EVALUATION OF DIFFERENT CROPPING PATTERNS UNDER …

1

EVALUATION OF DIFFERENT CROPPING PATTERNS

UNDER COMMAND AREA OF SMALL DAMS IN POTHWAR:

A CASE STUDY OF PIRA FATEHAL DAM

MASOOD AKHTER

08-arid-738

Department of Agronomy

Faculty of Crop and Food Sciences

Pir Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2015

EVALUATION OF DIFFERENT CROPPING PATTERNS

UNDER COMMAND AREA OF SMALL DAMS IN POTHWAR:

2

A CASE STUDY OF PIRA FATEHAL DAM

by

MASOOD AKHTER

(08-arid-738)

A thesis submitted in partial

fulfilment of the requirements for

the degree of

Doctor of Philosophy

in

Agronomy

Department of Agronomy

Faculty of Crop and Food Sciences

Pir Mehr Ali Shah

Arid Agriculture University Rawalpindi

Pakistan

2015

CERTIFICATION

I hereby undertake that this research is an original and no part of this thesis

falls under plagiarism. If found otherwise, at any stage, I shall be responsible for

further consequences.

3

Student’s name: Masood Akhter Signature: __________________

Registration No: 08-arid-738 Date: __________________

Certified that the content and form of thesis entitled “Evaluation of Different

Cropping Patterns Under Command Area of Small Dams in Pothwar: A Case

Study of Pira Fatehal Dam” submitted by Mr. Masood Akhter have been found

satisfactory for the requirement of the degree.

Supervisor: _______________________ (Prof. Dr. Fayyaz-ul-

Hassan)

Member: ________________________

(Dr. Muhammad Rasheed)

Member: _______________________ (Dr. Rifat Hayat)

Chairman: ___________________

Dean: _______________________

Director, Advanced Studies: ____________________________

4

I dedicate this humble effort,

the fruit of my thoughts and

study to my affectionate parents

who inspired me to higher ideals

of life

CONTENTS

Page

List of Tables xi

List of Figures xiv

List of Appendices xv

List of Abbreviations xvi

5

Acknowledgment xviii

Abstract xx

1 INTRODUCTION 1

2 REVIEW OF LITERATURE 8

2.1 SMALL DAMS AND THEIR IMPACT ON AGRICULTURE 8

2.2 CURRENT CROPPING PATTERNS OF POTHWAR 12

2.3 LEGUME BASED CROPPING PATTERNS 15

2.4 WATER USE EFFICIENCY 19

2.5 CROPPING PATTERNS AND THEIR ECONOMICS 20

3 MATERIALS AND METHODS 23

3.1 EXPERIMENTAL DESIGN AND CROPPING PATTERNS 23

3.2 SOIL DATA 27

3.2.1 Soil Ph 27

3.2.2 Electrical Conductivity – ECe 27

3.2.3 Soil texture 27

3.2.4 Moisture Contents (Soil Water Contents) 28

3.2.5 Available Phosphorus 28

3.2.6 Extractable Potassium 29

3.2.7 Nitrate Nitrogen 29

3.2.8 Total Organic Carbon (TOC) 30

3.3 CROP DATA 30

3.3.1 Summer Crops 32

3.3.2 Winter Crops 32

3.3.3 Data of Maize (Grain) (Zea mays) 32

3.3.4 Data of Sorghum (Fodder) (Sorghum bicolor ) 33

3.3.5 Data of Mash-bean (Vigna mungo), Mung-bean (Vigna radiate),

Chick-pea (Cicer arietinum) and Canola (Brassica napus)

34

3.3.6 Data of Wheat (Triticum aestivum) 36

3.4 NPK UPTAKE BY CROP PLANTS 38


3.6 ECONOMIC ANALYSIS 38

3.6.1 Partial Budget 39

3.6.2 Marginal Analysis 39

3.6.3 Benefit Cost Ratio 40

3.7 METEOROLOGICAL DATA 40

3.8 STATISTICAL ANALYSIS 40

6

4 RESULTS AND DISCUSSION 44

4.1 WINTER CROPS 44

4.1.1 Germination Count per Square Meter of Winter Crops in Response

to the Cropping Patterns, Year and Environment (Irrigated and

Rainfed)

44

4.1.2 Tillers per Square Meter of Wheat and Branches per Square Meter of

Chick Pea and Canola in Response to Cropping Patterns, Year and

Environment (Irrigated and Rainfed)

46

4.1.3 Spikelets per Square Meter of Wheat Crop and Pods per Square

Meter of Chick Pea and Canola in Response to Cropping Patterns

Year and Environment (Irrigated and Rainfed)

50

4.1.4 Grains per Square Meter of Winter Crops in Response to Cropping

Patterns, Year and Environment (Irrigated and Rainfed)

52

4.1.5 Thousand Grains Weight (TGW) of Winter Crops in Response to

Cropping Patterns, Year and Environment (Irrigated and Rainfed)

56

4.1.6 Biological Yield of the Winter Crops in Response to the Cropping 58


4.1.7 Grain Yield of Winter Crops in Response to Cropping Patterns,


62

4.1.8 Harvest Indices of Winter Crops in Response to Cropping

Patterns, Year and Environment (Irrigated and Rainfed)

64

4.2 SUMMER CROPS 68

4.2.1 Number of Plants per Square Meter of Maize in Maize- Wheat (CP-

4) and Maize-Chick Pea (CP-5) Cropping Patterns under Both the

Environments during Two Years

68

4.2.2 Number of Cobs per Square Meter of Maize in Maize-Wheat (CP-

4) and Maize-Chick Pea (CP-5) Cropping Patterns under Both the


69

4.2.3 Grains per Square Meter of Maize Crop in Maize-Wheat (CP-4) and Maize-

Chick Pea (CP-5) Cropping Patterns under Both the


70

4.2.4 Thousand Grains Weight of Maize Crop in Maize-Wheat (CP-4)

and Maize-Chick Pea (CP-5) Cropping Patterns under Both


71

7

4.2.5 Biological Yield of Maize in Maize-Wheat (CP-4) and Maize-

Chick Pea (CP-5) Cropping Patterns under Both Environments

during Two Years

73

4.2.6 Grain Yield of the Maize in Maize-Wheat (CP-4) and Maize-Chick

Pea (CP-5) under Both The Environments during Two Years

74

4.2.7 Harvest Index of Maize Crop in Maize-Wheat (CP-4) and Maize- 75

Chick Pea (CP-5) Cropping Patterns under Both the Environments

during Two Years

4.2.8 Plant Population per Square Meter of Sorghum under Sorghum- Wheat

77

Cropping Pattern (CP-3) under Both Environments during Two Years

4.2.9 Fodder Yield of Sorghum in Sorghum-Wheat Cropping Pattern (CP-3)

79 under Both Environments during Two Years

4.2.10 Number of Plants per Square Meter of Mash and Mung Bean

in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)

Cropping Patterns under Both Environments during Two Years

81

4.2.11 Number of Branches per Square Meter of Mash and Mung

Bean in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)

Cropping Patterns under Both Environments in Two Years

82

4.2.12 Number of Pods per Square Meter of Mash Bean and Mung Bean in

Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP- 6) Cropping

Patterns under Both Environments during Two Years

84

4.2.13 Thousand Grain Weight of Mash Bean and Mung Bean Crops in

Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)


86

4.2.14 Number of Nodules per Square Meter of Mash and Mung

Bean in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)


87

4.2.15 Biological Yield (kg/ha) of Mash Bean And Mung Bean Crops

in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)


89

8

4.2.16 Grain Yield (kg/ha) of Mash Bean and Mung Bean Crops in


Cropping Pattern under Both Environments during Two Years

90

4.2.17 Harvest Indices (%) of Mash Bean and Mung Bean in



92

4.3 NUTRIENT UPTAKE 93

4.3.1 N-Uptake by Crops under Different Cropping Patterns in

Irrigated and Rainfed Environments during Two Years

93

4.3.2 P-Uptake by Crops under Different Cropping Patterns in 96


4.3.3 K-Uptake by Crops under Different Cropping Patterns in


97

4.4 QUALITY PARAMETERS 101

4.1.1 Wheat Grain Protein Contents under Different Cropping Patterns

in Irrigated and Rainfed Environments during Two Years

101

4.4.2 Oil Contents of Canola Seed under different Cropping Patterns

in Irrigated and Rainfed Environments during TwoYears

103

4.4.3 Oleic Acid (%) of Canola Seed under Different Cropping

Patterns in Irrigated and Rainfed Environments during Two

Years

103

4.4.4 Linoleic Acid (%) of Canola Seed under Different Cropping

Patterns in Irrigated and Rainfed Environments during Two

Years

104

4.4.5 Erucic Acid (%) of Canola in Different Cropping Patterns

105

under Irrigated and Rainfed Environments during Two Years

4.4.6 Fiber Contents of Sorghum under Sorghum-Wheat Cropping 107

Pattern in Irrigated and Rainfed Environments during Two

Years

4.4.7 Acid Detergent Fiber (ADF) of Sorghum under Sorghum-Wheat

Cropping Pattern in Irrigated and Rainfed Environments during

Two Years

107

9

4.4.8 Nutrient Detergent Fiber (NDF) of Sorghum under Sorghum-

Wheat Cropping Pattern in Irrigated and Rainfed


108


4.6 ECONOMIC ANALYSIS 114

4.6.1 Partial Budget of Different Crops and Cropping Patterns under 114

Irrigated Environment during Two Years

4.6.1.2 Partial Budget of Different Crops and Cropping Patterns under

Rainfed Environment during Two Years

115

4.6.2 Benefit Cost Ratio (BCR) of Different Cropping Patterns under

Irrigated Environment during Two Years

116

4.6.2.1 Benefit Cost Ratio (BCR) of Different Cropping Patterns under

Rainfed Environment during Two Years

116

4.6.3 Marginal Analysis of Different Cropping Patterns 117

SUMMARY 123

LITERATURE CITED 126

APPENDICES 146

Appendix-1 146

Appendix-2 147

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LIST OF TABLES

Table No. Page

3.1 Detail of crops and inputs used in study 25

3.2 Physical characteristics of experimental site 26

3.3 Chemical characteristics of experimental site 26

3.4 Chemical characteristics of dam water 26

3.5 Soil fertility status before the experiments 31

3.6 Soil fertility status after the experiments 31

4.1. Germination count per square meter of winter crops in 48 response to cropping

patterns, year and environment

(irrigated and rainfed)

4.1.1 Tillers per square meter of wheat and branches per square 48 meter of chick

pea and canola in response to cropping patterns, year and

environment (irrigated and rainfed

4.1.2 Spikelet per square meter of wheat and pods per square meter

54 of chick pea and canola in response to cropping patterns,

year and environment (irrigated and rainfed)

4.1.3 Grains per square meter of winter crops in response to 54 cropping

patterns, year and environment (irrigated and rainfed)

4.1.4 Thousand grains weight (TGW) of wheat, chick pea and 61

canola in response to cropping patterns, year and environment

(irrigated and rainfed)

4.1.5 Biological yield of winter crops in response to cropping 61


4.1.6 Grain yield of winter crops in response to cropping patterns, 66 year and

environment (irrigated and rainfed)

11

4.1.7 Harvest indices winter crops in response to cropping 66 patterns, year

environment (irrigated and rainfed)

4.2. Different maize crop parameters in maize-wheat (CP-4) and 76

maize-chick pea (CP-5) cropping patterns under both

environments during two years

4.3 Yield parameters of sorghum (fodder) under sorghum-wheat

80 cropping pattern (CP-3) for two environments in two years

4.4 Growth and yield parameters of mash bean and mung bean 85 crops under

mash bean-wheat (CP-2) and mung bean-canola (CP-6) cropping

patterns under both environments during two years

4.5 Wheat grain protein contents under different cropping 102 patterns during both

years among two environments

4.6 Different quality parameters of canola seed under different 106 cropping patterns

during two years among two environments

4.7 Different quality parameters of sorghum (fodder) under 109 different

cropping patterns during both years among two environments

4.8 Water use efficiency of summer and winter crops under 112 different

cropping patterns during two years among two environments

4.9 Partial budget of different crops under irrigated environment 118 during two

years

4.10 Partial budget of different crops under rain fed environment 118 during two

years

4.11 Net benefit and benefit cost ratio (BCR) of different 119 cropping patterns

under irrigated environment during two years

4.12 Net benefit and benefit cost ratio (BCR) of different 119 cropping patterns

under rain fed environment during two years

4.13 Dominance analysis of different cropping patterns under the 120 irrigated

environment during two years

4.14 Dominance analysis of different cropping patterns under rain 120 fed


4.15 Marginal analysis of different cropping patterns under 121

irrigated environment during two years

4.16 Marginal analysis of different cropping patterns under rain 121 fed environment

during two years

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LIST OF FIGURES

Fig. No. Page

3.1 Location Map of Pira Fatehal Dam 24

3.2 Pira Fatehal Dam picture 24

3.3 Meteorological data for the year 2009 41

3.4 Meteorological data for the year 2010 41

3.5 Meteorological data for the year 2011

41

3.6 Rainfall data from the year 1985 to 2010

42

3.7 Minimum temperature from the year 1985 to 2010

43

3.8 Maximum temperature from the year 1985 to 2010

43

4.1 N-Uptake by crops under different cropping patterns in irrigated and rain-

95 fed environments (individual and combined) during two years

4.2 P-Uptake by crops under different cropping patterns in irrigated and rain-

98 fed environments (individual and combined) during two years

4.3 K-Uptake by crops under different cropping patterns in irrigated and rain-

100 fed environments during two years

LIST OF APPENDICES

14

Appendix No. Page

01 Average prices of seeds (Rs.kg-1) of different crops and 146

fertilizers during 2009-2011, used in economic analysis.

02 Average market prices of crops’ produce (Rs.kg-1) of 147

different crops during 2009-2011, used in economic

analysis.

LIST OF ABBREVIATIONS ac acre

C0 centigrade

CIMMYT International Maize and Wheat Improvement Centre

cm centimeter

CP cropping pattern

DAP Di-Ammonium Phosphate

DM Dry Matter

GDP Gross Domestic Production

15

g gram

ha hectare

ha-1 per hectare

i.e. that is

K Potassium

Kg kilogram

Kg ha-1 Kilogram per hectare

m meter

m2 square meter

m-2 per square meter

mm millimeter

m. ha million hectare

MRR Marginal Rate of Return

NARC National Agricultural Research Centre

P Phosphorus

pH Concentration of hydrogen ions

N Nitrogen

NS Non significant

Rs. ha-1 Rupees per hectare

Rs. kg-1 Rupees per kilogram

SDO Small Dams Organization

t. ha-1 tons per hectare

% Percent

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ACKNOWLEDGEMENTS

If oceans turn into ink and all of the wood becomes pens, even then the praise of

“Allah Almighty” cannot be expressed. He, Who created the universe, knows

whatever is there in it, hidden or evident and Who bestowed upon me the intellectual

ability and wisdom to search for the secrets. I must bow my head before Allah

Almighty Who is Compassionate and Merciful and Who enabled me to complete this

work, which marks an important turning point in my life.

Countless salutations be upon the “Holy Prophet

Muhammad

(Sallallah-o-Alaihe-Wa-Aalehi-Wassalim), the city of knowledge who has guided

his “Umma” to seek knowledge from cradle to grave.

I feel great pleasure to express my heartiest gratitude to my worthy supervisor Prof.

Dr. Fayyaz-ul-Hassan, Chairman, Department of Agronomy, PMAS Arid

Agriculture University, Rawalpindi, for his guidance, assistance and sympathetic

attitude during the completion of presentation of this manuscript.

I express the greatest gratitude to Dr. Muhammad Rasheed and Dr. Rifat Hayat the

members of my supervisory committee for their cooperation in accomplishment of

this manuscript and led me to successful endings.

Special thanks to Dr. Iftikhar, from NIFA, Dr. Agha Waqar from NARC, Dr.

Ahsaanul-Haq and staff of their labs for guidance and co-operation in lab work.

I would like to express my gratitude to the staff members of the department of

Agronomy especially Mr. Waseem, for their sincere co-operations during the

completion of my studies.

17

No acknowledgments could be ever adequately expressed my humble thanks to my

friends particularly Abdul Waheed, Dr. Imran, Dr. Mahmood, Dr. Ramzan, Yasir,

Adeel, Waseem, Asim, Kk and Iftekhar.

Last but not the least, unforgettable, affectionate, everlasting, heartiest thanks and

deep appreciations are to my wife, sisters, brothers, other family members and my

kids

Jozi and Shawal who prayed day and night for my success.

(MASOOD AKHTAR) AUTHOR

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ABSTRACT

Small dams have been constructed in Pothwar region with huge investment for

supplementary irrigation. However, farmers in command area (the area around the

dam where the irrigation water reaches or that can be irrigated from a dam and is fit

for cultivation) of small dams have not benefited from this precious water and are

still doing traditional agriculture such as summer fallowing etc. Non-existence of

suitable cropping pattern may be one reason. Therefore, different cropping patterns

(i) fallow-wheat (Fallow-Triticum aestivum) (CP-1), (ii) mash bean - wheat (Vigna

mungo-Triticum aestivum) (CP-2), (iii) sorghum - wheat (Sorghum bicolor-Triticum

aestivum) (CP-3), (iv) maize (grain) - wheat (Zea mays-Triticum aestivum) (CP-4),

(v) maize (grain) - chick pea (Zea mays-Cicer arietinum) (CP-5) and (vi) mung bean

- canola (Vigna radiata -Brassica napus) (CP-6) were evaluated for agro economic

efficiencies under command area of Pira fatehal dam as well as for adjacent un-

command or rain-fed area, on sandy loam soil for two years. Highest grain yield of

wheat (winter crops) was obtained from mash beanwheat (Vigna mungo-Triticum

aestivum) (CP-2) as compared to those from maize-wheat (Zea mays-Triticum

aestivum) (CP-4), fallow-wheat (fallow-Triticum aestivum) (CP-1) and sorghum -

wheat (Sorghum bicolour-Triticum aestivum) (CP-3) cropping patterns,

respectively, under both the environments. Chick pea grain yield remained lowest

under both the environments. Regarding summer crops, sorghum fodder (Sorghum

bicolour), maize grain (Zea mays) and mash bean (Vigna mungo) performed

excellent in terms of economic and grain yield. On the other hand, mung bean (Vigna

radiata) reflected poor response for grain yield under both the environment. Benefit

cost ratio of 7.17% and

5.35% for mash bean-wheat (Vigna mungo-Triticum aestivum) (CP-2) was highest

under both the environments, while lowest benefit cost ratio (5.12 %) was exhibited

from maizechick pea (Zea mays-Cicer arietinum) (CP-5) under irrigated and (1.37%)

19

from mung bean-canola (Vigna radiata -Brassica napus) (CP-6) under rain-fed

environment, respectively. Highest net returns were obtained from maize-wheat (Zea

mays-Triticum aestivum) (CP-4) cropping pattern under irrigated while from mash

bean-wheat (Vigna mungo-Triticum aestivum) (CP-2) cropping pattern under rain-

fed environment. Mung bean-canola (Vigna radiata -Brassica napus) (CP-6) proved

the lowest in terms of net returns from both the environments. Highest marginal rate

of return was exhibited by mash bean-wheat (Vigna mungo-Triticum aestivum) (CP-

2) when compared with fallowwheat (Fallow-Triticum aestivum) (CP-1), while

maize-wheat (Zea mays-Triticum aestivum) (CP-4) and sorghum - wheat (Sorghum

bicolour-Triticum aestivum) (CP-3) ranked 2nd and 3rd on marginal rate of return

basis in the same comparison, under irrigated environment. On the other hand, mash

bean-wheat (Vigna mungo-Triticum aestivum) (CP2) ranked 1st and mung bean-

canola (Vigna radiata -Brassica napus) (CP-6) ranked 2nd when compared with

fallow-wheat (Fallow-Triticum aestivum) (CP-1) for marginal rate of return in rain-

fed environment. Water use efficiency of wheat, following mash bean under both the

environments exhibited higher values when compared with those from

sorghumwheat, fallow-wheat and maize-wheat cropping patterns. Mung bean

showed poor response among all the cropping patterns for exhibiting water use

efficiency under both environments. Cropping intensities (of 200 %) from all the

cropping patterns except fallow-wheat (100%) were recorded from both the

environments. During the course of study, 2nd year summer and winter crops received

higher rainfalls than that of first year, which affected the economic yields of crops

under rain-fed environment, where as performance of all crops remained better under

irrigated environment during both the seasons and years, as below average rainfalls

were compensated by supplementary irrigations. Hence, this study concludes that

farmers having supplemental irrigation water resources should adopt maize (grain)–

20

wheat (Zea mays-Triticum aestivum) (CP-4) cropping pattern, based on economical

return as well as efficient utilization of available supplemental water, whereas, based

on improved nutrient utilization and monetary outputs, mash bean-wheat (Vigna

mungo-Triticum aestivum) (CP-2) cropping pattern should be followed under rain-

fed areas for better resource management. Also summer fallowing practice is not

economical for farmers under both irrigated and rain-fed environments.

