at p≤0.05, NS= non-significant

95

Table. 4.34. Effect of brassica water extracts and residues on microbial population, microbial activity and soil enzymatic activity in the rhizosphere of mung bean


a)Control 42 43 43 C 7 d 8 d 7 Cb)BWE @ 10 L ha-1 42 44 43 C 7 d 8 d 8 CBWE @ 20 L ha-1 44 45 45 C 8 d 9 cd 8 Cc)BR @ 4 tons ha-1 55 65 60 B 11 c 15 b 13 BBR @ 6 tons ha-1 67 78 71 A 16 b 20 a 18 AMean e)(Y) 50 B 55 A 10 B 12 ALSD (p≤0.05) T=9.05; Y=3.95 T=1.88; Y=1.19; T × Y=2.65

Bacteria (cfu/g x 105) g)AH Fungi (cfu/g x 104) AHControl 19 20 20 C 5 e 6 de 5 DBWE @ 10 L ha-1 21 21 21 C 6 de 6 de 6 CDBWE @ 20 L ha-1 21 21 21 C 6 de 8 de 7 CBR @ 4 tons ha-1 30 32 31 B 8 d 13 b 10 BBR @ 6 tons ha-1 33 37 35 A 10 c 16 a 13 AMean (Y) 25 B 27 A 7 B 10 ALSD (p≤0.05) T=3.21; Y=1.65 T=1.47; Y=0.93; T × Y=2.08

Microbial activity (mg CO2-C kg-1 d-1) 20 DAS Microbial activity (mg CO2-C kg-1 d-1) AHControl 3.58 3.70 3.64 C 2.97 3.12 3.04 CBWE @ 10 L ha-1 3.61 3.75 3.68 C 3.07 3.15 3.11 CBWE @ 20 L ha-1 3.65 3.78 3.72 C 3.09 3.18 3.14 CBR @ 4 tons ha-1 4.48 4.75 4.62 B 3.69 3.85 3.77 BBR @ 6 tons ha-1 4.96 5.22 5.09 A 4.08 4.28 4.18 AMean (Y) 4.06 B 4.24 A 3.38 B 3.51 A

LSD. (p≤0.05) T=0.45; Y=0.15 T=0.31; Y=0.11Alkaline phosphatase (μg NP/g soil/h) Dehydrogenase (μg TPF/g soil/h)

Control 134.77 134.80 134.78 C 20.65 d 22.35 d 21.51 CBWE @ 10 L ha-1 134.82 134.91 134.86 C 22.16 d 22.67 d 22.42 CBWE @ 20 L ha-1 135.12 135.18 135.15 C 22.86 d 23.25 d 22.56 CBR @ 4 tons ha-1 159.22 163.35 161.28 B 27.33 c 33.35 b 30.33 BBR @ 6 tons ha-1 175.48 186.44 180.96 A 33.67 b 38.33 a 35.00 AMean (Y) 147.88 B 150.94 A 24.74 B 27.99 ALSD (p≤0.05) T=6.51; Y=2.99 T=2.61; Y=1.65; T × Y=3.69

a)Control= (plots with no crop residues or extract application); b)BWE= brassica water extract; c)BR= brassica residues; d)T= treatments; e)Y= year; f)DAS= days after sowing; g)AH= after harvesting

and dehydrogenase (21.51 µg TPF g-1 soil h-1) was recorded in control (Table 4.34).

Interactive effect allelopathic weed management strategies and year resulted significant

96

effect on fungal population at both time after 20 days of sowing and at harvesting. The

highest fungal population (20 cfu/g × 104 and 16 cfu/g × 104, respectively) was recorded at

both time after 20 days of sowing and at harvesting with the application of brassica residues

at 6 tons ha-1 during 2nd year. The minimum fungal population (8 cfu/g × 104 and 6 cfu/g ×

104, respectively) was recorded at both time after 20 days of sowing and at harvesting with

control.

4.3.4. Economics and margiinal analysis

Economics and marrginal analysses were perrformed to compaere the net and

maerginal reeturns and domiinance of indiividual treatment. Among all treatments brassica

residue at 6 tons ha-1 gave maximum economical returns (347 $) during both years. While,

minimum net benefit was obtained from control.

In case of marginal analysis (Table 4.36), application of brassica residue at 6 tons ha -1

had maximum MRR (372 $). However, rest of treatments was dominated due to less benefit

and higher cost that vary and these were uneconomical treatments. So, brassica residue at 6

tons ha-1 may be the choice to get better economic returns.

97


Treatments Yield (kg ha-1)

Adjusted yield (kg ha-1)

Gross income e)$ ha-1

Total cost$ ha-1

Net benefits$ ha-1 Benefit cost ratio

a)Control 759 683 765 615 150 0.24

b)BWE @ 10 L ha-1 791 712 797 624 173 0.28

BWE @ 20 L ha-1 847 762 854 625 228 0.36

c)BR @ 4 tons ha-1 924 832 931 653 278 0.43

BR @ 6 tons ha-1 1007 906 1015 668 347 0.52


a)Control= (plots with no crop residues or extract application); b)BWE= brassica water extract; c)BR= brassica residues; d)PKR= Pakistani rupees; e)$= US dollar

Table. 4.36. Marginal analysis for two years (2014-2015)

Treatments

f)Cost that vary

($ ha-1)

Net benefits

($ ha-1)

g)Marginal cost

($ ha-1)


($ ha-1)


(%)

a)Control 0 150 - - -b)BWE @ 10 L ha-1 9 173 9 24 274

BWE @ 20 L ha-1 10 228 10 79 i)Dc)BR @ 4 tons ha-1 38 278 38 129 339

BR @ 6 tons ha-1 53 347 53 197 372

Remarksa)Control= (plots with no crop residues or extract application); b)BWE= brassica water extract; c)BR= brassica residues; d)PKR= Pakistani rupees; e)$= US dollar;

98

4.3.5. Discussion

Residues incorporation of allelopathic crops is an alternative and cost effective

method to reduce weed pressure in field crops and it also act as a green approach for

improving soil health. Our study showed a significant improvement in soil health and weeds

suppression potential of brassica residues incorporation and water extract. This approach had

significant reduction in weed number and weight of weed species in mung bean (Table 4.26)

owing to presence of isothiocyanates, isothayanates, isoprenoid and benzenoid with wide

spectrum of biological activities including allelopathy (Petersen et al., 2001; Cheema et al.,

2007). Brassicaceae family produces glucosinolates (GSLs) that are not biologically active.

When the plant tissue is disrupted, the GSLs are hydrolysed to a number of products. The

main breakdown products are Isothiocianates (ITCs) which are phytotoxic (Petersen et al.,

2001). Herebicidal actievity of 5 liquids iso-thiocyanates (ITCcs) (beinzoyl, ootolyl, motolyl,

tertooctyl, and 3-fluoorophenyl) on purrple and yeellow nut-sedge was evaleuated and it was

founds that all ITCcs were more eeffective in supprressing purrple nut-sedge than

yeellow nutseedge (Norsworthy et al., 2006). The rape-seed (Brrassica compeestris L.)

shoots extract and tur-nip (Brrassica compeestris L.) roots extract exhiibited inhibiition of

seeds germiination of cut leafs grounde cheerry (Physaliis angulate L.) by 58.7o% and 54.3o

%, r-espectively (Arslan et al., 2005). Liignans from Brassiica frutiiculosa showed strrong

inhiibition of germiination of Lacttuca satiiva (Cutillo et al., 2003). Narrwal et al. (2002)

statted that some a-ccessions of Brrassica junrcea and Brassirca nigrra caused siginificant

reductiion 75 to 82% at 75 DAG and 75-98% at harvest (120 D) in the densiity of winter

weed. Incorporation of rape-seed resiidues in soil priorr to sowiing of cotton reduceed

germiination of Amaranthus spp. while cotton germiination remaiined unaffeected; this was

prrobably due to releeasing of some grrowth inhiibitor subistance from decompeosed rape-

seed (Youniesabadi, 2005).

Our results indicate that increased quantities of crop residues have decreased bulk

density and increase total porosity of soil over time (Table 4.32). Shaver (2010) reported that

soil porosity is directly related to soil bulk density because as soil bulk density decreases, soil

porosity increases. In case of soil properties brassica residues as allelopathic weed

management strategy improved the SOM, N, available K and P in soil (Table 4.32). Crop

residues incorporation enhanced soil quality and improved the nutrient status of soil (Sidhu

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and Beri, 2005). Moisture retention is the main benefit of residues incorporation. It is caused

by decrease in runoff and evaporation of water from soil (Verhulst et al., 2011). Improved

moisture availability due to residue incorporation also indicated that the soil water holding

capacity was improved and the soil moisture was available for longer times to support plant

growth (Jin et al., 2013). This increase in moisture retention properties might decrease the

irrigational requirement of the crops, which should be investigated in future studies. The

improvement in nutrient accumulation (especially P and K) might be attributed to the fact the

enhanced moisture retention within the soil due to residue incorporation increased the

solubility of these nutrients within the soil (Zhou et al., 2002). Crop residue amendment also

increases N availability in soil and can reduce the fertilizers usage in soil (Beres and

Kazinczi, 2000). Sharma et al. (2000) also reported that by the addition of crop residues a

significant improvement in soil nitrogen and phosphorus was done. In our study a linear

decrease in the soil pH was observed by the application of brassica residues as allelopathic

weed management strategies (Table 4.32). Gong et al. (2008) reported that oil crop residue

incorporation in soil decreases the soil pH. The Parthenium hysterophorus residues in soil

also changed the chemistry of soil. It revealed that pH of P. hysterophorus infested soil

decreased, whereas the EC of soil increased (Batish et al., 2002a; Batish et al., 2002b).