21

Chapter 1

INTRODUCTION

Agriculture being the mainstay of Pakistan economy is contributing 21.4%

of GDP. About 45% of country‟s workforce is employed in agriculture with 2/3 rd of

country‟s population living in rural areas is directly or indirectly linked with this

sector for their livelihood (Govt. of Pakistan, 2013). Out of 23.5 million hectare of

Pakistan‟s total culturable land, 19.62 million hectares are irrigated and 8.32 million

hectares are the culturable waste. In between Indus and Jhelum Rivers, 2.2 million

hectares constitute one contagious block locally called as “the Pothwar plateau”. The

Pothwar region is the rain-fed area comprising of civil districts of Islamabad,

Rawalpindi, Jhelum, Chakwal and Attock. It is situated in the northern part of Punjab

province of Pakistan. In this region about 1.0 million hectare is cultivated and

constitutes 90 % of rain-fed cropped area in Punjab, having great potential for

agricultural and social development. However, water availability during the driest

months of October-November (sowing time of winter crops) and May-June (sowing

time of summer crop) is one of the limiting factors for agricultural productivity in

this area along with other factors, such as small and fragmented land holdings and

obsolete method of farming (Bhutta, 1999).

The rainfall in this region is erratic, varying from 250 mm in southwest part

to more than 1000 mm in the northeast of the region (Byerlee and Hussain, 1993).

More than 60% of annual rainfall occurs in summer months i.e. in monsoon season

from July-September (Salma et al., 2012). Major crops grown in this region are

wheat, maize, millet, sorghum and ground nut, with average yields of 0.5 t ha -1

1

22

and 0.7 t ha-1 for wheat and maize, respectively. In contrast, the average irrigated

yields of these crops are 3.1 t ha-1 and 1.7 t ha-1(ADP, 2008).

In the past, the rain-fed areas were considered as least potential areas for

agriculture, hence, most of the resources were diverted for the progress of the

irrigated areas. Once, irrigated areas reached to the peak of development, further

horizontal development of agriculture (the development in agricultural resources like

increase in area, canal system etc) is being considered as unachievable task. Now, it

has been realized by policy makers and government that rain-fed areas have the great

potential if properly utilized and tamed. It is evident that 80% of country‟s

livestock‟s population, 85% of peanut, 12% wheat, 69% of sorghum, 31% of millet,

23% rapeseed/mustard, 65% of chick pea and 17% of other pulses are being

contributed by this region to the national production (Khan, 2001).

In the arid and semi-arid regions, rainfall is one of the most important factors

affecting agricultural production. The total amount of annual precipitation and its

seasonal distribution are crucial for the agricultural sustainability. The uncertain and

highly variable rainfall pattern in Pothwar area has made the crop production very

risky depending mainly upon its frequency and distribution for the whole crop

season. Besides this, the topographic feature of the area favours high run-off of rain

water through steep slopes, especially in monsoon season, thus, causing erosion of

fertile soil. The run-off is not capable to in-filtrate in the soil, so, cannot contribute

for development of underground water recharge (Sidra et al., 2010).

The region being in between two main rivers (Indus & Jhelum), all rainfall

run off drains in to these rivers through Soan, Haro, Reshi, Bunha, Kahan and Kansi

tributaries (with a total basin area of 22,300 km) flow. Average yearly runoff through

these basins is 1.88 MAF (Million Acre Feet). Only 12% (0.22 MAF) of this run-off

is tapped while rest 88% (1.66 MAF) drains down to River Indus and Jehlum (Small

23

Dams Organization, 2013). This precious water from run-off may be conserved and

utilized for agricultural purposes through building small dams where feasible.

Consequently, it can reduce the hazards caused by delayed and uncertain rains to

crop production at the time of sowing and during crop growth period. In Pothwar,

there is much capability for surface and sub-surface water resource development and

improvement through construction of small dams at feasible sites for efficient

utilization of rain water. Each millimetre of water collected, stored, conserved and

saved in these areas can produce wheat by an average of about 10 kg ha -1 (Marshall

and Holmes, 1988).

To address the scarcity of irrigation water in rain-fed areas particularly in

Pothwar regions, Small Dams Organization was established in 1973 under the

umbrella of Punjab Irrigation and Power Department, for improving agriculture

economy of the region on the motto ” Blue Revolution brings Green Revolution”.

The other objectives of constructing these small dams included drinking water

supply, ground water recharge accessible for pumping, fisheries development,

erosion control and recreation.

Up till now 55 small dams have been commissioned in the region with a

total culturable command area (CCA) of 70689 acres (Small Dams Organization,

2013). Although cropping intensities and crop yields had shown improvement along

with reduction of water table after the construction of these dams in adjoining areas

(which has made ground water accessible for pumping and dug-wells). However,

still there is potential for expanding both land use and cropping intensities, if

innovative irrigational and agronomic practices through technical support of

agricultural extension services are adopted (Ashraf et al., 2007).

The adoption of progressive irrigated agriculture in the command areas under small

dams have been limited due to many reasons such as (i) lack of fully integrated

24

approach for command area development, (ii) small and fragmented land holdings

/unconsolidated lands, (iii) failure to prepare farmers to take full advantage of new

water supply, (iv) in-adequate land preparation for irrigated crop production, (v)

absence of transitional support for farmers who are supposed to shift from centuries

old rain-fed to irrigated agriculture, (vi) non- establishment of suitable crop rotation

and cropping patterns according to the agro climatic conditions and water supply,

(vii) technical and managerial problems for maintenance and operational activities

for running of water supply through these dams, (viii) non-existence of ear-marked

water courses in the command areas, (ix) insufficient institutional support to farming

community, (x) lack of well developed agro industry and agricultural markets in

locality for sale of produce of cash crops like sugarcane, cotton, oilseeds etc.

Summer fallowing is a common and traditional practice in rain-fed areas to

conserve the moisture for next winter crops – especially wheat. Fallowing is also

practiced even in the command areas of small dams, although irrigation water is

available. During fallowing period more than six cultivations are done to control

weeds and conserve soil moisture. However, excessive cultivation at the same depth

usually develops hard pan in the soil, reduces water infiltration and resultantly

reduces the crop yields. A yield reduction of 28% in wheat under subhumid

conditions with vehicular traffic has been reported by Hassan et al. (2007).

Cereal-cereal cropping pattern is another common feature in this region as

well as in the command areas. Continuous cereal growing on the same piece of land

depletes the nutrients in the soil which is being furnished through application of

costly commercial fertilizers. Crop rotation should be economically viable and

environment friendly intervention for sustainable farming especially in rain-fed crop

production. Inclusion of leguminous crops in cropping patterns increased grain yield

and improved water use efficiency of the subsequent wheat crop (Rifat and Safdar

25

2010). Moreover, adoption of such pattern would improve soil fertility after some

time.

Legumes enhance N- Supplying power of soils, thereby, increase the organic

matter, soil recovers, stimulate soil biological activity, make the soil easier to till,

reduce soil erosion by wind and water as soil physical properties such as soil aeration

as well as soil water holding capacity is improved, thus, increase the yields of

succeeding crops in rotation. This benefit is called rotation-effect. Wheat – legume

rotation resulted in the highest yield, protein content and better yield components

(Galantini et al., 1999).

The small dams and their command areas with the assured supply of water

may become a hub for diversified agricultural activities such as production of quality

seed of different crops for the region (cereals, legumes, oilseeds, fodders,

vegetables), green fodder for consumption by livestock (as fodder availability during

dry months is almost nil) along with the provision of white meat through poultry

farms and fisheries development.

As the topography of the Pothwar region is uneven, majority of farmers in

command areas have their holdings both under command and rain-fed lands, lying

side by side. They often grow similar crops on irrigated and on adjacent rain-fed

lands with the similar production practices. Therefore, the farmers of command areas

need assistance for their capacity building in adopting more intensive cropping

patterns based on the provision of water supply from these small dams, sustainable

soil management and production of value added crops on the commanded and rain-

fed land. Moreover, farmers in the region have no history of managing irrigation. It

is further added that improved water supply through these dams may necessitate

changes to existing practices, particularly crop rotation to maximize yield gains.

Moreover, increased production intensities would also create employment

26

opportunities and allow for further employment of landless labourers (Anonymous,

2008).

Present study was proposed to be conducted under the command area (the

area around the dam where the irrigation water reaches) of Pira Fatehal Small Dam,

situated about 15 km from Talagang, District Chakwal in Pothwar region of Punjab

province of Pakistan. It is a semi-arid area receiving low average annual rainfall

(approximately 300 mm). Height of the dam is 89.94 ft with the Gross Storage

Capacity of 7400 Aft and CCA (canal command area) of 1346 acres. Its catchments

area covers 69 sq.km. Capacity of its irrigation channel is 16 Cfs and total length of

the canal is 36379 ft. Major cropping systems presently followed in the area are

wheat-fallow-wheat, wheat-millet- wheat and wheat-fallow-brassica (sarson).

Legume crops particularly pulses are seldom included in the cropping pattern.

Cropping intensity is generally 100% and farmer‟s per acre return in terms of income

is low.

Un-fortunately no cropping pattern has been established and or recommended,

so far, for the command area of the small dam, suitable to the availability of dam

water supply which can result in the maximum per acre returns. The impact of small

dams in Pothwar was evaluated by Bhutta (1999) and he concluded that small dams

could be used to provide irrigation to the crops in winter and summer seasons and

which can further help to redesign cropping patterns. Meanwhile crop yield of rain-

fed farmers could be improved by construction of dams which can help to shift

cropping patterns towards high value market oriented crops (Ashraf et al., 2007).

Since these dams might result to give good economic returns to farmers and they can

also start integrated programmers in the command areas of these dams for effective

utilization of stored water, therefore, these dams could bring revolution in the

agriculture of small land holders in rain-fed areas of Pakistan resulting in boosting

up of crop productivity and elimination of poverty. Keeping in view the

27

potential of command area of Small Dam and monetary benefits of cropping patterns,

the present study was planned with the following objectives:

1. Comparison of cropping patterns and monetary output of the cropping patterns.

2. Comparison of different cropping patterns for command area of Small Dam and

adjacent rain-fed lands in terms of water use efficiency and NPK uptake by crop

plants.

Chapter 2

REVIEW OF LITERATURE

2.1 SMALL DAMS AND THEIR IMPACT ON AGRICULTURE

Small dams have been constructed in Pothwar region with huge investment for

supplementary irrigation. Their impact on agriculture is reviewed as under:

Crop production in rain-fed areas mainly depends upon rainfall and on

average 17% of cultivated area depends upon it. The areas of Pothwar plateau, North-

eastern and the Northern Mountains considered as rain-fed areas on which dry land

farming is done in Pakistan. The rain-fed agriculture production contributes 10% to

the total agricultural production. The most important factor which contributes to the

agricultural development is availability of water. The rainfall pattern is highly erratic

and most rainfalls occurs in the monsoon

(July to September). The cultivated area of Pothwar plateau is 1.0 M ha where as the

total area of this region is 2.2 Mha. The surface runoff occurs annually about 4.2109

m. Farmers reduce the inputs to decrease in risk of loss in the event of drought just

because of uncertain situation of rainfall. Due to the shortage of water, the agriculture

28

development is at subsistence level in this area. Other factors like small land holding,

unconventional means of farming, lack of infrastructure and institutional facilities

which constitutes to meager agriculture. Migration to cities and division of land

holding are the factors those cause the less economic development (Bhutta, 1999).

There are two possible approaches which lead to the increases in agriculture

production, one is to bring more area under cultivation known as horizontal expansion

and other is to increase in yield per hectare known

8

as vertical expansion, the most important factor that affects the crop‟s yield (Ashraf

et al., 1999). Since 1961, Government of Punjab has constructed 32 small dams and

about 19 more dams are under construction in Pothwar plateau through Small Dam

Organization (SDO). The capacity of these dams to irrigate the area of 1420 hectares

but only 40% of this expected area has been developed (Iqbal and Shahid,

1992). On average about 69% of water is used for irrigation purpose (Bhutta, 1999).

Because of high surface area to volume ratio, these reservoirs subjected to high

evaporation losses and on average about 50% evaporation losses occur from the

surface of these reservoir in arid and semi arid regions (Sakthivadivel et al., 1997).

Meanwhile high evaporation also occurs to in small dams of Pothwar region i.e. 1.74

m/year evaporation occurs which is about 20% of their storage capacity. The losses

through seepage and percolation also occurs about 20% of volume of reservoir against

5% in large dams (Keller et al., 2000). The cost per unit of water in small dams is also

relatively high. The cost per 1000 m3 of water ranges from $1 to 32 (1 US$ = Rs. 60)

for large reservoirs, whereas it is from $7 to 110 for medium and small dams

(NESPAK, 1991 and Keller et al., 2000). Therefore, it is necessary to manage the

small dams because these dams act as buffer during the dry seasons and dry years as

well (Mugabe et al., 2003).

http://www.sciencedirect.com/science/article/pii/S0378377407001357#bib15









































29

Although cropping intensities and crop yields had increased and depth to

water table decreased after the construction of small dams, yet there is potential for

expending both land use and cropping intensities, if innovative agronomic and

irrigational practices are adopted by the farming community under the command area

of small dams (Ashraf et al., 2007). Similarly, Wajid et al. (2013) were of the view

that crop revenue significantly increased, yields of crops showed improvement,

while number of livestock kept by farmers increased significantly, whereas ground

water table came up in adjoining areas. However, these benefits can be further

improved through capacity building of farmers, provision of technical support to

them to utilize efficiently the dam water and provision of financial support through

credit. Furthermore, Siddiqui et al. (2011) reported that small dams help to recharge

the groundwater, control floods in hilly and plain tracts and help to develop fish

culture. They also highlighted issues like development of command area, low water

conveyance and application efficiencies on small dams.

Water resource development and management are concomitant. Without

proper management, the water resource developed can be lost without playing a

significant role in the crop production and socio-economic development of the area.

Proper management required adequate knowledge of water availability, water

requirement and productive water use (Mugabe et al., 2003). In addition to above;

Naeem et al. (2012) concluded that use of small dams‟ water may be improved on

sustainable basis through proper planning and management.

Watershed programs could be used significantly to improve cropping

intensity as by doing watershed managements. Soil moisture could be improved

which open new opportunities for diversifying farming activities in rain-fed areas

(Renfro, 2005). Meanwhile, Tarar, (1999) suggested that water use efficiency could

30

be improved by changing the water distribution practices from the existing natural co-

operation basis to weekly rotational schedule by giving share according to the size of

land holdings so that water could be made available to every farmer in the command

area according to his weekly turn. Since dams were estimated to contribute to 12-16%

of food production. Therefore, dams could be used to increase crop productivity of

rainfed agriculture (Asianics Agro-Dev. International, 2000). The use of dams for

improvement in agriculture was also highlighted by Ogbeide et al. (2003).

The uncertain and highly variable rainfall pattern in Pothwar area has made the crop

production very risky depending mainly upon its frequency and distribution for the

whole crop season. Besides this, the topographic feature of the area favors high run-

off of rain water through steep slopes, especially in monsoon season, thus, causing

erosion of fertile land. The runoff is not capable to in-filtrate in the soil, so, cannot

contribute for development of ground water reservoir and demands for water

harvesting and storage to be used as supplemental irrigation for crop production

particularly in the months of April to June and October to December” (Sidra et al.,

2010).

The chain of primary and secondary positive impacts of water storage would

radically transform the command area„s economy and pave the way for sustained

development of the dam command area. The impact of the canals planned to be taken

out of the Munda dam on the farm sector in the command area of these canals were

earlier studied and after farm house hold surveys; they concluded that the cropping

intensity would rise to 180% compared to existing 83 percent. The number of crops

in the area would increase to 12 compared to 8 at present and the yields would rise in

case of all crops resulting in at least 18-folds increase in total farm production. The

average annual total incremental farm production projected to be 414602 metric

31

tonnes (MT), cereals 20,628 MT; sugarcane 291180 MT; vegetables 82,011 MT; fruit

11,250 MT and fodder 9,533 MT (Tariq et al., 2007).

Water use is a global concern and competition for fresh water among different

sectors was growing, especially in a water-scarce scenario. The earlier results

depicted that cattle activities required more water than crops at pilot scale but at

broader ones water use patterns were determined largely by cultivation. Study

overviewed different performance across scales and suggested that complex spatial

interactions and emerging properties might arose when the analysis were scaled-up

from the pilot to the regional level (Frank et al., 2012).

2.2 CURRENT CROPPING PATTERNS OF POTHWAR

The conservation of soil and water resources is affected by the management practices

related to crop production. There is a significant role of cropping pattern in crop

management practices and types of cropping patterns including monoculture, crop

rotations, continuous cropping pattern and multiple cropping patterns.

Cropping systems are the major components of crop management. Selection of

cropping patterns depends on bio-physical and socio-economic environments and

farmer‟s decision making process. Cropping patterns under rain-fed environment are

more diverse. The existing cropping patterns are not in harmony with the rainfall

pattern. Summer fallow is practiced by 80 % of the farmers in the rain-fed area of

Pothwar region, mainly to stabilize wheat (Razzaq et al., 2002). The cropping

intensity and yield in rain-fed areas vary from year to year depending upon rainfall

and available soil water contents. Wheat yield is very low, which could be increased

with proper management of production factors. The crop yields and crop intensities

in rain-fed areas of Pothwar differ from year to year and mainly depend on rainfall

and soil water. Most crops need a specific soil type and rainfall zone, except wheat

32

which has diversity and grows well on various soils and rainfall zones. In high rainfall

areas, maize and pulses are vital crops, while, in case of low rainfall areas, groundnut,

sorghum and winter pulses (chick pea) give good returns.

There are two major land types in Pothwar, first type is lepara fields and second is

maira fields. Being close to village and receiver of FYM of village, lepara fields are

relatively rich in nutrients as compared to maira fields which are located far away

from village. In lepara lands, the cropping intensities and yields of crops is noticeably

higher in contrast with the maira lands, however, lepara lands accounts only 18 % of

cultivated lands (Farooq and Basher, 2001).

In lepara lands, wheat-maize and wheat-fallow are important cropping patters in the

areas and in maira lands, mainly two crops in two years are usually obtained. Maize

and oats have replaced sorghum and millets as food and fodder crops in the cropped

areas. Mainly two types of fallow systems are practiced in rainfed areas, firstly, one

year fallow system and secondly seasonal fallow, in response to rainfalls. Lack of soil

moisture, insufficient practices of weed control and lack of legume cropping do affect

yield out put. Due to continuous cropping, there is exhaustion of soil water and soil

fertility which resulted in lower crop production due to lower soil C and N

(Jeegadeeswari et al., 2001).

Legume growing practice can change the current cropping system from non legume

based cropping patterns to legume based cropping patterns. Throughout Pothwar,

wheat is mainly grown in all cropping patterns. Also being grown in all rainfall zones

(Byerlee et al., 1992) on about 80% of the rain-fed areas and usually fallow-wheat

cropping pattern is followed (Razzaq et al., 1991). Main cropping patterns developed

over a period of time depends on rainfall received during summer season, which

results in sowing of winter crops (Sheikh et al.,1988).

33

Getting two crops in two years is known as “Dofasla-Dosala”. One yearly and

seasonal fallow system is mainly followed in rain-fed areas. Crop rotations common

in maira lands are Wheat-millet-fallow-fallow, wheat-summer pulses-fallow-fallow,

wheat-fallow-wheat-fallow and groundnut-fallow-groundnut-fallow

Sustainability of exhaustive agriculture is a vulnerable in the North China Plain

(NCP) by use of extreme nitrogen and serious water scarcities. The possible option

of changing the conservation system (Con.W/M) of winter wheat (Triticum Aestivum

L) and summer maize with an improved double cropping system (Opt. W/M), A two

year system (winter wheat/summer maize–spring maize, W/M–M), and a single

cropping system (Spring maize, M) based on finest water and the N controlling

approaches. It directed during enduring experiment in the NCP which exhibited that

less than 70 mm of irrigation water can be preserved by Opt. W/M as associated with

Con, W/M, annual grass ground water consumed under Opt. W/M was till 250 mm

in winter wheat season 65-90% water spent. About 35 and 61% production decreased

when water irrigated in W/M-M and M related to Con.W/M correspondingly. Annual

ground water declined to190 mm in W/M-M and 94mm in M in the result. N fertilizer

rate was condensed 59% and 72% in W/M-M and M when linked with Con.W/M

correspondently. Across the 6 year duration there was no momentous difference in

the gross economic output between Con.W/M and W/M-M. There was no important

economic loss detected between Con.W/M and M during 6 years excluding in 2008-

2010 rotation. The W/M-M and M systems exhibited inordinate potential to decrease

the water and N use and attain ground water usage equilibrium, and these results

should be measured for sustainable agriculture and economic progress in the NCP

(Meng et al., 2012)

34

2.3 LEGUME BASED CROPPING PATTERNS

Long term sustainability of cropping systems should be taken into account on

the use and effective management of natural resources. Legumes are considered to be

an important component of sustainable cropping systems of the semi arid tropics

because of the ability to convert atmospheric nitrogen in the organic reserves of the

soil. The biological nitrogen fixation by legumes is obvious for sustainable

agricultural systems. A considerable part of nitrogen fixed by legumes is utilized by

succeeding cereals. Studies conducted at different sites (Mandra, Taxila and

Islamabad) on legume cereal cropping sequence by Aslam et al. (1997) showed that

the yield benefits were greatest at Mandra and least at Islamabad. Average grain and

straw yield benefits were 6.2t ha-1at Mandra and 2.5 t ha-1 at Islamabad which related

to the grain yield increases of the following wheat of 0.49 t ha-1 at Mandra and 0.03 t

ha-1 at Islamabad.

Long term management trials at Kulumsa and Asasa, south eastern Ethiopia were

conducted by Taa et al., (2004). They examined that in each of the seventeen instances

(out of 24), there was a significant cropping system effect on wheat grain yield. Faba

bean-wheat or faba bean-wheat-wheat treatments were superior to continuous wheat.

Similarly, Bagayoko et al. (2004) studied legume cereal rotation at four sites in Niger

and Burkina Faso, West Africa. They analyzed that grain and total dry matter yields

of cereals at harvest increased by legume-cereal rotations at all sites. Rotations effects

could be confound by both N utilization and fertilizer by

N efficiency. However, Rifat and Safdar (2010) conducted the studies on the role of

legumes in building soil fertility and contributing substantial amount of N as

compared to cereal rotations for sustainability of cereal based cropping patterns and

suggested that mash bean and mung bean both with and without P fertilization

35

increased the biomass and grain yield of succeeding wheat, with an increase of 18%

over non legume sorghum.

Asim et al. (2006) evaluated the potential importance of mung bean in rain-fed

cropping systems for sustainable agriculture and observed that response of wheat

growth, development and yield differed significantly when followed after mung bean

as compared to fallow. Also, legume cereal crop rotation of mung beanwheat gave

net monetary benefits of Rs.5820 per hectare.

Fallow legume manuring was beneficial in increasing yield of subsequent fodder by

12.87% to 25.75 % in rotation and provided extra cover to the soil from erosion in

addition to improve soil fertility status (Habib et al., 2011). Similarly, Felton et al.

(1998) reported the results from selected treatments of 10 experiments in Northern

New South Wales, when chickpea and wheat in one season were followed by wheat

in following seasons. Out come indicated comparatively higher dry matter and grain

yields of wheat after chickpea than after wheat. Responses to chickpea were 0.8 to

3.3 t ha-1 (shoot dry matter) and -3 to 39 kg nitrogen ha-1 (shoot N). Responses in

wheat grains yields were 0.1 to 1.5 t ha-1. Grain proteins responses were small (0 to

6%) and variable. These productivity responses could be explained largely in terms

of additional nitrate-N following chickpea.

The effect of tillage, crop rotation and N fertilizer on wheat yield in rain-fed

Mediterranean region was studied by Lopes-Bellido et al. (1996). The crop rotation

comprised of wheat-sunflower, wheat-chickpea, wheat-faba bean, wheat-fallow and

continuous wheat with N fertilizer rates of 50, 60, 100 and 150 kg N ha-1. Analysis

for average nitrate contents at a depth of 0-90 cm revealed 42 kg nitrogen ha-1 with

minimum for the wheat-fallow and 44 kg N ha-1 for wheat sunflower rotations and

maximum for continuous wheat (72kg N ha -1), where N fertilizer was applied to

36

wheat every year. The nitrate nitrogen contents for rotations including a legume

were 61 and 57 kg N ha-1 for wheat-chickpea and wheat-faba bean, respectively. The

protein contents in grains increased significantly with an increase in N fertilizer rate.

The results suggested that wheat-legume rotations increased grain yield with

minimum N –inputs.

The effect of three different crop rotations: wheat-soyabean, wheatsoyabean-vetch-

maize and wheat-soyabean-oat-soyabean-vetch-maize were studied by Sisti et al.

(2004) in a long term experiment under zero tillage and conventional tillage on the

stocks of soil organic matter in a clay oxilol of Passo. At the end of thirteen years

under continuous sequence of wheat-soyabean there was no significant improvements

in soil C-stocks between zero tillage and conventional tillage. However, rotations

where the N fixing legumes vetch was planted as a winter green manure crop, soil C

stocks under zero tillage increased by a approximately 10 Mg ha-1.

Kadambot et al. (2012) analyzed and reviewed the innovations in agronomy for food

legumes and suggested the inclusion of food legumes, and legumes generally in

cropping systems a vital step for the movement towards conservation and organic

agriculture. This would lead towards more ecological based approaches in managing

nutrition, weeds, diseases and pests.

To boost the overall yield of the cereal crops like wheat, use of additive crops

on the cultivated land is a sound management strategy and around 20% or more yield

profits are shown by literature collected from all over the world. Most of these

benefits relate to control of diseases, enhanced nitrogen application and irrigation

water. Kirkegaard et al. (2008) summarized over the recent thoughts about the well-

known crop rotation and the other mechanisms like allelopathy and soil structure

which are helpful for the producers for more than 2000 years.

37

The factors like soil erosion, albedo, soil chemical properties, carbon

restoration ability, pest invasion and the ground cover are affected by the cropping

system used. Crop amplification is achieved by the multiple cropping systems. By

plotting the 8600 household in ten sub-saharan African countries, cropping systems

were identified. Maize or groundnut cropping system was common in 35% of all

organizational units in datasheet. By using the Dynamic global vegetation model for

coped land LPJmL, scientist related six different managing situations and their

vulnerability as variation extent to climate change. Different managing strategies and

climatic conditions decreased the mean crop yield by 6-24% in sub-Saharan Africa.

Exclusion of some traditional successive cropping systems gained in yield by at least

25%. Farmer who adopted the sowing date to continuous varying climatic conditions

in the successive cropping systems, crop yield decreased. In monoculture cropping

pattern of calorific crops, yields would reach 45% to the sequential cropping system

at the end of 21st century. Cropping choice of farmers, cropping systems and the

sowing dates were suggested as the important variation strategies to continuous

climate change and these strategies might be painstaking in climate change influence

on agriculture (Waha et al., 2013).

2.4 WATER USE EFFICIENCY

Irrigating wheat after five weeks interval may enhance water use efficiency. The

water use efficiency (WUE) of 8.01 kg ha-1mm-1 was obtained when wheat crop

irrigated after five weeks interval (Khan et al., 2007). Under rain-fed conditions

seeding crops with different ratios would enhance WUE. Ansar et al.

(2013) reported 6.2%, 4.8% and 10.0% higher water use efficiency (WUE) through

25:75 seeding ratio of vetch-oat, vetch barley and vetch-wheat. Similarly, Rifat et al.

(2010) estimated the overall 29% increase in grain WUE of legumes in succeeding

year and 11-44% higher in the wheat followed by legumes as compared to that from

38

non-legume sorghum. Moreover, the P fertilization to legumes increased grain WUE

of wheat by 22% compared to that of control.

Wisal et al. (2006) evaluated three rotations: i) cereal-fallow-cereal

(farmers‟ practice) ii) cereal-legume-cereal and iii) cereal-cereal-cereal with two

tillage treatments: i) Tillage (-crop residues) and tillage (+ crop residues) and ii)

notillage (-crop residues) and no-tillage (+ crop residues). They recorded WUE of

14.88 kg ha-1 mm-1 in oat fodder under cereal-legume-cereal cropping pattern and

14.86 kg ha-1 mm under cereal–fallow-cereal (farmers‟ practice). In another

experiment, Wisal et al. (2010) evaluated the effect of tillage and crop residues

management on mung bean (vigna radiata (l.) crop yield, nitrogen fixation and water

use efficiency in rain-fed areas. The results obtained showed improvement in mung

bean yield (grain/straw) and WUE in no-tillage treatment as compared to tillage

treatment. Maximum mung bean grain yield (1224 kg ha-1) and WUE (6.61kg ha-1

mm-1) were obtained in no-tillage (+ residues) treatment. The N concentration in

mung bean straw and grain was not influenced significantly by tillage or crop residue

treatments. These results suggested that crop productivity and WUE in the rain-fed

environment may be improved with minimum tillage and crop residues retention.

However, Arif and Azim (2009) reported enhancement of water use efficiency of

wheat in fallow-wheat sequence by 8.77, 4.96 and 19.56% compared to groundnut-

wheat sequence in high, medium and low rainfall locations, respectively, whereas,

water use efficiency of sunflower and mung bean decreased in wheat-sunflower

+mung bean intercropped sequence by 271.97 and 37.47% in comparison to fallow-

sunflower+mung bean intercropped sequence in high and medium rainfall zones,

respectively. Moreover, water use efficiency of sunflower and mung bean in spring

season was 124.85 and 144.54% higher as compared to those in summer season.

39

Darwish et al. (2005) investigated soil degradation because of the combined effect

of mis-managed crop rotation, poor fertilization and irrigation policies. They

concluded that monoculture and other cultural practices caused salt accumulation in

the soil (9.0dsm-1). The deterioration of soil quality was also linked to

mismanagement in cropping patterns, fertilizer input and irrigation with low quality

waters.

2.5 CROPPING PATTERNS AND THEIR ECONOMICS

The traditional wheat-fallow-wheat (w-f-w) cropping system was evaluated with the

improved wheat-maize fodder-wheat (w-mf-w) and wheat-mung beanwheat (w-mb-

w) cropping systems at NARC, Islamabad. Two tillage practices, i.e. shallow tillage

with cultivator and deep tillage with mouldboard; and four fertilizer treatments viz.,

control (c), recommended dose of fertilizer for each crop (f), farmyard manure (fym)

@ 1.5 t ha. The recommended doses of fertilizer for individual crop with fym (f+fym)

were also included in the study to know their impact on the crops yield in the cropping

systems. Economic analysis of the data revealed that the traditional wheat-fallow-

wheat cropping system could be economically replaced with wheat-maize fodder-

wheat cropping system even under water stress condition and there will be no

economical loss of wheat yield when planted after maize fodder. The application of

recommended dose of fertilizer along with fym @ 1.5 t ha enhanced the yield of wheat

and maize fodder. In addition to this, it was concluded that the improved cropping

system of wheat-maize fodderwheat may help the farmers to sustain productivity of

these crops, stable economic benefits and improvement in soil nutrients and organic

matter over time (Khaliq, 2012).

Khan et al. (2009) examined the performance of most of the indicators i.e. yield,

gross margins, farm income, labour productivity, income distribution, cropping

intensity and crop diversity enhanced in irrigated as compared to rain-fed, while,

40

increased marginal factor productivity, irrigation productivity and rate of institutional

credit availability under irrigated area. They were of the view that rainfed area had

always been least efficient with respect to all of the quantified indicators. Past

research had not provided recommendations relevant to farmers of the area and

generally been developed without economic analysis to determine the most profitable

and least risky practices. Moreover recommendations had not considered differences

in land type, rainfall and crop rotation in the area and had provided general

recommendations to cover the entire region. In addition, the recommendations

provided were very costly for farmers to adopt, they estimated. Due to deficiencies of

research and poor extension services, many farmers had not adopted the

recommendations being provided by research and extension. Further, they concluded

that farmers in both the irrigated as well as the rain fed must shift from conventional

crops to high value crops for commercial production. Similarly, Arif and Azim (2009)

assessed the economic feasibility of proposed cropping patterns under different soil

moisture regimes of Pothwar plateau during 2003-2006.

The study revealed the highest benefit cost ratio under high rainfall zone (Rawalpindi)

in fallow-wheat (2.90) and sunflower + mung bean intercropping based cropping

patterns (2.79). Under medium (at Chakwal) and low rainfall regimes (at Attock),

fallow-wheat and groundnut based cropping patterns proved most efficient and

remunerative. They found the highest gross and net benefits for sunflower + mung

bean based cropping pattern in high rainfall zone (Rs. 54077.00 & 34738.00 ha-1,

respectively), however, these benefits were highest in groundnut (legume) based

cropping patterns in medium and low rainfall zones. They appraised that marginal

rates of returns were substantially higher for canola based cropping patterns at all the

locations (220.91-341.60%).

41

Chapter 3

MATERIALS AND METHODS

3.1 EXPERIMENTAL DESIGN AND CROPPING PATTERNS

A field study was conducted for evaluating different cropping patterns under the

command and un-command (rain-fed) area of Pira Fatehal Small Dam situated near

Talagang city, District Chakwal in Pothwar region of Punjab province, Pakistan,

during summer 2009 to winter 2010-11. Six cropping patterns (i) fallow-wheat

(Fallow-Triticum aestivum) (CP-1), (ii) mash bean - wheat (Vigna mungo-Triticum

aestivum) (CP-2), (iii) sorghum - wheat (Sorghum bicolorTriticum aestivum) (CP-

3), (iv) maize (grain) - wheat (Zea mays-Triticum aestivum) (CP-4), (v) maize

(grain) - chick pea (Zea mays-Cicer arietinum) (CP-5) and (vi) mung bean - canola

(Vigna radiata -Brassica napus) (CP-6) were arranged in a randomized complete

block design (RCBD) three factors factorial, using three replications in each

environment (irrigated and rain-fed). The net plot size was 6x4 m2. Sources of

fertilizers applied were Urea, DAP and SOP. The experiment was started in summer

42

2009 in which summer crops such as sorghum fodder, maize, mash and mung bean

were sown, while wheat, chick pea and canola were sown in winter after the harvest

of summer crops. The same sequence of crops was repeated in respective plots

during next year. These cropping patterns were grown under irrigated and rain-fed

environments. In irrigated environment, irrigation water supply from dam was

applied, while in rain-fed environment, no irrigation water was applied and crops

were grown with rainfall moisture only. Other details of

crops, inputs and cultural practices are given in table 3.1.

23

Fig 3.1 Location map of Pira Fatehal Dam

43

Fig 3.2 Pira Fatehal Dam

25

Table 3.1 Detail of crops and inputs used in the study

Crop Variety Seed rate

(kg ha-1

)

Fertilizer

applied N- P-K (kg

ha-1

)

Planting date

(year 1)

Harvesting

date

(year 1)

Planting

date

(year 2)

Harvesting

date

(year 2)

Inter

culture

Number of

Irrigations

applied

2009 2010

Mash

bean Mash bean-3 20 20-60-50 Crop

Summer crops

2

hoeing 3 2

Mung

bean NM-06 20 20-60-50

Sowing date

Harvest

date

Sowing

date

Harvest

date 2

hoeing 3 2

Maize Ageti-2002 40

120-60-50

(I)

Mung

bean 20-07-09 8/10/2009 24-07-10 16-10-10

2

hoeing 4 4

80-60-50

(R)

Mash

bean 20-07-09 8/10/2009 24-07-10 16-10-10

Sorghum JS-2002 87

120-60-50 (I)

Sorghum 20-07-09 8/10/2009 24-07-10 16-10-10

2

hoeing 5 5 80-60-50

(R)

Wheat Chakwal-50 100

120-60-50

(I) Maize 20-07-09 29-10-09 24-07-10 24-10-10 2

hoeing 5 5

80-60-50

(R)

Winter crops

Chick

pea Balkasar 75 20-60-50 Wheat 12/11/2009 8/5/2010 2/11/2010 11/5/2011

2

hoeing 4 3

Canola Pakola 5 80-60-50

Chick

pea 12/11/2009 18-04-10 2/11/2010 22-04-11 4 3

Canola 12/11/2009 18-04-10 2/11/2010 22-04-11

2

hoeing

46

Table 3.2 Physical characteristics of experimental site

Soil characteristics Before experiment After experiment

Silt (%) 32.00 32.50

Clay (%) 13.00 13.00

Sand (%) 55.00 54.50

Textural class Sandy-loam Sandy –loam

Table3.3 Chemical characteristics of soil

Soil characteristics Before experiment After experiment

Soil pH 7.34 7.36

EC (dsm-1) 0.83 0.84

Table 3.4 Chemical characteristics of dam water

Water characteristics Before experiment After Experiment

EC (dsm-1) 0.99 0.98

RSC 2.34 2.33

SAR Nil Nil

3.2 SOIL DATA:

Composite soil samples were collected prior to sowing of crops from 0-30 cm depth,

from each replication and were analyzed for soil pH, electrical conductivity, and soil

texture, while composite soil samples, for NO3-N, total organic carbon (TOC) available

47

phosphorus and extractable potassium were taken from each plot before start and at the

end of experiment.