In case of microbial population and enzymatic activities of soil by using brassica

residues as allelopathic weed management strategies in mung bean the results showed

significant improvement (Table 4.34). Microbial abundance and soil enzymes are soil

biological activities which are one of the important indicators of soil quality (Dick, 1994).

The residues incorporation of different crops in soil modified the bio-chemical attributes i.e.

soil microbial population and soil enzymatic activity (Doran, 1980; Dick et al., 1983). Soil

enzymes are used as important indicators for nutrient cycling processes and soil fertility

status, particularly in long term farming systems (organic and conventional) (Bohme et al.,

2005). Dehydrogenase enzyme is an important for the oxidation of soil organic matter





Incorporation of tobacco crop residues.in soil increased the activity of amylase and

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phosphodiesterase. At Akola, Maharashtra, Ravankar et al. (2000) reported that incubation of

soil with 1% organic residues of cotton stalk, safflower straw, sorghum stubble, soybean

stover, wheat straw, sugar cane trash, ground nut husk, sunflower straw, green gram stover,

parthenium with seed, grass complex with seed, and Xanthium with seed mixed showed wide

variation in the rate of decomposition, C:N ratio and microbial population at different

intervals. Fungal, bacterial, and actinomycetes populations increased at 30 days of


Various allelopathic weed management strategies used in this work had significant

impact on crop yield. This increase in crop yield might be done due to reduction in weed

competition during critical crop growth period and the improvement of soil health. The

effective reduction in weeds also increases the obtainability of resources such as light,

moisture, nutrients and space (Kruidhof et al., 2008). A recent research on wheat residues

application in Mediterranean environment by Stagnari et al. (2014) resulted that soil moisture

conservation ability was improved especially during critical growth period of test crop. The

residues which are decomposed completely in soil provide allelochemicals but also

contribute in crop nutrition. It provides nitrogen through release in the rhizosphere of tested

crop plant. By the application of residues as a biological weed management it immobilizes

the nitrogen which may reduce the availability of nitrogen (Khaliq et al., 2015). But, at later

stages of crop growth the obtainability of nitrogen was improved through mineralization, so

this prolonged supply of nitrogen is a big source of nutrition for test crop as well as next

crops.

So, brassica residues incorporation improve in soil properties viz. moisture retention,

restoring physical properties, enhance nutrient cycling, microbial activity (Alam et al., 2014;

Adugna and Abegaz, 2016; Nawaz et al., 2016) and suppression in weeds due to physical

hindrance by residues, reduced light penetration and suppressing ability of allelochemicals

which released from these plant residues (Kamara et al., 2000; Khaliq et al., 2014) harvested

better seed yield and achieve higher profitability in spring planted mung bean.

4.3.6. Conclusion

Various allelopathic weed management strategies used in this work had significant

impact on weed, crop yield and soil properties. Allelopathic crop residues are magical source

of nutrition and have efficient role in controlling weeds which is essential for sustainable

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crop production. A high suppression of weed density, fresh and dry weight was observed

when brassica residues @ 6 tons ha-1 were incorporated into soil. Apparently, the residues

favorably affected the soil properties viz. nutrient dynamics, microbial populations, soil

enzyme activities. The improvement in soil properties and suppression in weeds harvested

better seed yield and achieve higher profitability in spring planted mung bean. This method

may provide a possible.alternative.for.achieving.sustainable.weed.management in spring

planted mung bean with substantial improvement in soil properties.

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4.4. Experiment-IV: Isolation of allelochemical resistant strains of bacteria

and determination of their active role in rhizosphereFirst the isolation and purification of bacterial strains from mung bean rhizosphere

soil which is amended with allelopathic crops (sorghum, sunflower and brassica) water

extracts and residues was done. Second the resistance of bacterial against synthetic

allelochemicals and allelopathic crops water extracts was checked. Third the biochemical

characterization (colony morphology and cellulase activity), bioassays for plant growth

promoting traits (zinc, phosphate solubilizing and nitrogen fixation activity) were observed.

Fourth tests of seed germination and seedling growth were done.


Results:

4.4.1. Isolation and purification of culturable bacterial strains

Culturable bacterial strains were obtained both from rhizospheric soil samples at 20 DAS as

well as at harvesting of mung bean crop which was amended with allelopathic crops

(sorghum, sunflower and brassica) water extracts and residues. Eleven bacteria were obtained

from rhizosphere soil samples at 20 DAS while fifteen bacteria from rhizosphere soil

samples at harvesting (Table 4.37). From fifth dilution (10-5) of the rhizosphere soil solution

the colonies were selected on the basis of morphology and isolates were sub-cultured on half-

strength R2A agar. The single selected colonies were transferred to small polystyrene petri-

dishes and checked for their purity. After three time colony purification on half-strength R2A

twenty six strains of bacteria with relatively high growth in the medium were isolated and

preserved by pouring 400 µL glycerol and 600 µL half-strength R2A cultures in 1 mL

Eppendorf at -40oC (Table 4.37).

4.4.2. Test of bacterial resistance against synthetic allelochemicals and allelopathic

crops water extracts

For test resistance of bacterial strains against synthetic allelochemicals (p-coumaric,

ferulic and syringic acid) and allelopathic crops (sorghum, sunflower and brassica) water

extracts, the disc diffusion method was used. The zone of inhibition was observed and the

resistance and sensitivity of isolated bacteria towards synthetic allelochemicals and

allelopathic crops water extracts used was determined. It was found that all isolates were

found to be resistant to p-coumaric, ferulic and syringic acid and brassica water extracts

103

Table. 4.37. Test the resistance of bacterial isolates from mung bean rhizosphere against synthetic allelochemicals and allelopathic crop water extracts

Strains ID Source of isolation p-cumaric Ferulic acid Syringic acid a)SWE b)SFWE c)BWEC-14HM Rhizosphere ++ ++ ++ ++ ++ ++C-17HM Rhizosphere ++ ++ ++ ++ ++ ++10-5HM Rhizosphere + + + + + +9-2M Rhizosphere + + + + + +14-5HM Rhizosphere + + + + + +15-15M Rhizosphere + + + + + +10-10M Rhizosphere ++ ++ ++ ++ ++ ++15-13M Rhizosphere + + + + + +4-3M Rhizosphere + + + + + +10-17M Rhizosphere + + + - + +4-21M Rhizosphere + + + + - +10-9M Rhizosphere + + + + + +4-1HM Rhizosphere + + + + + +5-18HM Rhizosphere + + + + + +14-8HM Rhizosphere + + + + + +9-1HM Rhizosphere + + + + + +4-6HM Rhizosphere + + + + + +4-17HM Rhizosphere ++ ++ ++ ++ ++ ++15-12HM Rhizosphere + + + - - +10-11HM Rhizosphere + + + + + +10-9HM Rhizosphere + + + + + +15-21HM Rhizosphere + + + + + +15-23M Rhizosphere + + + + + +15-18M Rhizosphere + + + - - +15-15HM Rhizosphere + + + + + +9-17M Rhizosphere + + + - + +10-5HM Rhizosphere + + + + + +14-17HM Rhizosphere + + + + + +9-2HM Rhizosphere + + + + + +

a)SWE= sorghum water extract, b)SFWE= sunflower water extract, c)BWE= brassica water extract, (++) = stand for highest resistant, (+) = stand for resistant, (-) = stand for susceptible

104

(Table 4.37). Bacterial isolates viz. 4-17HM, C-14HM, C-17HM and 10-10M were showed

highest resistant to all synthetic allelochemicals and allelopathic crops water extracts while

10-17M, 9-17M, 15-12HM and 15-18M were showed little bit sensitivity to sorghum water

extract and 4-21M, 15-12HM and 15-18M were showed little bit sensitivity to sunflower

water extract (Table 4.37).

4.4.3. Biochemical characterization

Colony morphology: The bacteria showed white, off white, milky white, yellow, dark

yellow and green colonies with variable sizes (small, medium and large) and shapes (round,

irregular, waxy) on half strength R2A agar plates (Table 4.38).

Cellulase activity: To check the cellulase activity an aliquot of 100 µL of each bacterial

isolate was spot inoculated on agar plates containing CMC (10 g L -1), K2HPO4 (1 g L-1),

KH2PO4 (1 g L-1), MgSO4 7H2O (0.2 g L-1), NH4NO3 (1 g L-1), FeCl3 6H2O (0.05 g L-1), CaCl2

(0.02 g L-1), and agar (20 g L-1). The plates were incubated at 28oC for 96 hrs. After

incubation each plate was flooded with 0.1% Congo red for 15 to 20 minutes and then 2-3

time washed with NaCl (1 M). A clear zone was observed around colonies which showed

cellulase activity (Table 4.3). It was found that the bacterial strains viz. 10-5HM, 15-15M, 4-

21M, 14-8HM, 10-5HM, 10-9HM, 14-17HM showed cellulase activity but the bacterial

strains viz. 10-17M, 4-17HM and 15-18HM showed highest cellulase activity. While the

bacterial strains viz. C-14HM, C-17HM, 9-2M, 14-5HM, 10-10M, 15-13M, 4-3M, 10-9M, 4-

1HM, 5-18 HM, 9-1HM, 4-6HM, 4-7HM, 10-11HM, 15-21HM, 9-17M, 9-2M, 15-23M and

15-12HM showed no hollow zone (Table 4.40).