The methodology to determine different soil physio-chemical characteristics of the

experimental area is described as under:

3.2.1 Soil pH

Buffer tablets were dissolved separately in 100 ml distilled water to prepare two buffer

solutions of PH 4 and PH 9. The PH meter was standardized with these two prepared

buffer solutions. Soil paste was prepared and taken in a beaker. After calibration, the

electrode of PH meter was inserted into the paste and PH of the given soil paste was

recorded from PH meter. (Winkleman et al., 1986)

3.2.2 Electrical Conductivity ( ECe )

From the saturated soil paste, saturated extract was obtained. After noting and adjusting

the temperature of the paste at 25oc, ECe was recorded with the help of conductivity

meter (Page et al., 1982)

3.2.3 Soil Texture

Using procedure described by Klute (1986), particle size analysis (soil texture) was

determined. Fifty grams of soil was taken in 600 ml capacity beaker, sodium hexa phosphate

solution 100 ml was taken in a beaker and mixed well after adding 300 ml of distilled water.

The sample was kept overnight. The sample used then transferred to dispersion cup and 5

minute shaking was done. The suspension was taken to 1 L (1000 ml) cylinder, shaked

thoroughly with phamger and left for 4 minutes then first hydrometer reading was noted and

second reading was noted after 4 hours. After this, by using USDA international triangle,

textural class was determined.

48

3.2.4 Moisture Contents (soil water contents).

For moisture contents‟ determination, prior to sowing and after harvest of each

crop fresh soil samples were taken from each plot, in the metallic cans and weighed.

These samples were kept in an oven at 105o C until a constant dry weight of each sample

was obtained. Then using following formula, soil moisture was recorded:

Soil moisture % = Fresh soil weight-oven dried soil weight x 100

Oven dried soil weight

3.2.5 Available Phosphorus:

For determination of available phosphorus (P) in the soil, Olsen‟s methodology was

followed. After adding 5 gm soil sample, fifty ml of 0.5 mg NaHco3 was added into a

250 ml flask and then placed at reciprocal shaker for half an hour for shaking. Then

suspension was filtered through filter paper whatman No.40. A blank sample was run.

Five ml of above filtrate was transferred to a volumetric flask having 25 ml capacity.

Then 5 ml of ascorbic acid, ammonium molybdate antimony potassium tartarate and

sulphuric acid as colour developing reagent were carefully added. By adding distilled

water, volume was made up to the mark.

Then the colour intensity was measured as percent absorbance at 880 mu using

spectrophotometer. 0, 0.5, 1.0 2.0 3.0 4.0 and 5.0 ml of 10 ppm P standard solution each

were taken into 50 ml volumetric flask for preparation of a standard curve. Ten ml each

of NaHCO3 and colour developing reagent were added to 50 ml volumetric flasks and

distilled water was added to make the volume up to the mark. These flasks were allowed

to stand for 15 minutes after shaking, and then the colour intensity was measured at 880

mu, as colour absorbance (colour intensity was measured as colour absorbance ). Then

49

phosphorus (P) concentration corresponding to the absorbance value of test solution was

noted (Page et al., 1982)

3.2.6 Extractable Potassium.

Two gram soil sample was taken into a 250 ml flask, 50 ml of ammonium

acetate of PH. 7.0) was added to the flask, kept at reciprocal shaker for 30 minutes. Then

suspension was filtered through Whatman filter paper No.40. After this, potassium was

determined by flame photo meter using potassium filter (Page et al,

1982).

3.2.7 Nitrate -Nitrogen

A 25 g soil sample and 50 ml of distilled water were added into a 250 ml bottle for

determination of No3–N following salicylic acid method. After shaking the contents for

an hour, these were filtered through whatman No.42 filter paper. Then 0.5 ml of a

sample was taken in test tube and 1 ml of 5 % salicylic acid reagent was added, mixed

well and left for thirty minutes. In each tube, 10 ml of 4 M NaOH reagent was added,

and tubes were left for an hour after mixing the contents thoroughly, for full color

development. Readings were recorded by using spectronic 20 at 410 nm

(Vendrell and Zupancic, 1990).

3.2.8 Total Organ Carbon (TOC)

In a digestion tube, 2g soil of each respective treatment, and 5 ml of potassium

dichromate (K2 Cr2 O7) were taken and for a few seconds, mixed well. Ten ml of

sulphuric acid (H2 So4), while mixing, was added to each tube and mixed for further 30

seconds. After this, these tubes were placed in preheated block digester. Tubes were

removed after 30 minutes, allowed to cool, water was then added to half to the mark,

and again mixing was done; tubes were filled to the graduated mark, after further cooling

with water, volumes were increased up to 3-4 times with thorough mixing. When the

50

suspensions were settled, the same amount was decanted into centrifuge tubes, from

each digest solution and centrifuged for 15 minutes. At the same time, standards were

prepared. Then, the sample and standard supernatant solutions were run at 610 nm

through spectrophotometer and their absorbance were measured (Heans, 1984). Soil

total organic carbon was calculated using following formula:

% C = mg C_____________ x 100

Oven dried soil weight

3.3 CROP DATA

The experiments were completed in two years. Sequence of summer and winter Table 3.5

Soil fertility status before the experiments

Cropping

pattern

Soil NO3-N (ppm) Soil Phosphorus

(ppm) Soil Potassium K

(ppm) Soil Total organic

Carbon (ppm)

Irrigated Rain-fed Irrigated Rain-fed Irrigated Rain-fed Irrigated Rain-fed

Fallow-Wheat 1.27 1.27 1.27 1.27 74.67 89.33 0.35 0.36

Mash beanWheat 1.38 1.33

1.36 1.36 66.67 85.33 0.36 0.39

Sorghum-Wheat 1.33 1.31 1.32 1.32 61.33 82.67 0.34 0.35

Maize-Wheat 1.33 1.34 1.34 1.34 64.00 74.67 0.35 0.35

Maize-Chick

pea 1.34 1.34 1.34 1.34

70.67 78.00 0.35 0.36

Mung

beanCanola 1.37 1.35 1.36 1.36

68.00 70.67 0.36 0.34

Table 3.6 Soil fertility status after the experiments

Cropping

pattern

Soil NO3-N(ppm) Soil Phosphorus

(ppm) Soil Potassium K

(ppm)

Soil Total organic Carbon (ppm)

Irrigated Rain-fed Irrigated Rain-fed Irrigated Rain-fed Irrigated Rain-fed

Fallow-Wheat 1.30 1.33 4.75 5.99 89.33 101.33 0.36 0.35

Mash beanWheat 1.44 1.44

5.77 6.01 72.00 97.33 0.38 0.39

Sorghum-Wheat 1.37 1.38 4.89 5.61 82.67 92.00 0.36 0.34

Maize-Wheat 1.37 1.41 5.27 5.78 84.00 89.33 0.37 0.35

Maize-Chick

pea 1.43 1.39 5.34 5.70

78.67 92.00 0.35 0.35

Mung

beanCanola 1.40 1.39 6.05 5.83

78.67 89.33 0.39 0.35

51

crops remained the same during both the years.

Data collected regarding different crops during the course of study is as follows:

3.3.1 Summer Crops:

Following summer crops‟ data was collected:

Maize (Grain), Sorghum (fodder), Mash bean and Mung bean.

3.3.2 Winter Crops:

Following winter crops‟ data was collected:

Wheat, Chick pea and Canola.

3.3.3 Data of Maize (Grain) i.e. Zea mays

3.3.3.1 No. of plants m-2

Number of plants m-2 at harvest from three randomly selected sites of each plot, were counted

and their average was worked out.

3.3.3.2 Number of cobs m-2

Cobs of plants from one square meter in each plot under maize crop was counted and recorded.

3.3.3.3 Number of grains m-2

Total number of grains of cobs from plants in one square meter area from each plot of maize

was counted and recorded.

3.3.3.4 Thousand grain weight (g)

For 1000- grain weight (g), three random samples of 1000 grains from cobs of each maize

plot were counted, weighed and average was worked out.

52

3.3.3.5 Grain yield (kg ha -1)

At maturity, maize plants from one square meter from each plot of maize (grain) were

separately harvested and their grain yield was recorded, which was then converted into

kg ha-1.

3.3.3.6 Biological yield (kg ha-1)

Whole above soil plants from selected area of one square meter from each respective

plot were harvested, tied in bundles, sun dried and weighed, then data was converted

into kg ha-1.

3.3.3.7 Harvest index (%)

Harvest index was calculated by the following formula:

H.I. % = Grain yield x 100

Biological yield

3.3.4 Data of Sorghum (fodder) i.e. Sorghum bicolor

3.3.4.1 Plant population (m-2)

Before flowering, number of sorghum plants per square meter was recorded from each plot

for sorghum fodder and their average was calculated.

3.3.4.2 Green fodder yield (kg ha-1)

Total plants from one square meter area were taken randomly from each respective plot.

Samples were tied and weighed then the weights were converted into kilogram per

hectare.

3.3.4.3 Fodder quality:

53

At 50 % heading of sorghum, ten plants were selected randomly from each

respective plot for determination of fodder quality in respect of nutrient detergent fibre

(NDF) as well as acid detergent fibre (ADF), using method described by Van Soest &

Robertson, (1980).

3.3.5 Data of Mash Bean (Vigna mungo), Mung Bean (Vigna radiata), Chick-Pea

(Cicer arietinum) and Canola ( Brassica napus)

3.3.5.1 Number of plants m-2 at maturity

Number of plants of each crop in an area of one square meter from each respective plot was

counted and their averages were computed.

3.3.5.2 Nodule number (for legumes)

The roots of plants from one square meter were taken out. Nodules on the roots of each

plant were counted and averaged and recorded.

3.3.5.3 Number of branches m-2

Plants from one square meter were selected from each plot for above

mentioned crops; number of branches on every plant were counted from an area of one m2

and recorded.

3.3.5.4 Number of pods m -2

From one square meter area in respective plots, number of pods of the plants was counted.

3.3.5.5 Number of seeds m -2

From one square meter of each plot under these crops, total number of seeds was counted

and recorded.

54


Three samples of 1000- grain were randomly taken from the harvested lot of each plot,

weighed then their average was calculated.

3.3.5.7 Grain yield (kg ha -1)

At maturity, plants from one square meter from each respective plot were separately

harvested, and grain yield was recorded. The yield was then converted into kilogram per

hectare.

3.3.5.8 Biological yield (kg ha -1)

For obtaining biological yield from one square meter randomly selected from each plot

were separately harvested, sun dried, tied in bundles and weighed. Then it was converted into

kilogram per hectare.


Harvest index values of each crop were calculated by the following formula:

H.I (%) = Grain yield______ x 100

Biological yield

3.3.5.10 Oil contents (%) of canola and fatty acids determination

Seed oil contents and fatty acids of canola from harvested lot of each plot were determined

by the methods of AOAC (2002).

55

3.3.6 Data of Wheat (Triticum aestivum)

3.3.6.1 Germination count m-2

Number of plants in one square meter of each plot under wheat was counted after 45 days of

sowing and recorded.

3.3.6.2 Number of tillers per unit area

One square meter area was marked randomly in each plot under wheat.

Number of tillers from the marked area was counted then the data was averaged.

3.3.6.3 Number of spikelets m-2

Spikelets m-2 from each plot of wheat were counted and recorded.

3.3.6.4 Number of grains m-2

Number of grains m-2 from respective wheat plots was counted and recorded.


Three samples of 1000- grain were randomly taken from the harvested lot of each plot,

weighed and then their average was calculated.

3.3.6.6 Grain yield (kg ha-1)

At harvest, randomly one square meter from each respective plot was

separately harvested, and grain yield was recorded. The yield was then converted into kilogram

per hectare.

56

3.3.6.7 Straw yield (kg ha-1)

For obtaining biological yield, three samples of above soil plants from one square

meter, randomly selected from each plot were separately harvested, sun dried, tied in

bundles and weighed. Then it was converted into kilogram per hectare.


Harvest index values from each harvest lot were calculated by the following formula:

H.I (%) = Grain yield_____ x 100

Biological yield

3.3.6.9 Seed protein contents (%)

Seed proteins contents from harvested lots of wheat were determined by method of

A.O.A.C. (2002).

3.4 NPK UPTAKE BY CROP PLANTS

N uptake by crop plants was determined using Kjedhal apparatus, Phosphorus was

determined by Spectrophotometer and potassium by Flame photo meter and there uptake

was collected by the following formula:

nutrient uptake (kg ha-1) = % concentration of nutrient x biomass

100

Then NPK uptake data was converted into uptake through whole cropping pattern

basis.


Water use efficiency of each crop was collected by the formula given by

Gregory (1991).

(i) WUE (for rain-fed crops) = e_____

f-g+h

57

(ii) WUE (for irrigated crops) = e_____

(f-g)+i+h

Where “e” represents the grain yield kg ha -1, “f” is soil water content

(mm) prior to sowing “g” is for soil water contents at harvest, “h” is rainfall received and

“i” is the irrigation water amount.

3.6 ECONOMIC ANALYSIS

Two years experimental data of the crops was analyzed for partial budgets, benefit cost

ratio and marginal rate of returns by the methodology described by

CIMMYT (1988).

3.6.1 Partial Budget

The costs of production of crops that vary among different cropping patterns were

recorded and yield prices of crops were used for making partial budgets of each cropping

pattern. The yield prices of the crops were calculated by using the average market prices

of the crops. The gross field benefits for each cropping pattern were calculated by the

following formula:

Gross Benefit=Field price of in put x yield

The cost that varied for each input was calculated by the following formula:

Cost of an input (that varied) = Field Price of input x Quantity of input used

The net benefits of each cropping pattern were worked out by the formula given below:

Net Benefits= Gross field benefits-Total costs that varied

3.6.2 Marginal Analysis

It involves dominance analysis of cropping patterns. In dominance analysis, cropping

patterns were arranged in order of increasing variable costs and a cropping pattern was

dominated for which the variable costs were higher but the net benefits were equal are

lower than the preceding patterns. Marginal rate of return (MRR) was worked out by

the formula described by CIMMYT (1988).

58

MRR= __ ΔNB__________ x 100

ΔTVC

Where

ΔNB= Change in net benefits

ΔTVC= Change in total variable cost

On the basis of economic analysis, the cropping patterns with the highest net benefit

was assumed that minimum marginal rate of return on investment of 100% was needed

in order to formulate the recommendations for farmers to adopt new cropping patterns.

3.6.3 Benefit-Cost Ratio (BCR):

Benefit-cost ratio for each cropping pattern was worked out as follows:

Benefit-cost ratio (BCR) = net benefit gained by cropping pattern

total expenditure of the cropping pattern

3.7 METEOROLOGICAL DATA

Average monthly rain fall (mm) and average maximum and minimum

temperatures (0C) data was obtained from nearby agriculture department.

3.8 STATISTICAL ANALYSIS

Data of the crops collected during the course of study was analyzed using Statistix 9.0. The means

were compared using LSD at 5% (Steel and Torrie 1980).

59

Fig 3.3 Meteorological data for the year 2009



0

10

20

30

40

50

60

Jul Aug Sep Oct Nov Dec

Rainfall (mm)

Mean Mini Temp °C

Mean Max. Temp °C

0

10

20

30

40

50

60

70

80

90

Rainfall (mm)

Mean Mini Temp °C

Mean Max. Temp °C

0 5

10 15 20 25 30 35 40 45

Jan Feb Mar April May

Rainfall (mm)

Mean Mini Temp °C

Mean Max. Temp °C

60

Fig 3.6 Rainfall data from the year 1985 to 2010

Fig 3.7 Minimum temperature from the year 1985 to 2010

0.00

100.00

200.00

300.00

400.00

500.00

600.00

1985 1990 1995 2000 2005 2010

10.00

12.00

14.00

16.00

18.00

20.00

1985

1990

1995

2000

2005

2010

61

Fig 3.8 Maximum temperature from the year 1985 to 2010 Chapter 4

RESULT AND DISCUSSION

4.1 WINTER CROPS

4.1.1 Germination Count per Square Meter of Winter Crops in Response to Cropping

Patterns, Years and Environments (Irrigated and Rain-fed)

The effects of cropping patterns, year, environment (Irrigated and Rain-fed) and

interactions differed significantly for all winter crops for plant population per square

meter. Cropping pattern main effect have affected significantly on the plant population

per square meter of winter crops like wheat, chick pea and canola (Table 4.1.). The

highest plant population per square meter (216.83) was recorded for wheat in Mash

bean-Wheat cropping pattern while lowest (23.00) was recorded for canola under Mung

bean-Canola cropping patterns under irrigated conditions. The difference in plant

population per square meter was 89% due to cropping patterns under irrigated

conditions. However, under rain-fed conditions results revealed that plant population

per square meter remained lower than that of irrigated system. The plant population per

20.00

25.00

30.00

35.00

40.00

1985 1990 1995 2000 2005 2010

62

square meter of wheat crop remained significantly lower (92.00) in SorghumWheat

cropping pattern under rain-fed environment while highest plant population per square

meter (127.33) was recorded in Mash bean-Wheat cropping pattern. Similarly, for chick

pea and canola, the plant population per square meter remained lowest under rain-fed

conditions compared to irrigated conditions in cropping patterns like Maize-

Chick pea and Mung bean-Canola respectively. Among environments the highest plant

44

population per square meter recorded under irrigated which remained significantly different from

rain-fed conditions.

The interactive effect of Cropping pattern x Environment x Year depicted

significant effect on plant population per square meter of all winter crops. The highest

plant population per square meter for wheat crop recorded in Mash bean-Wheat cropping

pattern during second year under irrigated environment. However, the lowest plant

population per square meter was recorded for chick pea crop in Maize-Chick pea

cropping pattern during first year under rain-fed environment.

The synergistic effect of following crops was earlier reported by Anderson

(2005a and b) who concluded that yield of wheat crop increased due to synergistic effect

of crops on crop establishment parameters like plant population per square meter which

resulted to highest yield similar to our findings. Meanwhile increased wheat yield due

to break crop system compared to continuous wheat cropping system was reported by

Kirkegaard et al. (2008) who concluded highest wheat yield under break crop system.

The increased plant population per square meter in our finding similar to Kirkegaard et

63

al. (2008) might be due to residual fertility (N and P), and greater available soil water at

planting following the break crop than following a previous wheat crop. The effect of

previous crops on water availability for next crops resulted to highest plant population

per square meter as reported by Hatfield et al. (2001). They further elaborated that crop

residues due to previous crops might resulted to differences in soil water content at

planting of crops like wheat which might result to highest plant population per square

meter. The findings of Unger and Vigil, (1998) and Gregory et al., (2005) concluded

that suitable cropping patterns could maintain soil water by increased organic matter

content, improved soil structure and water holding capacity. Since higher organic matter

content increases surface area of soil for water absorption resulting to optimum crop

stand (Gregory et al., 2005). Similarly, if crop plant population per square meter is good,

this might result to humid microenvironment because of shading effect of crop canopy

which could also minimize soil water evaporation. Improved water storage by previous

crop compared to fallow system resulted to improved water storage as reported by Joyce

et al. (2002). Therefore the system which conserved maximum soil water resulted to

maximum plant population per square meter as, in our findings maximum plant

population per square meter was recorded for Mash bean-Wheat cropping patterns

compared to FallowWheat similar to findings of earlier researcher like Joyce et al.

(2002).

4.1.2 Tillers per Square Meter of Wheat and Branches per Square Meter of Chick

Pea and Canola in Response to Cropping Patterns, Year and Environment

(Irrigated and Rain-fed)

The variation in number of tillers per square meter of winter crops between

different cropping patterns (Fallow-Wheat, Mash bean-Wheat, Sorghum-Wheat, Maize-

Wheat, Maize-Chick pea and Mung bean-Canola) was significantly different under two

64

environments (irrigated and rain-fed) during both years (Table 4.1.1). Under Mash bean-

Wheat and Maize-Wheat cropping patterns the highest number of tillers per square meter

(386) was obtained while the lowest number of tillers per square meter (150) for wheat

was recorded under Fallow-Wheat cropping pattern under rainfed environment. The

increment in wheat tillers per square meter from Fallow-Wheat to Mash bean-Wheat

and from rain-fed to irrigated environment was 61%. Similarly, chick pea numbers of

branches per square meter were higher (145.18) under irrigated condition than that of

rain-fed environment (66.45). There was 54% more number of branches under irrigated

farming than rain-fed conditions. Similar trend was observed for canola branches per

square meter in Mung bean-Canola cropping pattern. The numbers of branches per

square meter for chick pea were higher (145.18) under irrigated condition than that of

rain-fed environment (66.45). The reduction in branches per square meter among

different years during both environments was 54%. The main effect of environment on

winter crops tillers for wheat and branches for chick pea and canola per square meter

remained significantly different. The highest tillers for wheat and branches for chick pea

and canola per square meter 348.26 recorded under irrigated conditions and it remained

53 % greater than rain-fed environment (160.82). Similarly, among years the highest

winter crops tillers for wheat and branches for chick pea and canola per square meter

352.83 recorded for second year under irrigated conditions. However, under rain-fed

conditions it remained lowest (176.11) during first year. The reduction in grains per

square meter among different years during both environments was 50%.

The three way interactive effect of Cropping patterns x Environments x Years depicted

significant difference on winter crops‟ tillers for wheat and branches for chick pea and

canola per square meter. The highest wheat tillers per square meter (391.67) recorded

for Mash bean-Wheat x Irrigated x Y2 interactions followed by Maize-Wheat x

65

Irrigated x Y2 (389) interactions while lowest recorded for Fallow-Wheat x Rain-fed x Table

4.1. Germination count per square meter of winter crops in response to cropping patterns, years

and environments (irrigated and rain-fed)

CP Irrigated Rain-fed

Y1 Y2 Mean Y1 Y2 Mean

FALLOW-WHEAT 196.00b 194.00b 195.00B 87.00f 112.00d 99.00DE

MASH BEAN BEAN-WHEAT 216.00a 217.00a 217.00A 121.0cd 134.00c 127.00C SORGHUM-WHEAT 193.00b 195.00b 194.00B 90.00f 94.00ef 92.00E MAIZE-WHEAT 206.00ab 204.00ab 205.00B 107.0de 108.00de 107.00D MAIZE-CHICK PEA 12.00gh 12.00gh 12.00FG 07.00h 09.00gh 08.50G MUNG BEAN BEAN-CANOLA 23.00gh 23.00g 23.00F 13.00gh 17.gh 15.00FG

Mean 141.00A 141.00A 71.00C 79.00B

141.00A 75.00B

LSD for CP x E x Y 15.61

LSD for CP x E 11.03

LSD for E x Y 6.373

LSD for E 4.506

Any two means no sharing a common letter in a column or row differ significantly at 5% probability level

(In all the tables given in the thesis, capital letters show the differences between the years‟

means, while the small letters show the differences between treatments‟ means.)

Table 4.1.1 Tillers per square meter of wheat and branches per square meter for

chick pea and canola in response to cropping patterns, years and environments

(irrigated and rain-fed).

Mean FALLOW-WHEAT 368.00c 368.00c 368.00B 143.00gh 158.00efgh 150.00EF MASH BEAN BEAN-WHEAT 380.00c 392.00bc 386.00B 203.00def 211.00de 207.00C SORGHUM-WHEAT 350.00c 349.00c 349.33B 150.33fgh 166.00efgh 158.00DEF MAIZE-WHEAT 383.00c 389.00bc 386.00B 194.00defg 194.00defg 194.00CD MAIZE-CHICK PEA 137.00gh 153.00efgh 145.00F 55.00j 78.00ij 66.00G MUNG BEAN BEAN-CANOLA 444.00ab 466.00a 455.00A 128.00hi 250.00d 189.00CDE

344.00A 353.00A 146.00C 176.00B

Mean

LSD for CP x E x Y 58.24


LSD for E x Y 23.77

LSD for E 16.81


Irrigated Rain - fed

Mean Y1 Y2 Y1 Y2

349.00 A 161.00 B

66

Y1 interactive effect (142.67). The significant difference between highest and lowest tillers

per square meter due to three way interactive effects was 63%.

The Mash bean-Wheat system could be an alternative to Fallow-Wheat cropping

system under rain-fed conditions. However if water is available than MaizeWheat could

be a suitable options based upon tillers per square meter in our findings. The results of

our findings were at par with Chen et al. (2010) who concluded that availability of

irrigation could increase the choice of selection of crops for the rain-fed farmers. The

negative impacts of exhaustive crops like sorghum and maize was reported by earlier

researcher who concluded that crops planted before wheat had impacts on water in arid

environments as recharge might not occur due to these crops in summer and which might

affect the growth of wheat or other crops like chick pea and canola significantly

(Norwood, 2000). Therefore, they suggested to remove fallow-wheat system. It‟s better

to use such crops which are not exhaustive in nature (Miller et al., 2002). Similarly,

inclusion of legume crop like mash bean in the system might result to increased N

contents of soil which might increased yield of coming wheat crop compared to other

cropping patterns. Similar results were reported by earlier researcher about increased

yield and N due to grain legumes system compared to monoculture where cereal was

planted only (Kirkegaard et al. 2008). The 49% increased wheat crop yield in Australia

due to inclusion of legumes in cropping patterns reported by Evans et al. (2003) which

might be due to increased N in soil. However, Peoples and Craswell (1992) concluded

37% increased wheat crop yield due to use of legume in cropping patterns. Meanwhile,

Angus et al., 2001 in their findings concluded that yield might increases to 40-50% due

to inclusion of grain legumes in the cropping patterns even if N application is limited.

However, Stevenson and van Kessel (1996) reported 91 % increased wheat yield when

pea was used as legume crops in cropping system. The reduced grains per square meter

67

after sorghum might due to its excessive utilization of nutrients which resulted to poor

soil structure and availability of soil water.

4.1.3 Spikelets per Square Meter of Wheat Crop and Pods per Square Meter of Chick Pea

and Canola in Response to Cropping Patterns, Years and

Environments (Irrigated and Rain-fed)

Spikelets per square meter of wheat crop in different cropping patterns remained

significantly different from each other. The highest number (7092.2 spikelet m-2) of

spikelet per square meter of wheat crop was recoded in Mash bean-Wheat cropping

patterns under irrigated conditions which remained non significant from other cropping

patterns of wheat under irrigated conditions (Table 4.1.2). The minimum (2043 spikelet

m-2) spikelet per square meter of wheat crop was recorded in fallow-wheat cropping

patterns under rain-fed conditions. The increased trend in spikelet per square meter of

wheat crop was 71% due to its shift from fallow-wheat to Mash bean-Wheat and from

rain-fed to irrigated environment. However, for canola the maximum (9671.4) pods per

square meter were recorded in Mung bean-Canola cropping patterns under irrigated

environments while it remained minimum (4210.7 pods m-2) under rain-fed

environments. The chick pea pods per square meter remained minimum in Maize-Chick

pea cropping pattern under both environments. The main effect of environments on

pooled spikelet per square meter of wheat crop and pods per square meter of chick pea

and canola revealed that it remained maximum under irrigated environments compared

to rain-fed environment. Meanwhile, main effect of years on spikelet per square meter

of wheat crop and pods per square meter of chick pea and canola depicted significant

maximum trend during second year under irrigated environment compared to first year.

However, it remained minimum during first year under rain-fed environment.

68

The three way interactive effect of Cropping patterns x Environments x Years

depicted significant effect on spikelet per square meter of wheat crop and pods per

square meter of chick pea and canola (table). The maximum (10024) pods per square

meter were recorded for canola due to three way interactive effect of Mung beanCanola

x Irrigated x Y2 interactions. However, it remained minimum (3602 pods m-2) due to

three way interactions of Mung bean-Canola x Rainfed x Y1. The significant reduction

among two interactions due to change in environment and year on pods per square meter

was 64%. The three way interactive effect on wheat spikelets per square meter revealed

that it remained higher (7445 spikelet m-2) under Mash bean-Wheat x Irrigated x Y2

interactions while it remained lower (1851spikelet m-2) under FallowWheat x Rainfed x

Y1 interactions. The increment in spikelet per square meter due to shift of crop from

Fallow-Wheat x Rainfed x Y1 to Mash bean-Wheat x Irrigated x Y2 was 75%.

The maximum spikelet per square meter and pods per square meter after legume

crops might be due to their role in the improvement of soil structure and nutrients status

which ultimately maintained soil water in the field. As reported by

Struik and Bonciarelli, (1997) sustainable cropping patterns must have goals like maximization

of the use of beneficial natural processes in the cropping system like nitrogen fixation and

antagonistic or synergistic relationships between micro organisms and maximization of the

recycling of certain elements (nutrients, carbon, etc.). However, the cropping patterns needs to be

like that which can restricts the unfriendly agriculture and rely more on the internal resources

compared to external. Since in our studies the maximum spikelet per square meter and pods per

square meter was recorded after legume crop which might be due to synergistic effect of legume

crop on the system. Similarly, Wu, (2008) concluded increased water use efficiency and yield

due to change in cropping system from Fallow to legume base. O‟Connell et al.(2003) concluded

high recharge of ground water under fallow compared to cropped patterns however, inclusion of

69

legume in cropping pattern might increase infiltration and drainage due to macro porosity and

earthworm activity in the soil. Furthermore, Liu et al. (2009) reported sustainable cropping

patterns like wheat and grain legumes might result to similar response as we can obtained by

maintaining wheat stubble on the soil in a no tillage situation which ultimately resulted to higher

yield components of crop as reported in our findings.

4.1.4 Grains per Square Meter of Winter Crops in Response to Cropping

Patterns, Year and Environment (Irrigated and Rain-fed)

The difference in grains per square meter of winter crops between different cropping

patterns (Fallow-Wheat, Mash bean-Wheat, Sorghum-Wheat, Maize-Wheat,

MaizeChick pea and Mung bean-Canola) were significantly different under two

environments (irrigated and rain-fed) during two year (Table 4.1.3). Under Mash

beanWheat, the wheat grains per square meter obtained were 20423 g m-2 followed by

Maize-Wheat cropping pattern but both remained non significantly different from each

other under irrigated conditions. The lowest 5847 g m-2 wheat grains per square meter

recorded under Fallow-Wheat cropping pattern under rain-fed environment. The

increment in wheat grains per square meter from Fallow-Wheat to Mash bean-Wheat

and from rainfed to irrigated environment was 71%. Similarly, chick pea grains per

square meter remained non significantly different during both environments and years

in Maize-Chick pea cropping pattern. Similar trend was observed for canola grains per

square meter in Mung bean-Canola cropping pattern. The main effect of environment

on winter crops grains per square meter remained significantly different. The highest

number of grains per square meter 12759 g m-2 was recorded under irrigated conditions

and it remained 60 % greater than rain-fed environment. Similarly, among years, the

highest winter crops grains per square meter 13015 g m-2 were recorded for second year

under irrigated conditions. However, under rain-fed conditions it remained lowest

70

during first year. The reduction in grains per square meter among different years during

both environments was 64%.


depicted significant difference on winter crops grains per square meter. The highest

number of wheat grains per square meter (21115 g m-2) was recorded for Mash

beanWheat x Irrigated x Y2 interactions followed by Maize-Wheat x Irrigated x Y2

(21077 g m-2 ) interactions while lowest was recorded for Fallow-Wheat x Rain-fed x

Y1 interactive effect (5215 g m-2 ). The significant difference between highest and

lowest Table 4.1.2 Spikelet per square meter of wheat crop and pods per square meter

of

chick pea and canola in response to cropping patterns, year and environment (irrigated and rainfed)

Y1

Mean Y1 Y2

FALLOW-WHEAT 5935d 6158cd 6046.70C 1851j 2235ij 2043G MASH BEAN-WHEAT 6739bc 7445b 7092.21B 3079fgh 3438fg 3258.7E

SORGHUM-WHEAT 5771d 5845d 5808C 1958j 2341ij 2149.8G

MAIZE-WHEAT 6834bc 7110b 6972B 2506hij 2870ghi 2687.9F MAIZE-CHICK PEA 441k 483k 462H 172k 270k 220.7H

MUNG BEAN-CANOLA 9319a 10024a 9671.42A 3602f 4820e 4210.7D

5840.12 6177.41 2194.60 2662.32

B A

LSD for CP x E x

Y 725.48


LSD for E x Y 296.17

LSD for E 209.43

CP Irrigated Rainfed

Mean Y2

Mean

D C

A 6008.72 2428.53 B

71

Any two means no sharing a common letter in a column or row differ significantly at

5% probability level

Table 4.1.3 Grains per square meter of winter crops in response to cropping patterns,

year and environment (irrigated and rainfed).


Y1 Y2 Mean Y1 Y2 Mean

FALLOW-WHEAT 17574c 17800c 17687B 5215i 6479gh 5847F

MASH BEAN-WHEAT 19732b 21115a 20423A 9224de 10183d 9704D

SORGHUM-WHEAT 16731c 16855c 16793C 5822hi 6912gh 6367F

MAIZE-WHEAT 19859b 21077a 20468A 7406fg 8543ef 7975E

MAIZE-CHICK PEA 664j 761j 713G 211j 379j 295G

MUNG BEAN-CANOLA

Mean

461j 482j 471G 213j 284j 249G

12503B 13015A

12759A

4682D

5073

5463C

B

LSD for CP x E x Y 1153

LSD for CP x E 815.29 LSD

for E x Y 470.71

LSD for E 332.84

Any two means no sharing a common letter in a column or row differ significantly at 5%

probability level

grains per square meter due to three way interactive effects was 75%.

The Mash bean-Wheat system could be an alternative to Fallow-Wheat cropping

pattern under rain-fed conditions. However, if water is available than MaizeWheat could

be a suitable options based upon grains per square meter in our findings. The results of

our findings were also depicted by earlier researcher who concluded that availability of

irrigation could increase choice of selection of crops for the rain-fed farmers (Chen et

al., 2010). Similarly, inclusion of legume crop like mash bean in the system might result

to increased N contents of soil which might increases yield of coming wheat crop

compared to other cropping patterns. Similar results were reported by earlier researcher

about increased yield and N due to grain legumes system compared to monoculture

where cereal was planted only (Kirkegaard et al., 2008). The 49% increased wheat crop

yield in Australia due to inclusion of legumes in cropping patterns reported by Evans et

72

al. (2003) which was be due to increased N in soil. However, Peoples and Craswell

(1992) concluded 37% increased wheat crop yield due to use of legume in cropping

patterns. Meanwhile, Angus et al. (2001) in their findings concluded that yield might

increases to 40-50% due to inclusion of grain legumes in the cropping patterns even if

N application is limited. However, Stevenson and van Kessel (1996) reported 91%

increased wheat yield when pea was used as legume crops in cropping system. The

reduced grains per square meter after sorghum might be due to its excessive utilization

of nutrients which resulted to poor soil structure and availability of soil water. The

negative impacts of exhaustive crops like sorghum and maize was reported by earlier

researcher who concluded that crops planted before wheat have impacts on water in arid

environments as recharge might not occur due to these crops in summer and which might

affect the growth of wheat or other crops like Chick pea and Canola significantly

(Norwood, 2000). However, they suggested to remove fallow-wheat system it‟s better

to use such crops which are not exhaustive in nature (Miller et al. 2002).

4.1.5 Thousand Grains Weight (TGW) of Wheat, Chick Pea and Canola in

Response to Cropping Patterns, Year and Environments

Thousand grains weight (TGW) of wheat crop in different cropping patterns

remained significantly different from each other. The highest thousand grains weight

(39.53 g) of wheat crop was recoded in Mash bean-Wheat cropping patterns under

irrigated conditions which remained significantly higher from other cropping patterns of

wheat under irrigated conditions (Table 4.1.4). The minimum (27.06 g) thousand grains

weight for wheat crop was recorded in Sorghum-wheat cropping patterns, under rain-

fed conditions. The increased trend in thousand grains weight of wheat crop was 46%

due to its shift from exhaustive sorghum-wheat to Mash bean-Wheat and from rain-fed

to irrigated environment. However thousand grains weight for chick pea remained

73

significantly highest in Maize-chick pea cropping pattern under irrigated environment

(253.25 g) than rain-fed (188.81 g). The increased trend in thousand grains weight of

chick pea was 34% due to its shift from rain-fed to irrigated environment. Whereas, for

canola, the thousand grains weights were not statistically different from each other

during both the years and environments. The main effect of environments on pooled

thousand grains weight of wheat, chick pea and canola revealed that it remained highest

under irrigated environments compared to rain-fed environment. Meanwhile, main

effect of years on thousand grains weight of wheat, chick pea and canola depicted that

there was no significant difference during first and second years under irrigated

environment. However, it was significantly higher during second year under rain-fed

environment than the first year.


depicted significant effect on thousand grains weight of wheat, chick pea and canola

(Table 4.1.6). The highest thousand grains weight (255.02 g) was recorded for chick pea

due to three way interactive effect of Maize-Chick pea x Irrigated x Y2 interactions.

However, it remained minimum (182.14 g) due to three way interactions of Maize-Chick

pea x Rain-fed x Y1. The significant reduction among two interactions due to change in

environment and year on thousand grains weight was 40%. The three way interactive

effect on wheat thousand grains weight revealed that it remained high

(39.56 g) under Mash bean-Wheat x Irrigated x Y2 interactions while it remained low

(24.29) under Mash bean-Wheat x Rain-fed x Y1 interactions during second year. The

increment in thousand grains weight due to shift of crop from Mash bean-Wheat x Rain-

fed x Y1 to Mash bean-Wheat x Irrigated x Y2 was 63%.

Thousand grains weight is a direct measure of crop productivity. Greater the

thousand grains weight, greater will be the final yield. The highest TGW after legume

74

crops might be due to their role in the improvement of soil structure and nutrients status

which ultimately maintained soil water in the field. Sustainable cropping patterns must

have goals like maximisation of the use of beneficial natural processes in the cropping

system like nitrogen fixation and antagonistic or synergistic relationships between micro

organisms and maximisation of the recycling of certain elements (nutrients, carbon,

etc.). However, the cropping patterns needs to be like that which can restricts the

unfriendly agriculture and rely more on the internal resources compared to external (Van

Ittersum and Rabbinge, 1997). Since in our studies the maximum TGW was recorded in

the cropping pattern where legume crop was grown earlier resulted to synergistic effect

on the system. The reduction in cop yield due to exhaustive cropping system is problem

now everywhere which might result to reduced profitability for farmers and poor

utilization of resources like water and land. According to Anderson et al. (2005) the

productivity of cropping system is interaction of environment factors, genotype and

management. Therefore, emphasis needs to be given on management so that resources

can be utilized effectively, as in our findings the highest yield was recorded under

irrigated environments which was established due to storage of rainwater in small dam.

However according to Sharma et al. (2008) and Anderson (2010) majority of variation

in yield is due to environmental factors while only 10% is attributed to management.

The environmental factor impacts could be mitigated by adopting strategies like storage

of rainwater in the form of dams which could be utilized when crop is under stress as

supplemental irrigation under rain-fed environments.