4.4.4. Bioassays for plant growth promoting traits

Following bioassays for plant growth promoting traits were studied during the

experimentation:

Zinc solubilizing activity: For zinc (Zn) solubilizing activity 100 µL of each bacterial

isolate was inoculated on agar pates consist of glucose (10 g L-1); (NH4)2SO4 (0.5 g L-1); KCl

(0.2 g L-1); MgSO4 7H2O (0.1 g L-1); trace of MnSO4 and FeSO4, yeast extract (0.5 g L-1); and

agar (15 g L-1)

106

Table. 4.38. Morphological characteristics of allelochemical resistant bacterial isolates from mung bean rhizosphere

Strains ID Source of isolation Colony size Colony shape Colony colourC-14HM Rhizosphere Large Round Off whiteC-17HM Rhizosphere Small Round White10-5HM Rhizosphere Large Round Dark yellow9-2M Rhizosphere Medium Irregular Off white14-5HM Rhizosphere Small Wavy Yellow15-15M Rhizosphere Large Round Off white10-10M Rhizosphere Medium Round White15-13M Rhizosphere Medium Round Milky white4-3M Rhizosphere Small Wavy Off white10-17M Rhizosphere Large Irregular White4-21M Rhizosphere Small Round Off white10-9M Rhizosphere Small Irregular Off white4-1HM Rhizosphere Medium Wavy White5-18HM Rhizosphere Medium Round White14-8HM Rhizosphere Small Irregular Milky white9-1HM Rhizosphere Medium Wavy Yellow4-6HM Rhizosphere Medium Irregular Off white4-17HM Rhizosphere Large Round Yellow15-12HM Rhizosphere Medium Round Green10-11HM Rhizosphere Small Round Yellow10-9HM Rhizosphere Large Wavy White15-21HM Rhizosphere Large Irregular Off white15-23M Rhizosphere Small Round Off white15-18M Rhizosphere Medium Round Off white15-15HM Rhizosphere Medium Round White9-17M Rhizosphere Small Irregular Milky white10-5HM Rhizosphere Medium Round White14-17HM Rhizosphere Medium Irregular Off white9-2HM Rhizosphere Small Round Yellow

Table. 4.39. Assessment of zinc activity of allelochemical resistant bacterial isolates from mung bean rhizosphere Strains ID Source of isolation a)ZnO b)ZnCo3

107

C-14HM Rhizosphere ++ -C-17HM Rhizosphere ++ ++10-5HM Rhizosphere + -9-2M Rhizosphere + -14-5HM Rhizosphere + -15-15M Rhizosphere + -15-13M Rhizosphere + -4-3M Rhizosphere + -10-17M Rhizosphere - -4-21M Rhizosphere + +10-9M Rhizosphere + +4-1HM Rhizosphere + +5-18HM Rhizosphere ++ -14-8HM Rhizosphere + -9-1HM Rhizosphere + +4-17HM Rhizosphere ++ ++15-12HM Rhizosphere + -10-11HM Rhizosphere + +10-9HM Rhizosphere - -10-10M Rhizosphere + +15-21HM Rhizosphere + -15-23M Rhizosphere + -15-18M Rhizosphere + -15-15HM Rhizosphere + +9-17M Rhizosphere + +4-6HM Rhizosphere - -10-5HM Rhizosphere - -14-17HM Rhizosphere - -9-2HM Rhizosphere + -

a)Zno= Zinc oxide, b)ZnCo3= Zinc carbonate, (++) = show highest zinc activity, (+) = show zinc activity, (-) = no zinc activity

supplemented with 0.1% Zn (oxide and carbonate). A holo zone was observed when plates

were incubated (28 ± 2oC) for 7-10 days (Table 4.39). In case of zinc oxide, it was found that

the bacterial strains viz. 10-5HM, 9-2M, 14-5HM, 15-15M, 15-13M, 4-3M, 4-21M, 10-9M,

4-1HM, 14-8HM, 9-1HM,15-12HM, 10-11HM, 10-10M, 15-21HM, 15-23M, 15-18M, 15-

108

15HM and 9-17M showed zinc solubilizing activity but the bacterial strains viz. C-14HM, C-

17HM, 4-17HM and 15-18HM showed highest zinc solubilizing activity. While the bacterial

strains viz. 10-17M, 10-9HM, and 4-6HM showed no clear zone. When the source was zinc

carbonate, it was found that the bacterial strains viz. 4-21M, 10-9M, 4-1HM, 9-1HM, 10-

11HM, 10-10M and 9-17M showed zinc solubilizing activity but the bacterial strains viz. C-

17HM, 15-15HM and 4-17HM showed highest zinc solubilizing activity. While the bacterial

strains viz. C-14HM, 10-2HM, 9-2M, 14-2HM, 12-12M, 4-2M, 10-17M, 2-12HM, 14-2HM,

12-21HM, 12-22M, 15-18M, 4-6HM, 10-5HM, 14-17HM and 9-2HM showed no halo zone

(Table 4.39).