4.1.6 Biological Yield of Winter Crops in Response to Cropping Patterns, Year and

Environment (Irrigated and Rain-fed)

The effects of cropping patterns, year, environment (Irrigated and Rain-fed) and

interactions differed significantly for all winter crops‟ biological yields. Cropping pattern

75

main effect have affected significantly on the biological yield of winter crops like wheat,

chick pea and canola (Table 4.1.5). The highest biological yield (17434 kg ha-1) was recorded

for canola in Mung bean-Canola cropping patterns under irrigation system followed by

wheat in Maize-wheat cropping patterns. The significantly lowest biological yield (6116 kg

ha-1) of wheat was recorded in Fallow-Wheat cropping pattern under rain-fed conditions.

The significant change in biological yield due to shift from Mash bean-Wheat cropping

patterns and from irrigated environments to Fallow-wheat and rain-fed environment was

59%. The biological yield of chick pea remained lowest among all winter crops in Maize-

Chick pea cropping patterns. Furthermore, biological yield of chick pea remained lowest

under rain-fed environments in Maize-Chick pea cropping patterns. Similarly, main effect

of environments revealed that biological yield remained highest under irrigated

environments compared to rain-fed environments. The percentage difference in biological

yield due to shift of winter crops from irrigated to rain-fed environments was 50%. The

results for biological yield of winter crops remained at par with each other during both year

under irrigated environment but it was significantly different among two years under rain-

fed environments.

The three way interactive effect (Cropping patterns x Environments x Years)

depicted significant difference on biological yield of winter crops (Table 4.1.7). The

highest (18001 kg ha-1) interactive biological yield was recorded for canola in Mung

bean-Canola Cropping patterns x Irrigated x Y2 interactions which were significantly

different from canola yield in Mung bean-Canola x Irrigated x Y1 interactions. The

wheat crop yield due to three way interactive effect remained highest (14929 kg ha-1) in

Mash bean-Wheat x Irrigated x Y2 interactions followed by Maize-Wheat x Irrigated x

Y2 interactions. However, it remained non significantly different from each other. The

76

significantly lowest biological yield of wheat was recorded in SorghumWheat x Irrigated

x Y1 interactions which remained at par with Sorghum-Wheat x

Irrigated x Y2 interactions, Fallow-Wheat x Irrigated x Y1 interactions and

FallowWheat x Irrigated x Y2 interactions. The lowest wheat yield (5425 kg ha -1) due

to three way interactive effect was recorded in Fallow-Wheat x Rain-fed x Y1

interactions. The significant increase in wheat biological yield due to shift of wheat crop

from FallowWheat x Rain-fed x Y1 interactions to Mash bean-Wheat x Irrigated x Y2

interaction was 64%. However, the lowest biological yield (1651 kg ha-1) was recorded

for chick pea crop due to three way interactive effect of Maize-Chick pea x Rain-fed x

Y1 which was at par with Maize-Chick pea x Rain-fed x Y2.

The highest biological yield of winter crops like wheat and canola after

legume might be due to fact that legume can fix atmospheric nitrogen effectively, which

ultimately improved soil nutrient status resulting to good growth of crops. Meanwhile,

earlier researcher in their findings reported that wheat yield increased significantly due

to sowing of legume crops in the cropping pattern (Gan et al., 2003). They further

concluded that increased yield might be due to residual nitrogen and soil water retained

in soil prior to sowing of crop due to previous legume crop. Similarly, Norwood (2000)

concluded in their findings that winter yield remained highest when planted after legume

crops compared to fallowing. The increase in crop yield might also be due to water

content at planting as concluded by Saseendran et al. (2010). Robertson et al.(2010) in

their findings concluded that rotations with legume crops Table 4.1.4 Thousand

grains weight (g) of wheat, chick pea and canola in response to cropping patterns, year

and environment (irrigated and rainfed)

Y1 Y2 Mean Y1 Y2

FALLOW-WHEAT 36.94ef 37.77ef 37.35DE 28.3i 30.38ghi 29.34G

CP Irrigated Rai nfed

Mean

77

MASH BEAN-WHEAT 39.3e 39.56e 39.53C 24.29j 30.9ghi 27.6GH

SORGHUM-WHEAT 36.43f 37.52ef 36.97E 25.09j 29.03hi 27.06H

MAIZE-WHEAT 39.5e 38.98ef 39.14CD 31.13gh 33.06g 32.09F

MAIZE-CHICK PEA 251.48b 255.02a 253.25A 182.14d 195.48c 188.81B

MUNG BEANCANOLA 3.17k 3.26k 3.21I 2.55k 2.78k 2.67I

Mean 67.80A 68.68A

68.24A

48.91C 53.60B

51.26B

LSD for CP x E x

Y 2.8328



LSD for E 0.8178


Table 4.1.5 Biological yield kg per ha of winter crops in response to cropping


Mean

LSD for CP x E

x Y 950.33



LSD for E 274.34


FALLOW-WHEAT

MASH BEAN-

14190cd 14149cd 14169C 5425jk 6806gh 6116G

WHEAT 14904c 14660c 14782BC 6450hi 7740g 7095F

SORGHUM-WHEAT 13479d 13321d 13400D 5639ij 6770h 6205G

MAIZE-WHEAT 14779c 14929c 14854B 5598ij 7354gh 6476FG

MAIZE-CHICK PEA

MUNG BEAN-

4602k 4892jk 4747H 1651l 2353l 2002I

CANOLA 16866b 18001a 17434A 10907f 12266e 11586E

13137A 13325A 5945C 7215B


Mean Y1 Y2 Y1 Y2

Mean 13231 A 658 0 B

78

could be an option for enhanced crop biomass and productivity with good economic

returns. Meanwhile, achieved yields under rain-fed environment are very low which

could be enhanced by adopting strategies like storage of water so that yield could be

closer to potential yield as reported by Anderson (2010).

4.1.7 Grain Yield of Winter Crops in Response to Cropping Patterns, Year and


Statistical analysis on yield data collected in response to cropping patterns, year

and environment (Irrigated and Rain-fed) showed that treatments have significant effect

on winter crops‟ grain yields (Table 4.1.6). The main effect of cropping patterns

revealed that highest (5553.9 kg ha-1) wheat grain yield was recorded in Mash bean-

Wheat cropping pattern under irrigated conditions followed by that under MaizeWheat

(5294.2 kg ha-1) and Fallow-Wheat (5233.1 kg ha-1). However, non-significant

difference was recorded for wheat grain yield under Maize-Wheat and Fallow-Wheat

cropping patterns but both differ significantly from Mash bean-Wheat cropping patterns.

The significantly lowest wheat grain yield (2017.5 kg ha-1) was recorded in Fallow-

Wheat cropping pattern under rain-fed environments. The 64% increment in wheat grain

yield was recoded due to wheat crop shift from Fallow-Wheat to Mash bean-Wheat

cropping patterns. However, grain yield of chick pea remained the minimum in Maize-

Chick pea cropping patterns but yield remained significantly maximum (1685 kg ha -1)

under irrigated environments compared to rain-fed environments. Similarly, canola

yield remained significantly highest (2368.7 kg ha-1) in Mung bean-Canola cropping

patterns under irrigated conditions compared to rainfed environment where it remained

lowest (1262.6 kg ha-1). Meanwhile, main effect of environment (Irrigated and Rain-

fed) on grain yield of winter crops revealed that it remained significantly different

among two environments. However, highest grain yield of winter crops was obtained

79

under irrigated environment (4130 kg ha -1) compared to that in rain-fed where it

remained the lowest (1685 kg ha-1). The significant increase in winter crops‟ yield due

to shift from rain-fed to irrigated environment was 59%. Furthermore, the winter crops

grain yield remained significantly different during two years under two environments.

However, it remained same during two years when considered under the same

environment only.


depicted significant effect on grain yield of winter crops (Table 4.1.6). The highest 5599

kg ha-1 grain yield was recorded for wheat due to three way interactive effect of

Mash bean-Wheat x Irrigated x Y2 interactions. However, it remained lowest (1720.7

Kg ha-1) due to three way interactions of Sorghum-Wheat x Rain-fed x Y1. The

significant reduction among two interactions due to change in environment and year on

wheat yield was 69%. The three way interactive effect on chick pea yield revealed that

it remained high (1736.6 kg ha-1) under Maize-Chick pea x Irrigated x Y2 interactions

while it remained low (473.2 kg ha -1) under Maize-Chick pea x Rain fed x Y1

interactions. The increment in chick pea grain yield due to shift of crop from Maize-

Chick pea x Irrigated x Y2 to Maize-Chick pea x Rain-fed x Y1 was 73%. The three

way interactive effect on canola yield revealed that it remained significantly high under

irrigated environment compared to the rain-fed environments.

The highest wheat yield in Mash bean-Wheat cropping patterns might be due to additive nature

of mash bean to the soil resulting to improved soil structure. This might have improved the

organic matter in the soil and increased adsorption power for water resulting to high water

holding capacity. Similar results were concluded by Tribioli and Triboi-Blondel (2014) after

long-term experiments comparing cropping system with and without legume crops. They

reported that wheat grown after legume crop resulted to 80% higher grain yield compared to

80

non legume cropping system. Therefore, system like legume based cropping patterns could be

considered as self sufficient cropping system based on biological N fixation and it can be best

alternative to conventional intensive system. However, results were contradictory to Robertson

et al. (2010) who concluded that generally legumes are considered to best for a cropping

pattern but farmers prefer continuous cereals because of less returns from legume crops.

4.1.8 Harvest Indices of Winter Crops in Response to Cropping Patterns, Year and


Harvest indices of winter crops (Wheat, Chick pea and Canola) differed

significantly in response to cropping patterns, year and environment (Irrigated and Rain-

fed). The highest harvest index (37.57%) was recorded for wheat crop in FallowWheat

cropping patterns followed by Mash bean-Wheat cropping pattern (36.91%) under

irrigated conditions (Table 4.1.7). However, the lowest harvest index for wheat crop

(30.25%) was recorded in Maize-Wheat cropping pattern under rain-fed conditions. The

increment in harvest index due to change in cropping patterns from

Maize-Wheat to Fallow-Wheat was 24%. The harvest index for other crops like chick pea and

canola remained significantly lower than wheat in Maize-Chick pea and Mung bean-Canola

cropping patterns during both the years and under both the environments. However the highest

harvest index for chick pea (34.70%) was recorded under irrigated conditions while lowest by

rain-fed (28.84%) under Maize-Chick pea cropping pattern. The increment in harvest index of

chick pea crop due to its shift from rain-fed environment to irrigated environment was 20 % in

Maize-Chick pea cropping pattern. Similarly, for canola, the highest harvest index (13.61%) was

recorded under irrigated conditions and it remained significantly lowest (10.87%) when planted

under rain-fed environment in Mung bean-Canola cropping pattern. Harvest indices remained

significantly different under both environment (Irrigated and rainfed). In the meanwhile, the

highest harvest index was recorded under irrigated conditions

81

(32.32%), while the lowest harvest index was recorded for rain-fed conditions

(27.89%). The interactive effect of Environment x Year depicted significant effect on

harvest indices of winter crops. The highest harvest index recorded under irrigated

conditions but it remained non significantly different under both year (Table 4.1.9) in

irrigated environment. However, harvest indices differed significantly under both

environments during both years.

The interactive effect of Cropping patterns x Environment x Years on harvest

index of winter crops depicted significant difference. The highest wheat harvest index

(38.19%) was recorded in Mash bean-Wheat cropping pattern during second year under

irrigated environment while lowest (34.39%) was recorded in Sorghum-Wheat cropping

pattern for wheat crop during first year under same conditions.

Table 4.1.6 Grain yield of winter crops in response to cropping patterns, year and environment

(irrigated and rain-fed)

Mean

FALLOW-WHEAT

MASH BEAN-

5175.1b 5291.2ab 5233.1B 5291.2ab 2240.3e 2017.5E

WHEAT 5508.9ab 5599a 5553.9A 5599a 2640.6d 2363.4D

SORGHUM-WHEAT 4635.5c 4663.8c 4649.7C 4663.8c 2149.7ef 1935.2EF

MAIZE-WHEAT 5256.8ab 5331.6ab 5294.2B 5331.6ab 2168e 1952.1E

MAIZE-CHICK PEA

MUNG BEAN-

1632.9hi 1736.6gh 1684.7F 1736.6gh 683.9k 578.5H

CANOLA 2373.2de 2364.1de 2368.7D 2364.1de 1351.5ij 1262.6G

Mean 4097.1A 4164.4A

1497.4C 1872.3B

4130A 1685B

LSD for CP x E

x Y 358.95



LSD for E 103.62


CP Irrigated Rain - fed

Mean Y1 Y2 Y1 Y2

82

Table 4.1.7 Harvest index of winter crops in response to cropping patterns, year and

environment (irrigated and rain-fed)

Mean

FALLOW-WHEAT 36.44bcd 37.38ab 37.57A 33.06fg 32.92fg 32.99C

MASH BEAN-WHEAT 36.95abc 38.19a 36.91A 32.33gh 34.09ef 33.21C

SORGHUM-WHEAT 34.39ef 35.07de 35.63B 30.54ijk 31.74ghi 31.14D

MAIZE-WHEAT 35.56cde 35.71cde 35.48B 31.03hij 29.48jkl 30.25D

MAIZE-CHICK PEA

MUNG BEAN-

35.47cde 35.49cde 34.70B 28.64l 29.043kl 28.84E

CANOLA 13.60F 10.87G

LSD for CP x E x

Y 1.6464



LSD for E 0.4753


probability level

However, the lowest harvest index (29.48%) for wheat crop was recorded in

Maize-Wheat cropping pattern during second year under rain-fed conditions. The

increments in harvest index due to shift of wheat crop from Maize-Wheat x Rain-fed x

Year to Mash bean-Wheat x Irrigated x year was 30 %. In case of chick pea harvest

index for interactive effect remained non-significant during both years under irrigated

and rain-fed environment. While in case of canola, highest harvest index was calculated

during first year (14.08%) followed by second year (13.13%) under irrigated

environment while lowest harvest index for canola was calculated during first year

(10.75%) followed by second year (11.00%) under rain-fed condition.

The variability in harvest indices under different cropping patterns might be due

to effective utilization of resources like water and nutrients. Since under legume based


Mean Y1 Y2 Y1 Y2

m 14.08 m 13.13 n 10.75 n 11

Mean A 32.15 32.49 A

B 27.72 28.04 B

32.32 A 27.88 B

83

cropping pattern, the availability of water and nutrients might be maximum resulting to

good establishment of crop and enhanced crop yield. The effective utilization of simple

and feasible options like development of water structures (small dams) could be better

options instead of alterations of whole cropping patterns. The other options might

include identification of the defects in the cropping patterns and suggesting remedies so

that productivity could be enhanced as concluded by Wivstad et al. (2008). However,

Robertson et al. (2010) suggested inclusion of legumes in the cropping patterns to

enhance the yield of cereal based cropping system.

Yield and yield components of crops remained higher in 2nd year than in 1st year which

was obviously due to more rainfall occurrence during 2nd year which had positive effect on

germination, growth and development of the crops, consequently resulting in higher yields.

4.2 SUMMER CROPS

4.2.1 Number of Plants per Square Meter of Maize Crop in Maize-Wheat (CP4) and

Maize-Chickpea (CP-5) under Both Environments during Two

Years

There was significant difference among both the environments and years for

maize number of plants per square meter (Table 4.2). Number of plants per square meter

varied significantly for varying environments. Maximum number of plants was

calculated for irrigated environment (10.42) while minimum number of plants (7.25)

was counted for rain-fed environment. Percentage difference for number of plants per

square meter between both the environments was 43%. Both the cropping patterns gave

same results (9.00) for number of plants per square meter. Maize crop did not differed

statistically regarding plant population per square meter during both the growing years .

While studying the interactive effects, all the cropping patterns, environments and years

were statistically non-significant. The significant difference in plant population among

84

two environments might be due to the availability of water under irrigated conditions

due to storage of water in the form of construction of small dam and then supplying that

water to the crop as supplemental irrigation. Since water resources are limited in rain-

fed farming system therefore by the storage of rainwater, efficiency of rain-fed

agricultural system could be improved. The rainfall in the rainfed system ranges from

250-550 mm annually and rainfall occurrence is highly variable, therefore it might leads

to drought resulting to reduced crop productivity (Cook et al., 2000). The dry spell

effects on crops could be mitigated by rainwater harvesting into mini dams and

providing that water as supplemental irrigation (Fox and Rockstron, 2000). However,

integration of rainwater harvesting and supplemental irrigation with new modern

techniques like drip irrigation could also boost water use efficiency by minimizing

evaporation losses (Xiao and Wang, 2003). The effect of supplemental irrigation on

maize crop was concluded that maize yield increases from 20-88% and water use

efficiency increased from 15 to 60 kg ha-1 mm-1 only because of supplemental irrigation.

4.2.2 Number of Cobs per Square Meter of Maize Crop in Maize-Wheat (CP-4)

and Maize-Chickpea (CP-5) Cropping Patterns under Both Environments

during Two Years

The productivity of maize crop is directly related to the number of cobs per plant.

In the present study number of cobs per square meter did not differ considerably for both

the cropping patterns, while, growing environments significantly differed for number of

cobs per square meter. Maximum number of cobs per square meter (11.74) was recorded

under irrigated environment, however, minimum number of cobs per square meter was

recorded (7.38) under rain-fed conditions (Table 4.2). About 36% reduction regarding

number of cobs was calculated under rain-fed condition compared to irrigated

environment. Whereas, numbers of cobs did not varied significantly during both the

85

years. On the other hand, while studying the interactive effect of cropping patterns, years

and environments, all the interactions were statistically non-significant. The difference

in the number of cobs of maize under different environments might be due to availability

of supplemental irrigation under irrigated environment due to availability of rainwater

stored into small dam. The availability of rainwater to the crops as supplemental

irrigation was earlier reported by Singandhape et al.(2003) who concluded that water

shortage during crop growth stages could be minimized by making water available to

the crops in the form of supplemental irrigation by means of rainwater harvesting.

4.2.3 Grains per Square Meter of Maize Crop in Maize-Wheat (CP-4) and Maize-

Chickpea (CP-5) Cropping Patterns under Both Environments during Two

Years

There was significant difference among both the environments and years for

maize grains per square meter (Table 4.2). Results depicted that there was no significant

difference among cropping patterns (Maize-Wheat and Maize-Chick pea) for grains per

square meter. Whereas, irrigated and rain-fed environments varied considerably for

maize grains per square meter. Maximum maize grains per square meter (3444.92) were

obtained during first year as compared to second year which produced minimum grains

per square meter (3024.40). About 14% increase in grains per square meter was

calculated during second year compared to the first year. Maximum maize grains per

square meter (4322.10) were counted under irrigated conditions while minimum maize

grains per square meter (2147.11) were obtained under rain-fed environment. About 50

% reduction in grains per square meter was calculated under rain-fed environment

compared to irrigated conditions. While studying the interactive effects, all the cropping

patterns, environments and years were statistically non-significant.

The maximum grains per square meter during second years might be due to

86

more rains occurrence during 2nd year. Furthermore, presence of legume crop in the

previous winter season might be the reason which enhanced nutrient status of the soil.

Since legumes presence in field resulted to increased N compared to other non-legume

crops. Similar findings were reported by Giller, (2001) who concluded that legume in

the system boost agricultural crop production by fixing N and conserving water

resources.

The difference in the grains per square meter under different environments (rain-

fed and irrigated) might be due to availability of water at critical growth stages of crop

as supplemental irrigation. The use of storage water to meet the crop water demands

during different growth stages of crop was reported by Xu and Mermoud, (2003) who

emphasized on the use of rainwater harvesting technologies in rain-fed region. Similar

conclusion was made by Ali and Theib, (2004) and Patrick et al. (2004) who reported

that crop water demand in rain-fed regions could be fulfilled by storing rainwater and

using it whenever it is required by the crops.

4.2.4 Thousand Grains Weight of Maize Crop in Maize-Wheat and Maize-

Chickpea Cropping Patterns under both Environments during Two Years

Maize thousand grains weight for both the cropping patterns exhibited

nonsignificant difference (Table 4.2) but there was significant difference among both

the environments (irrigated and rain-fed) and during both growing years (2009 and

2010). Results depicted that there was no significant difference among cropping patterns

(Maize-Wheat and Maize-Chick pea) on maize thousand grains weight. Whereas,

irrigated and rain-fed environments varied considerably for maize thousand grains

weight. Maximum maize thousand grains weight (194.55 g) was obtained during second

year as compared to first year which produced minimum thousand grains weight (186.76

g). About 4% increase in thousand grains was calculated during second year compared

87

to the first year. In the same way, thousand grains weight varied greatly during both

environments. Maximum maize thousand grains weight (279.64 g) was recorded under

irrigated conditions while minimum maize thousand grains weight (101.67 g) under

rain-fed environment. In the meanwhile, about 64% reduction in thousand grains weight

was calculated under rain-fed environment compared to irrigated conditions. Most of

the interactive effects were non-significant among all the cropping patterns,

environments and years, except CP x E which was highly significant at 5% P level. The

increased maize grain yield during second year might be due to the chick pea presence

in the field during previous winter season. As chick pea is crop belonging to legume

family it will boost the N fixation resulting to increased soil health. The improvement in

the health of soil might resulted to higher availability of nutrients for the maize crop

which enhances its crop productivity during second year compared to first summer crop

growing year. Since N is the main nutrient which play significant role in the productivity

of crops therefore, by introducing legume in the cropping patterns might increases crop

productivity (Ahmad, 2000). Similarly, N deficiency results to decreased crop

productivity as it is main yield limiting factors for cereal production as concluded by

Shah et al. (2003). Since cost of fertilizer is very high therefore, farmers are not willing

to invest huge amount of money, the good option could be the use of legume in the

system which could improve nutrient status (Giller, 2001). Furthermore, Dalal et al.

(1998) emphasized on the use of cereal– legume cropping systems than cereal

monocultures as this improves the mineral N in root zone resulting to maximum crop

productivity. The nitrate-sparing by the legume ultimately improves the N status in the

soil and coming crops might get maximum benefit. Similar conclusion was reported by

Evans et al.(1991) who emphasized on the use of legume crops in the cropping pattern

so that overall productivity of the cropping system might be improved. The cereal–

88

legume cropping systems have so many other benefits, like less incidence of crop

diseases due to previous crops (Bailey and Duczek,

1996), weed controls (Blackshaw et al. 1994), increased availability of P, K and S

(Bullock and Bullock, 1992), amelioration of soil structure (Aslam et al., 1997) and

production of different growth regulators due to legume crop residues. Based upon all

these benefits, it might be concluded that legume based cropping system improves

overall condition of the system which might include crop and soil.

The highest thousand grain weight under irrigated environment developed under

rain-fed cropping system by conservation of water could be best option to improve crop

yield under rain-fed agriculture. This might be possible by conserving rain water in

water reservoirs which could be used when irrigation is required by crops. Ramalan and

Nwokeocha, (2000) reported rainwater harvesting concept to utilize rainwater to boost

agriculture crop production in different part of world. furthermore, Ali and Theib, (2004)

and Patrick et al. (2004) concluded that water shortage in rain-fed areas might be

minimized by rainwater harvesting technologies like construction of mini dams.

4.2.5 Biological Yield of Maize Crop in Maize-Wheat (CP-4) and MaizeChickpea (CP-5)

Cropping Patterns in both Environments in Two Years

Biological yield of a crop is direct measure of its establishment under any

climate. In the present study biological yield did not differ considerably for both the

cropping patterns, while, growing environments significantly differed for biological

yield for maize crop. Maximum biological yield (11623 kg/ha) was recorded under

irrigated environment, whereas, minimum biological yield (6500 kg/ha) was recorded

under rain-fed conditions (Table 4.2). About 44% reduction in biological yield for maize

crop was obtained under rain-fed condition compared to irrigated environment.

Biological yield did not varied significantly during both the years. The interactive effect

of cropping patterns, years and environments on biological yield remained

89

nonsignificant. The incorporation of legumes in the cropping patterns improved

biological yield of crop as obtained in the present studies. Since legumes have the ability

to fix N2 and store that N in the soil which remained available for successive crops.

Legumes in the cropping system improve the soil fertility and conserved soil water, as

concluded by Aslam et al., 1998.

4.2.6 Grain Yield of Maize Crop in Maize-Wheat (CP-4) and Maize-Chickpea (CP-5)


Outcomes of the current study highlighted that maize crop behaved differently

under different environments and during both years for grain yield. Maize grain yield

differed significantly under different environments. Maximum grain yield (4022.83 kg

ha-1) was harvested for irrigated environment whereas minimum grain yield (1943.81 kg

ha-1) was recorded under rain-fed environment (Table 4.2). About 52% reduction was

calculated under rain-fed condition compared to irrigated environment. Maize grain

yield did not differ considerably in two cropping patterns. On the other hand, maize

grain yield varied significantly during both years. Maximum yield of the grains (3079.40

kg ha-1) was harvested during second year whereas minimum grain yield (2887.31 kg

ha-1) was obtained during first year. About 6 % increase in maize grain yield was

calculated during second year compared to first growing year. The interactive effect of

cropping patterns, years and environments, all the interactions were statistically non-

significant for grain yield. The increased maize crop yield during second year in present

findings might be due to incorporation of legume crops (chick pea) in the previous year

which added N in the soil and improved the fertility of soil. Since N is main

macronutrient essential for crop productivity (Ahmad, 2000) and its deficiency might

lead to decreased crop production (Shah et al., 2003). The availability of rainwater to

the crops as supplemental irrigation was earlier reported by Singandhape et al. (2003)

90

who concluded that water shortage during crop growth stages could be minimized by

making water available to the crops in the form of supplemental irrigation by means of

rainwater harvesting. The harvesting of rainwater with storage capacity could enable

farmers to go for supplemental irrigation under rain-fed regions as concluded by Fox

and Rockstron, (2000). This will ultimately help to mitigate dry spell effects in crop

production of rain-fed regions. Similarly, Xiao and Wang (2003) in their findings

reported that rainwater harvesting with supplemental irrigation could improve crop yield

in arid areas.

4.2.7 Harvest Index of Maize Crop in Maize-Wheat (CP-4) and Maize-Chickpea

(CP-5) Cropping Patterns under Both Environments during Two Years

Harvest index differed significantly under both the growing environments for maize

crop. However, harvesting index did not vary considerably for both the cropping

76

Table 4.2. Different maize crop parameters in maize-wheat (CP-4) and maize-chick pea (CP-5) cropping patterns under both environments during two years

MAIZE-WHEAT

Plant/m2

8.66

Cobs/m2 Grains/m2 TGW BY GY HI

9.541 3188.10 190.76 9257.91 3024.60 31.87

MAIZE-CHICK PEA 9.00 9.583 3281.20 190.55 8865.22 2942.21 32.80

Y1 8.33 8.94 3024.40B 186.76B 8803.41 2887.31B 32.35

Y2 9.33 10.18 3444.92A 194.55A 9319.73 3079.40A 32.32

Irrigated 10.41A 11.74A 4322.10A 279.64A 11623A 4022.83A 34.69A

Rainfed 7.25B 7.38B

NS

2147.11B

NS

101.67B

NS

6500B 1943.81B 29.98B

CP x Y NS NS NS NS

CP x E NS NS NS ** NS NS ***

Y x E NS NS NS NS NS NS ***

CP x Y X E NS NS NS NS * NS **

LSD for CP 1.08 1.47 391.51 7.49 681.63 173.39 1.03

LSD for Y 1.08 1.47 391.53 7.49 681.63 173.39 1.03

LSD for E 1.08 1.47 391.50 7.49 681.63 173.39 1.03

LAS for CP x Y 1.53 2.08 553.66 10.59 963.96 245.22 1.46

LSD for CP x E 1.53 2.08 553.66 10.59 963.96 245.22 1.46

LSD for E x Y 1.53 2.08 553.66 10.59 963.96 245.22 1.46

LSD for CP x E x Y 2.16 2.94 782.99 14.98 1363.3 346.79 2.07


92

patterns. Maximum harvest index (34.69%) was observed under irrigated environment,

whereas, minimum harvest index (29.99%) was recorded under rain-fed conditions

(Table 4.2). About 14% reduction in harvesting index of maize crop was calculated

under rain-fed condition compared to irrigated environment. Harvest index did not vary

significantly during both the years. On the other hand, while studying the interactive

effect of cropping patterns x environments and years x environments harvest index

varied significantly at 5% P level, whereas, for cropping patterns x environments x

years, the interactions were statistically significant at 1% P level. The difference in the

harvest index of maize under different environments might be due to availability of

supplemental irrigation under irrigated environment and due to availability of rainwater

available at the time of use by the crop. Our findings were at par with Xiao and Wang

(2003) who emphasized on the use of rainwater harvesting and supplemental irrigation

with integration of modern techniques to improve crop yield in rain-fed areas. The

option of micro-catchments as rainwater harvesting could maximize rainwater use under

rain-fed regions as concluded by Tsiouris et al. (2002).

4.2.8 Plant Population per Square Meter of Sorghum in Sorghum-Wheat Cropping

Pattern (CP-3) under Both Environments during Two Years

Plant population per square meter varied significantly for both the environments

during two years for sorghum (Table 4.3). Results depicted significant difference

between both the growing years. Maximum number of plants (18.5 plants m -2) was

recorded during second year as compared to first year which produced minimum number

93

of plants (15.8 plants m-2). About 15% increase in sorghum number of plants per square

meter was observed during second year compared to the first year.

Irrigated and rain-fed environments varied considerably for number of plants per square

meter. Maximum numbers of plants (20.2 plants m-2) were obtained from irrigated

environment as compared to rain-fed environment which produced minimum sorghum

plants (14.2 plants m-2). About 30% reduction in number of plants was exhibited in rain-

fed environment as compared to that in irrigated one. while in case of the interactions

highest sorghum number of plants (20.3 plants m-2) was calculated during second year

of irrigated environment fallowed by that in first year of irrigated environment (20 plants

m-2), whereas, lowest sorghum number of plants was calculated in first year of rain-fed

environment (11.7 plants m-2). This could be attributed to our findings that more rainfalls

occurred during 2nd summer season of the studies as compared to the rains received in

1st summer season. The increment in sorghum number of plants due to its shift from

rain-fed environment to irrigated environment was 42%. Utilization of supplemental

irrigation system under rain-fed conditions could boost agricultural crop production by

producing maximum plants in a unit area. This supplemental irrigation could be

considered as new index of rain water harvesting under rain-fed ecosystem. The

provision of supplemental irrigation to crops at critical growth stages resulted to

improved plant establishment compared to mere rain-fed environment. Similar results

were reported by Xiao et al. (2007) who concluded that supplemental irrigation could

boost crop yields. Therefore, integration of rainwater harvesting and supplemental

irrigation might play a critical role in the improvement of rain-fed agriculture by

94

increasing crop yield and water use efficiency. The findings of present studies confirmed

the objectives of maximization of utilization of rainfall by harvesting rainwater and

making available that in the form of supplemental irrigation. Pre-treated catchment and

micro-catchment could also be used to increase the runoff efficiency and maximization

of collection of rainwater (Tsiouris et al., 2002).

4.2.9 Fodder Yield of Sorghum in Sorghum-Wheat Cropping Pattern (CP-3) under

Both Environments during Two Years

There was statistically significant difference between two environments for

sorghum green fodder yield (Table 4.3). Results depicted that there was no significant

difference among two growing irrigated one. While in case of the interactions,

significant highest sorghum green fodder yield (53893 kg/ha) was obtained during

second year of irrigated environment fallowed by first year of irrigated environment

(52161 kg/ha), whereas, significant lowest green fodder yield was calculated in first year

of rain-fed environment (22109 kg/ha) followed by second year(27416 kg/ha) from rain-

fed environment. The increment in sorghum green fodder yield due to its shift from rain-

fed environment to irrigated environment in 2nd year was 59%. The difference in the

fodder yield of sorghum under different environments might be due to availability of

supplemental irrigation under irrigated environment due to availability of rainwater in

the stored form into small dam. The availability of rainwater to the crops as supplemental

irrigation was earlier reported by Singandhape et al., 2003 who concluded that water

shortage during crop growth stages could be minimized by making water available to

the crops in the form of supplemental irrigation by means of rainwater harvesting. The

95

harvesting of rainwater with storage capacity could enable farmers to go for

supplemental irrigation under rain-fed regions as concluded by Fox and Rockstron

(2000).

Table 4.3 Yield parameters of sorghum fodder under sorghum-wheat cropping pattern

(CP-3) for two environments during two years

Plants/m2

Fodder Yield

(kg ha-1)

Y1 (2009) 15.83B 37135.00

Y2 (2010) 18.51A 40654.00

E1 (Irrigated) 20.17A 53027.00A

E2 (Rain-fed) 14.17B 24762.00B

Y1*E1 20.00a 52161.00a

Y2*E1 20.33a 53893.00a

Y1*E2 11.67c 22109.00b

Y2*E2 16.67b 27416.00b

LSD for Y 1.8983 4779.00

LSD for E 1.8983 4779.00

LSD for Y x E 2.6846 6758.60



96

4.2.10 Number of Plants per Square Meter of Mash Bean and Mung Bean Crops

in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6) Cropping


Number of plants per square meter exhibited significant differences for both the

cropping patterns under both the environments (irrigated and rain-fed). Results depicted

that there was great difference among cropping patterns (Mash bean-Wheat and Mung

bean-Canola) on number of plants per square meter (Table 4.4).

Maximum number of plants per square meter (16.66) was counted under Mash

bean-Wheat cropping pattern compared to Mung bean-Canola cropping pattern which

produced minimum (14.16) plants per square meter. About 15% reduction in number of

plants per square meter was calculated under Mung bean-Canola cropping pattern

compared to Mash bean-Wheat cropping pattern. There was no significant difference

observed for number of plants per square meter during both the years. On the other hand,

irrigated and rain-fed environments varied significantly for number of plants per square

meter. Maximum number of plants per square meter (18) was obtained under irrigated

environment as compared to rain-fed environment which produced minimum number of

branches per square meter (12.83). About 30% reduction in number of plants per square

meter was calculated under rain-fed environment compared to irrigated environment.

The interactive effects regarding plant population per square meter were

nonsignificant among all the cropping patterns, environments and years. Maximum

number of plants per square meter (16.67) obtained under Mash bean-Wheat cropping

pattern compared to Mung bean-Canola cropping pattern might be due to more fixation

97

of N by Mash bean compared to Mung bean. As it is confirmed that N is main

macronutrient essential for crop productivity (Ahmad, 2000). Its deficiency might lead

to decreased crop production (Shah et al., 2003). The demand of N in cereal based

cropping system could be improved by supply of N from other sources like use of

legume crop in crop rotation (Miller, et.al., 2003).

4.2.11 Number of Branches per Square Meter of Mash Bean and Mung Bean

Crops in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6)


Number of branches per square meter exhibited significant difference (Table 4.4)

for both the cropping patterns and under both the environments (irrigated and rain-fed)

during two years. Results depicted that there was great difference among cropping

patterns (Mash bean-Wheat and Mung bean-Canola) on number of branches per square

meter. Maximum number of branches (59.87 branches m-2) was counted under Mash

bean-Wheat cropping pattern as compared to Mung bean-Canola cropping pattern which

produced minimum branches per square meter (49.03 branches m-2). About 18%

reduction in number of branches per square meter was calculated under

Mung bean-Canola cropping pattern compared to Mash bean-Wheat cropping pattern.

There was considerable difference for number of branches per square meter during both

the years. Significantly maximum number of branches per square meter (57.01 branches

m-2) was obtained during 2nd year as compared to that of 1st year which produced

minimum branches per square meter (51.90 branches m-2). During 2nd year, 9% increase

in numbers of branches was observed than in 1st year. In the same way, irrigated and

98

rain-fed environments varied significantly for number of branches per square meter.

Maximum number of branches per square meter (66.79 branches m-2) was obtained

under irrigated environment as compared to rain-fed environment which produced

minimum number of branches per square meter (42.12 branches m-2). About 36%

reduction in number of branches per square meter was calculated under rain-fed

environment compared to irrigated environment. The interactive effects were

nonsignificant among all the cropping patterns, environments and years for branches

count per square meter. Maximum number of mash bean branches per square meter

(59.87 branches m-2) under Mash bean-Wheat cropping pattern as compared to that of

mung bean under Mung bean-Canola cropping pattern might be due to improvement in

N fixation through higher nodule number in case of mash bean. Similar results were

reported by earlier researcher in which they concluded that N2 fixation could boost

agricultural productivity after studying different system of cropping patterns (Shah et

al., 2003). They evaluated mung bean (Vignaradiata)–wheat (Triticumaestivum)

sequence and a lentil (Lens culinaris) summer cereal sequence. Mung bean and sorghum

(Sorghum bicolor) or maize (Zea mays) were grown in the summers and lentil and wheat

in the winters and reported that mung bean fixed 112 kg N/ha and lentil fixed were 42–

85 kg N/ha, with a mean of 68 kg N/ha. The benefits of legumes in the cropping system

is well established as it can fix atmospheric N2 and roots left after legumes crops results

to maximum crop productivity as it is the biggest source of

N reloading in the soil N pools as concluded by Peoples and Craswell, (1992).

4.2.12 Number of Pods per Square Meter of Mash Bean and Mung Bean Crops in

Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6) Cropping

99


Number of pods per square meter exhibited significant difference for both the

cropping patterns under both the environments (irrigated and rain-fed). Results depicted

that there was significant difference among cropping patterns (Mash beanWheat and

Mung bean-Canola) on number of pods per square meter. Maximum pods per square

meter (565.72) were counted under Mash bean-Wheat cropping pattern, whereas, Mung

bean-Canola cropping pattern produced minimum (411.82) pods per square meter (Table

4.4). About 27% reduction in number of pods per square meter was calculated under

Mung bean-Canola cropping pattern compared to Mash beanWheat cropping pattern. In

the meanwhile, there was no significant considerable difference was observed for

number of pods per square meter during both the years.