Phosphate solubilizing activity: Each bacterial culture (100 µL) was spot inoculated in the

center of agar plates containing tricalcium phosphate (Ca3 (PO4)2) as insoluble phosphate

source with the following ingredients: (g L-1) glucose (10), (NH4)2SO4 (0.5), NaCl (30), KCl

(0.3), FeSO4 7H2O (0.03), MnSO4 4H2O (0.03), MgSO4 7H2O (0.3), Ca3 (PO4)2 (10), and agar

(20), H2O (1000 mL), pH (7.0-7.5). Formation of halo zone around colonies after incubation

at 28 ± 2oC for 7-10 days was a sign of phosphate solubilizing activity (Table 4.40). It was

found that the bacterial strains viz. C-14HM, 10-5HM, 14-5HM, 10-10M, 4-21M, 5-18HM,

4-6HM, 10-9HM and 15-18HM showed zinc solubilizing activity but the bacterial strains viz.

C-17HM and 4-17HM showed highest zinc solubilizing activity. While the bacterial strains

viz. 10-5HM, 15-15M, 15-13M, 4-3M, 10-17M, 10-9M, 4-1HM 14-8HM, 9-1HM, 10-5HM,

14-17HM, 4-7HM, 10-11HM, 15-21HM, 9-17M, 9-2M, 15-23M and 15-12HM showed no

halo zone (Table 4.40).

Nitrogen fixation activity: 100 µL of each pure bacterial culture was inoculated into NFA

(Nitrogen Free Agar) plates. The plates were incubated at 28 ± 2oC for 7-10 days and

observed for the formation of halo zone around the colonies (Table 4.40). It was found that

the bacterial strains viz. C-14HM, 14-5HM 10-10M, 15-13M, 4-3M, 10-17M,10-9M, 4-

1HM, 4-6HM, 14-17HM and 4-

Table. 4.40. Assessment of cellulase enzyme, nitrogen fixation and phosphate solubilization activity of allelochemical resistant bacterial isolates from mung bean rhizosphere

Strains ID Source of isolation Cellulase enzyme activity

Nitrogen fixation activity

Phosphorus solubilization activity

C-14HM Rhizosphere - + +C-17HM Rhizosphere - ++ ++10-5HM Rhizosphere + - +

109

9-2M Rhizosphere - - -14-5HM Rhizosphere - + +15-15M Rhizosphere + - -10-10M Rhizosphere - + +15-13M Rhizosphere - + -4-3M Rhizosphere - + -10-17M Rhizosphere ++ + -4-21M Rhizosphere + - +10-9M Rhizosphere - + -4-1HM Rhizosphere - + -5-18HM Rhizosphere - - +14-8HM Rhizosphere + - -9-1HM Rhizosphere - - -4-6HM Rhizosphere - + +4-17HM Rhizosphere ++ ++ ++10-5HM Rhizosphere + - -10-9HM Rhizosphere + - +14-17HM Rhizosphere + + -15-18HM Rhizosphere ++ - +4-7HM Rhizosphere - + -10-11HM Rhizosphere - - -15-21HM Rhizosphere - - -9-17M Rhizosphere - - -9-2M Rhizosphere - - -15-23M Rhizosphere - - -15-12HM Rhizosphere - - -

(++) = show highest activity, (+) = show activity, (-) = no activity

110

7HM showed zinc solubilizing activity but the bacterial strains viz. C-17HM and 4-17HM

showed highest zinc solubilizing activity. While the bacterial strains viz. 10-5HM, 9-2M, 15-

15M, 4-21M, 5-18HM, 14-8HM, 9-1HM, 10-5HM, 14-17HM, 10-9HM, 15-18HM, 10-

11HM, 15-21HM, 9-17M,9-2M, 15-23M and 15-12HM showed no halo zone (Table 4.40).

4.4.5. Discussion

Allelopathy is a biological phenomenon in which an organism releases one or more

bio-chemicals that influences the other organisms. These biochemical are recognized as

allelochemicals and can have positive or negative effects on the target organisms (Ferguson

and Rathinasabapathi, 2003). These chemicals are released into environment by plant organs

through volatiles, leachates, root exudates, and residues decomposit (Cecile et al., 2003). The

performance of interacting plant species can be affected by the.release.of allelo-chemicals in

the.rhizosphere soil by plants (Inderjet, 2005). Plant allelochemicals can act as selective

agents to drive an adaptation in associated plant species to cope with organic compounds

released by their ‘‘chemical neighbor’’ (Vivanco et al., 2004). In this situation, microbial

community can affect interaction between plants and plant allelochemicals as soil microbes

may degrade the allelochemicals after entering the soil (Kaur et al., 2009). Soil

microorganisms consume organic molecules and therefore chemicals may not accumulate to

phytotoxic levels (Inderjet, 2005). In this contest we isolate twenty six bacterial strains from

the rhizosphere of mung bean which was amended with allelopathic crops (sorghum,

sunflower and brassica) water extracts and residues. We check the resistance of bacterial

strains against three synthetic allelochemicals (p-coumaric, ferulic and syringic acid) and

three allelopathic crops (sorghum, sunflower and brassica) water extracts by disk diffusion

method (Table. 4.3). We observed that most of them were resistant against synthetic

allelochemicals and allelopathic crop water extracts. Our results were correlated with De

Gelder (2006). He said that microorganism adapt to change environmental condition by

horizontal gene transfer which provides them with new traits (pesticides/antibiotics/chemical

resistance) so they can survive and colonize their new environments.

The rhizosphere is a unique ecological niche that shapes microbial community

structure through the interactions of plant species, root exudates, soil properties, and many

other factors (Mendes et al., 2013). Organic acids released from the roots of plants can

support highest microbial biomass and metabolic activity. Due to this reason more active and

112

distinct microbes are present in rhizosphere soil as compared to bulk soil. Interactions

between plants and microbes in rhizosphere soil control important bio-geochemical

processes, such as nutrient cycling and greenhouse gas emissions. The bacterial strains have

beneficial effect on maintenance of satisfactory level of nutrients especially phosphorus

(Saravanan et al., 2007). In our study, four bacterial isolates were found efficient solubilizer

of phosphate (Table 4.3). The ability of bacterial strains to solubilize insoluble phosphorus

and convert it to plant available form is an important characteristic under conditions where

phosphorus is a limiting factor. Phosphate solubilizing strains can increase the availability of

phosphorus to plant by mineralizing organic phosphorus compounds and by converting

inorganic phosphorus into more available form. Phosphate solubilization is mainly due to the

production of microbial metabolites including organic acids which decreases the pH of the

culture media (Shahid et al., 2012). The presence of phosphate solubilizing microbes in soils

might be considered a positive indicator of using these microbes as bio-fertilizers for crop

production and beneficial for soil sustainability. The results of nitrogen fixation by NFA

method indicated that fairly large population of mung bean associated nitrogen fixers are

present in the soils and can be beneficial to improve nitrogen nutrition of mung bean and

other crops.

4.4.6. Conclusion

To sum up, this study identified novel bacterial strains 4-17HM, C-14HM, C-17HM

and 10-10M isolated from rhizosphere soil of mung bean which were amended by

allelopathic crop water extracts and residues, showed the highest resistance against synthetic

allelochemicals and allelopathic crop water extracts and the maximum nitrogen fixing, zinc

and phosphate solubilization activity. Due to their resistance and active role in the

rhizosphere, as nitrogen fixing, zinc and phosphate solubilization they could be applied with

the allelopathic crop water extracts and residues to soil functional and healthy.

113

Chapter-V

SUMMERY

The present research work was.conduccted to evaluaete the effect of allel-opathic

crops (sorrghum, sunflowerr and brrassica) on weeds, productivity and rhizosphere of mung

bean during 2014 and 2015. Laboratory trials were conducted in Plant and Microbial Ecology

Lab, Department of Agronomy, University of Agriculture, Faisalabad. Three sets of field

experiments were conducted and repeated at Student Research Farm, Department of

Agronomy, University of Agriculture, Faisalabad, during 2014 and 2015. In all field

experiments, crop water extracts (10 and 20 L ha-1) were foliar applied at 15 DAS and

residues incorporation (4 and 6 tons ha-1) was done before sowing. All other features were

kept same. Salient results of the studies are summarized below.

5.1. Experiment-I: Effect of sorghum crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) Weed data including weed density fresh and dry weight significantly suppressed by

the application of sorghum water extracts (10 and 20 L ha-1) and residues incorporation

(4 and 6 tons ha-1) in mung bean as compared with control.

Foliar application of sorghum water extracts (10 and 20 L ha-1) and residue

incorporation (4 and 6 tons ha-1) significantly influenced different morphological,

yield related traits, seed yield of mung bean as compared with control.

At mung bean harvested the soiil physiical, chemical and biological propertiies were

significantly improved by the application of sorghum residues (4 and 6 tons ha-1) as

compared with control but in case of sorghum water extracts (10 and 20 L ha -1) there

was no significant effect as compared with control.

Among all the treatments sorghum residues incorporation at 6 tons ha-1 showed the

highest suppression of weed density, fresh and dry weight (62, 67, 65%, respectively)

during both study years.

In case of soil properties significant improvement was observed by the application of

sorghum crop residues at 6 tons ha-1 as compared with control during both study

years.

114

Maximum mung bean seed yield was improved (37%) by the application of sorrghum

crop reesidues at 6 tons ha-1 as comparred with contrrol durring both study years.

Sorghum residues incorporation at 6 tons ha-1 had higher net benefits (306 $ ha-1)

followed by 4 tons ha-1 during both years (2014 and 2015). While MRR was

maximum in the appliication of sorrghum residues at 6 tons/ha during both the years as

compared with marginal rate of return of control.

5.2. Experiment-II: Effect of sunflower crop water extracts and residues on

weeds, productivity and rhiizosphere of mung bean (Vigna radiata L.) In this experiment weed data including weed density fresh and dry weight

significantly suppressed by the application of sunflower water extracts (10 and 20 L

ha-1) and residues incorporation (4 and 6 tons ha-1) in mung bean as compared with

control.

Morphological, yield related traits and seed yield of mung bean were significantly

affected by the foliiar appliication of sunflowerr waterr extrracts at 10 and 20 L/ha and

also by the incorporation of sunflower residues at 4 and 6 tons ha -1 as compared with

control.

Soil properties like physical, chemical and biological at the harvesting of mung bean

were significantly promoted by the application of sunflower residues at 4 and 6 tons

ha-1 as compared with control which was statistically at par sunflower water extracts

at 10 and 20 L ha-1.

Among all the treatments sunflower residues incorporation at 6 tons ha-1 showed the




sunflower crop residues at 6 tons ha-1 as compared with control during both study

years.

Maximum mung bean seed yield was improved (36%) by the application of sunflower

crop residues at 6 tons ha-1 ass comparred wiith contrrol durring botth study years.

Among all applied treatments sunflower residues incorporation at 6 tons ha -1 gave

higher net benefits (339 $ ha-1) .during both years. .In case of marrginal analysiis

115

sunflower residues incorporation at 6 tons ha-1 gave maximum MRR (Rs.

327723.53%) during both study years.

5.3. Experiment-III: Effect of brassica crop water extracts and residues on

weeds, productivity and rhizosphere of mung bean (Vigna radiata L.) Weed data including weed density fresh and dry weight significantly suppressed by

the application of brassica water extracts (10 and 20 L ha -1) and residues

incorporation (4 and 6 tons ha-1) in mung bean as compared with control.

Foliar.application.of brassica water extracts (10 and 20 L ha-1) and residues

incorporation (4 and 6 tons ha-1) significantly influenced different morphological,

yield related traits, seed yield of mung bean as compared with control.

At mung bean harvested the soil physical, chemical and biological properties were

significantly improved by the application of brassica residues (4 and 6 tons ha -1) as

compared with control but in case of sorghum water extracts (10 and 20 L ha -1) there

was no significant effect as compared with control.

Among all the treatments brassica residues incorporation at 6 tons ha -1 showed the




brassica crop residues at 6 tons ha-1 as compared with control during both study years.

Maximum mung bean seed yield was improved (33%) by the application of brassica

crop residues at 6 tons ha-1 as compared with control during both study years.

Brassica residues incorporation at 6 tons ha-1 had higher net benefits (347 $ ha-1)

followed by 4 tons ha-1 during both years (2014 and 2015). While MRR was

maximum in the application of brassica residues at 6 tons ha-1 during both the years as

compared with marginal rate of return of control.

5.4. Experiment-IV: Isolation of allelochemical resistant strains of bacteria

and determination of their active role in rhizosphere To sum up, this study identified bacterial strains 4-17HM, C-14HM, C-17HM and 10-

10M isolated from rhizosphere soil of mung bean which was amended by allelopathic

crop water extracts and residues, showed the highest resistance against synthetic

116

allelochemicals and allelopathic crop water extracts and the maximum nitrogen

fixing, zinc and phosphate solubilization activity.

5.5. Conclusion Residues incorporation of different allelopathic crops (sorghum, sunflower and

brassica) was more effective than their water extracts application in weed

suppression, improvement in soil health and productivity of mung bean.

Application of crop residues at 6 tons/ha was the mostt effeective and economeical

treatments with hiighest net benefit and margiinal rate of returrns. While, other

treatments were uneconomical due to concomiitantly lowerr net returrns associiated

with each of these.

Due to their resistance and active role of bacterial strains 4-17HM, C-14HM, C-

17HM and 10-10M in the rhizosphere, as nitrogen fixir, zinc and phosphate

solubilizer it could be applied with the allelopathic crop water extracts and residues to

manage weeds and improve soil health.

5.6. Future research priorities Combine application.of.allelopathic crop water.extracts, residues, and resistant

bacterial strains to manage weeds under field condition

The roles of allelochemicals produced or induced by AM-fungi or by endophytes and

to understand their effects on microbial and plant communities

Interaction of soil factors and allelopathic activities of crop plants

117

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