On the other hand, irrigated and rain-fed environments varied significantly for

number of pods per square meter. Maximum number of pods per square meter (645.89)

were obtained under irrigated environment as compared to rain-fed environment which

produced minimum number of pods per square meter (331.66). About 49% reduction in

number of pods per square meter was observed under rain-fed environment compared to

irrigated environment. The interactive effects were nonsignificant all the cropping

patterns, environments and years except CP x E which was highly significant at 5% P

level. Maximum number of pods per square meter was obtained under Mash bean-Wheat

cropping pattern compared to Mung bean-Canola cropping pattern which produced

minimum pods per square meter because of the fact

85

Table 4.4 Growth and yield parameters of mash bean and mung bean crops in mash bean-wheat (CP-2) and mung beancanola (CP-

6) under both environments during two years

MASH BEAN-WHEAT

Plants/m2

16.66A

Branch/m2

59.87A

Pods/m2 TGW(%) Nodule/m2 BY (kg/ha) GY (kg/ha) HI (%)

565.72A 32.96A 570.71A 3126.73A 1063.12A 33.86NS

MUNG BEAN-CANOLA 14.16B 49.03B 411.82B 30.37B 468.58B 2728.32B 916.41B 33.41

Y1 15.00NS 51.90B 470.49 31.09B 545.53 2846.90B 960.20B 33.74NS

Y2 15.83 57.01A 507.06 32.22A 493.76 3008.12A 1019.34A 33.52

Irrigated 18.00A 66.79A 645.89A 34.05A 765.63A 3330.60A 1161.11A 34.83A

Rain-fed 12.83B 42.12B 331.66B 29.26B 273.67B 2524.41B 818.41B 32.44B

CP*E NS NS ** ** * NS NS NS

CP*Y NS NS NS NS NS NS NS NS

E*Y NS NS NS ** NS NS * NS

CP*E*Y NS NS NS NS NS NS NS NS

LSD for CP 1.2066 4.7722 52.829 0.8608 60.286 154.44 49.66 0.9489

LSD for Y 1.2066 4.7722 52.829 0.8608 60.286 154.44 49.66 0.9489

LSD for E 1.2066 4.7722 52.829 0.8608 60.286 154.44 49.66 0.9489

LAS for CP x Y 1.7063 6.7489 74.712 1.2174 85.258 218.41 70.23 1.342

LSD for CP x E 1.7063 6.7489 74.712 1.2174 85.258 218.41 70.23 1.342

LSD for E x Y 1.7063 6.7489 74.712 1.2174 85.258 218.41 70.23 1.342

LSD for CP x E x Y 2.4131 9.5444 105.66 1.7217 120.57 308.87 99.32 1.8978


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that mash bean efficiently fixed N through higher nodule number and used the available

resources (nutrients and soil moisture) in a better way. The key factor which is

responsible for difference in the legume based system compared to other system might

be only N (Smiley et al., 1994) as it will be available in larger quantity in the crops

following legumes compared to crops following non legumes. Similar conclusion was

made by Ranells and Wagger (1996) and Stevenson and van Kessel (1996) who depicted

that crop gets N and yield benefits from legume based cropping system more compared

to other system.

4.2.13 Thousand Grains Weight (TGW) of Mash Bean and Mung Bean Crops in

Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6) Cropping Patterns

under Both Environments during Two Years

Thousand grains weight exhibited significant differences for both the cropping patterns

under both the environments (irrigated and rain-fed) as well as during 1st and 2nd year.

Results depicted that there was statistically significant difference among cropping

patterns (Mash bean-Wheat and Mung bean-Canola) in respect of producing test

weights. Maximum thousand grains weight (32.95 g) was recorded under Mash

beanWheat cropping pattern for mash bean crop while minimum TGW (30.37 g) was

recorded from Mung bean-Canola cropping pattern for mung bean crop (Table 4.4).

About 24% reduction TGW was calculated under Mung bean-Canola cropping pattern

compared to Mash bean-Wheat cropping pattern. Maximum TGW (32.22 g) was

recorded during 2nd year, while minimum TGW (31.09 g) was calculated during 1st year.

About 4% increase in TGW recorded during second year compared to that of first year.

Irrigated and rain-fed environments varied considerably for TGW. Statistically

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Maximum TGW (34.05 g) was recorded for irrigated environment as compared to rain-

fed environment where minimum TGW (29.26 g) was calculated. About 14% reduction

in TGW was recorded under rain-fed environment compared to irrigated environment.

The interactions CP x E was significant at 5% probability level while E x Y were

significant at 10 % probability level and all the remaining interaction were statistically

non-significant.

Maximum TGW recorded under Mash bean-Wheat cropping pattern for mash

bean crop compared to mung bean crop) might be due to higher N fixation by higher

nodule number which resulted in better crop growth and development in case of mash

bean compared to mung bean. Therefore, selection of good cropping patterns could boost

agricultural crop production and it can also reduce insect, pest and disease attack,

improves soil structure and improve organic matter and prevents weeds proliferation.

Our findings are supported with those of Peoples and Crasewell (1992) as well as Ahmad

(2000)

4.2.14 Number of Nodules per Square Meter of Mash Bean and Mung Bean Crops

in Mash Bean-Wheat (CP-2) and Mung Bean-Canola (CP-6) Cropping


Number of nodules per square meter for both the years exhibited non significant

difference (Table 4.4) but there was significant difference among both the environments

(irrigated and rain-fed) and cropping patterns. Results depicted that there was great

difference among cropping patterns (Mash bean-Wheat and Mung beanCanola) on

number of nodules per square meter. Significantly maximum nodules per square meter

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(570.71) were counted from mash bean under Mash bean-Wheat cropping pattern as

compared to that from mung bean under Mung bean-Canola cropping pattern which

produced minimum nodules per square meter (468.58). About 18% reduction in number

of nodules per square meter was calculated under Mung bean-Canola cropping pattern

compared to Mash bean-Wheat cropping pattern. Whereas, no significant difference was

recorded for number of nodules per square meter during both the years. On the other

hand, irrigated and rain-fed environments varied considerably for number of nodules per

square meter. Significantly maximum number of nodules per square meter (765.63) was

obtained under irrigated environment as compared to rain-fed environment which

produced minimum number of nodules per square meter (273.67). About 64% reduction

in number of nodules per square meter was calculated under rain-fed environment

compared to irrigated environment. The interactive effects were non-significant among

all the cropping patterns, environments and years except cropping patterns x

environments which was highly significant at 5% P level.

Production of higher number of nodules might be due to efficient utilization of

available resources such as soil moisture and nutrients which resulted in better crop

growth and development in case of mash bean compared to mung bean. Thus,

selection of cropping patterns on the basis of efficient utilization of available resources

could boost agricultural crop production and it can also reduce insect, pest and disease

attack, improves soil structure and improve organic matter and prevents weeds

proliferation. Similar findings were represented by Peoples and Crasewell (1992) as

well as by Ahmad (2000).

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The findings of Hulugalle and Daniells (2005) were also in line with our findings

who concluded that integration of proper cropping patterns with supplemental irrigation

could boost agricultural crop production in water scarce area of world.

4.2.15 Biological Yield (kg/ha) of Mash Bean and Mung Bean Crops in Mash Bean-

Wheat (CP-2) and Mung Bean-Canola (CP-6) Cropping Patterns under


Biological yield revealed significant variation for both the cropping patterns

under both the environments (irrigated and rain-fed) as well as during both years. Results

depicted that there was great difference among cropping patterns (Mash beanWheat and

Mung bean-Canola) for biological yield (Table 4.4). Maximum biological yield

(3126.73 kg/ha) observed in Mash bean-Wheat cropping pattern for mash bean crop

while minimum (2728.32kg/ha) under Mung bean-Canola cropping pattern for mung

bean crop. About 13% reduction in biological yield was observed under Mung bean-

Canola cropping pattern compared to Mash bean-Wheat cropping pattern. Biological

yield was considerably varied during both the years. Maximum biological yield

(3008.12kg/ha) was recorded during 2nd year while minimum biological yield

(2846.90kg/ha) was calculated during 1st year. About 5% increase in biological yield

was calculated 1n 2nd year compared to the1st year. Irrigated and rain fed environments

varied significantly for biological yield. Maximum biological yield (3330.60 kg/ha) was

observed for irrigated environment as compared to rain-fed environment where

minimum biological yield (2524.41 kg/ha) was obtained. About 24% reduction in

biological yield was calculated under rain-fed environment compared to irrigated

environment. The interactive effects were non-significant among all the cropping

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patterns, environments and years. Maximum biological yield under Mash bean-Wheat

cropping pattern for mash bean crop compared to Mung bean-

Canola cropping pattern for mung bean crop might be due to better availability of N and

higher N fixation by Mash bean compared to Mung bean. Since nodules are the main

responsible factor which fixes nitrogen effectively therefore Mash bean crop could be

considered more effective compared to Mung bean as it had more number of nodules in

our findings. According to Giller (2001) these legumes crops could add from 20-80 kg

N ha-1 and in some cases if nodulation is higher it can go up to 150 kg N ha-1. Similarly,

legume in the cropping patterns might increases the availability of soil nitrogen and

benefits of the legumes in cropping patterns are already well established in the findings

of Peoples and Craswell (1992). The fixation of substantial amount of atmospheric N2

was already reported by Giller (2001) who concluded that fertilizer could be eliminated

from the system by the use of legume crops. The significant difference in nodules per

square meter under two environments might be due to availability of water under

irrigated conditions compared to rain-fed environment. Since, supplemental irrigation

helps to boost crop root development in the soil resulting to maximum nodule

development in the unit area.

4.2.16 Grain Yield (kg/ha) of Mash Bean and Mung Bean Crops in Mash BeanWheat

(CP-2) and Mung Bean-Canola (CP-6) Cropping Patterns under


Grain yield showed significant discrepancy for cropping patterns under both

the environments (irrigated and rain-fed) during both the years. Results illustrated that

there was great difference among cropping patterns (Mash bean-Wheat and Mung bean-

106

Canola) for grain yield (Table 4.4). Significantly maximum grain yield (1063.12 kg ha -

1) was recorded under Mash bean-Wheat cropping pattern for mash bean crop while it

was minimum (916.41 kg ha-1) under Mung bean-Canola cropping pattern for mung

bean crop. About 14% reduction in grain yield was observed under Mung beanCanola

cropping pattern compared to Mash bean-Wheat cropping pattern. Grain yield was also

noticeably varied during both the years. Maximum grain yield (1019.34 kg ha -1) was

calculated during second year, while minimum grain yield (960.20 kg ha-1) was recorded

during first year. About 6% increase in grain yield was calculated during 2 nd year

compared with that of 1st year. Irrigated and rain-fed environments varied noticeably for

grain yield. Significantly maximum grain yield (1161.11 kg ha-1) was calculated for

irrigated environment as compared to rain-fed environment which gave minimum

(818.41 kg ha-1). About 30% reduction in grain yield was calculated under rain-fed

environment compared to irrigated environment. The interactive effects were non-

significant among all the cropping patterns, environments and years except E x Y which

was significant at 10% probability level. Maximum grain yield under Mash bean-Wheat

cropping pattern for mash bean crop compared to Mung bean-Canola cropping pattern

for mung bean crop might be due to better N fixation by nodules and higher 1000 grain

weight, resulting to higher yield. The legume in the cropping patterns might increases

the availability of soil nitrogen. The fixation of substantial amount of atmospheric N2

was already reported by Giller (2001) who concluded that fertilizer could be eliminated

from the system by the use of legume crops. Since nodules are the main responsible

factor which fixes nitrogen effectively, therefore Mash bean crop could be considered

more effective compared to Mung bean as it had more plant population, more branches,

107

more pods, higher number of nodules per unit area and heavier test weight in our findings

which ultimately contributed to highest grain yield. According to Giller (2001) these

legumes crops could add from 20-80 kg N ha-1 and in some cases if nodulation is high

it can goes up to 150 kg N ha-1.

4.2.17 Harvest Indices (%) of Mash Bean and Mung Bean Crops in Mash BeanWheat

(CP-2) and Mung Bean-Canola (CP-6) Cropping Patterns under


Harvest index showed significant divergence among both the environments

(irrigated and rain-fed), whereas, results illustrated that there was non significant

difference among cropping patterns (Mash bean-Wheat and Mung bean-Canola) and

during both the years for harvesting indices (Table 4.4). Significantly maximum harvest

index (34.83%) was recorded for irrigated environment as compared to rainfed

environment which gave minimum harvest index (32.44%). About 7% reduction in

harvest index was calculated under rain-fed environment compared to irrigated

environment. The interactive effects were non-significant among all the cropping

patterns, environments and years. The difference in the harvest index of Mash bean and

Mung bean under different environments might be due to availability of supplemental

irrigation under irrigated environment due to availability of rainwater supplied by small

dam. The availability of rainwater to the crops as supplemental irrigation was earlier

reported by Singandhape et al. (2003) who concluded that water shortage during crop

growth stages could be minimized by making water available to the crops in the form of

supplemental irrigation by means of rainwater harvesting. Based upon our findings it

could be concluded that rainwater harvesting could enables farmers to go for

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supplemental irrigation and improves the crop status under rain-fed system. Meanwhile,

rainwater harvesting makes secure access to water issues, build culture of natural

conservation and solve problems of ecological poverty and decentralized water

conservation and management system to ensure local food security. Our findings were

at par with Xiao and Wang (2003) who emphasized on the use of rainwater harvesting

and supplemental irrigation with integration of modern techniques to improve crop yield

in rain-fed areas. The option of micro-catchment as rainwater harvesting could maximize

rainwater use under rain-fed regions as concluded by Tsiouris et al. (2002).

4.3 NUTRIENT UPTAKE

4.3.1 N-Uptake by Crops under Different Cropping Patterns in Irrigated and Rain-

fed Environments during Two Years

Nitrogen is a key component for plant growth and development. Nitrogen uptake

varied significantly under both the environments (irrigated and rainfed) in all the

cropping patterns. Under irrigated environment maximum nitrogen uptake observed for

Sorghum-Wheat cropping pattern (pooled over years) while minimum nitrogen uptake

observed under Fallow-Wheat cropping pattern (Fig. 4.1) (bars given in figures are

default to show difference among treatments for nutrient uptake). On the other hand

under rain-fed environment less amount of nitrogen was up-taken compared to irrigated

environment (pooled over years). However, under rainfed conditions highest nitrogen

was taken up in CP-4 fallowed by CP-3 while minimum nitrogen was taken up by

Fallow-Wheat cropping pattern. In both the cases (irrigated and rain-fed) under Mash

bean-Wheat and Mung bean-Canola cropping patterns nitrogen uptake was relatively

lower than other cropping patterns. This reduction in nitrogen uptake was due to

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leguminous nature of crops in the cropping patterns. When pooled with years (2009-10

and 2010-11) and both the environments (irrigated and rain-fed) maximum amount of

nitrogen was taken up by Sorghum-Wheat cropping pattern while minimum nitrogen

was taken up by Fallow-wheat cropping pattern. Under Mash beanWheat and Mung

bean-Canola cropping patterns, amount of nitrogen uptake was higher than Fallow-

Wheat cropping pattern while lower than other cropping patterns. In case of nutrients

the resources capture efficiency can be measured by nutrient uptake, it was recorded

maximum in Sorghum-Wheat cropping pattern because of exhaustive nature of

sorghum. The highest nutrient uptake could also be because of greater root development

and more availability of nutrients.Our results were in line with Stevenson and van Kessel

(1996) who reported that N uptake could be due to improvement in the physical and

chemical conditions of soil. Our results related to the effect of environments reported

that N uptake remained maximum in irrigated environment compared to rain-fed

environment which might be due to efficient uptake of nutrient with water because of

availability of water. The efficient water saving supplemental irrigation could be

considered as new index of water saving irrigation under rain-fed farming system and

management of nutrients. The provision of supplemental irrigation to crops at critical

growth stages results to improved plant root establishment compared to rain-fed

environment because of efficient uptake of nutrients. Similar results were reported by

Xiao et al. (2007) who concluded that the

110

a. Horizontal bars show value of nitrogen uptake.

b. Vertical bars depict range of nitrogen uptake between minimum and

maximum values.

c. Cropping patterns (CP-1 to CP-6) are given on x-axis while nutrient uptake

on y-axis.

Fig 4.1 N-uptake by crops under different cropping patterns in irrigated and rain-fed

environments (individual and combined) during two years (individual and

combined)

Irrigate d Rainfed

Combined

111

supplemental irrigation could boost efficiency of supplied irrigation. Therefore,

integration of rainwater harvesting and supplemental irrigation might play a critical role

in the improvement of rain-fed agriculture by increasing crop yield, N uptake and water

use efficiency. Water is limited resource, therefore, its management is very important

for having maximum agricultural growth, minimum nutrient loss and crop production.

Since scarcity of water and drought are main constraints in rain-fed regions of world

which affect agricultural crop production significantly (Cook et al., 2000) therefore it

needs to be managed by storing water and building structures like mini-dams and small

dams.

4.3.2 P-Uptake by Crops under Different Cropping Patterns in Irrigated and

Rain-fed Environments during Two Years

Phosphorus uptake under all the cropping patterns, for both environments during two

years depicted variable trend. Under irrigated environment lowest phosphorus was taken

up by Mung bean-Canola cropping pattern (pooled over years) while maximum

phosphorus was taken up by CP-4 (Maize-Wheat cropping pattern). In case of rain-fed

environment minimum phosphorus uptake was observed under

Mung bean-Canola cropping pattern whereas highest phosphorus was taken up by

Sorghum-wheat cropping pattern (Fig. 4.2). When pooled both for years and

environments highest phosphorus was taken up by Sorghum-Wheat cropping pattern

and lowest phosphorus was taken up by Fallow-wheat cropping pattern fallowed by

Mung bean-Canola cropping pattern. Since Phosphorus is available to plants poorly

because of its fixation with Ca, Fe and Al. Similarly, it also moves slowly to the root

surface by diffusion. If P is supplied in low quantity, it might lead to enhanced uptake

112

from soil or plant itself has the ability to maintain P homeostasis in meristematic tissues.

The mechanism shown by plants to increase uptake of P includes changes in root

architecture, symbiotic relationships with mycorrhizal fungi, secretion of organic acids,

interactions with microorganisms, protons, and phosphatases to increase the availability

of phosphate in the rhizosphere. However, our results were in contradictory to earlier

researcher who reported more uptake of P in legume based cropping patterns. Overall

the production of grain legumes increased significantly which might be due to their

effective utilization in food, feed and industrial demands. Similarly grain legumes also

enhanced the N and P uptake in cropping patterns as concluded by Sinclair and Vadez

(2012). Since P is essential element like N and required for energy transfer within cells.

Therefore, use of legumes in the cropping patterns might result to maximum P uptake

as there are some legume species which have mechanism to convert unavailable P to

available form which result to maximum uptake of P (Ae et al., 1990). Our results further

elaborated that P uptake remained lowest under rain-fed environment compared to

irrigated, might be due to

unavailability of water under rain-fed environment.

4.3.3 K-Uptake by Crops under Different Cropping Patterns in Irrigated and

Rain-fed Environments during Two Years

Potassium is needed by the plants for different biochemical processes. In our

findings, when pooled for years under irrigated conditions more potassium was taken up

by the crops than in rain-fed environment. Under irrigated environment maximum

potassium was taken up by Maize-Wheat cropping pattern (CP- 4), while minimum

113

a Horizontal bars show value of phosphorus uptake.

b. Vertical bars represent range of phosphorus uptake between minimum

and maximum values.

c. Cropping patterns (CP-1 to CP-6) are given on x-axis while nutrient uptake

on y-axis.

Fig. 4.2 P-uptake by crops under different cropping patterns in irrigated and rain-fed

environments (individual and combined) during two years

Irrigated Rainf ed

Combined

114

potassium was taken up Mung bean-Canola cropping pattern (CP-6).

Almost same trend was also observed of potassium uptake under rain-fed environment

(Fig. 4.3). Highest potassium was taken up by Maize-Wheat, while lowest potassium

uptake was observed for Mung bean-Canola fallowed by Fallow-Wheat cropping

patterns. When pooled for year and environment highest potassium was taken up by

Maize-Wheat, whereas, minimum potassium uptake was observed under Mung

beanCanola cropping pattern fallowed by Fallow-Wheat cropping pattern. K uptake by

the plants depends upon number of factors which includes plant factors, soil moisture

and management of fertilizer. In case of plants main factor is root development and its

proliferation. Similarly, moisture is required to uptake K as mass flow, firstly its

movement to the roots by water and then entry in the root through diffusion mechanism.

The availability of K becomes less in case of water stress and excessive moisture.

According to St. Clair and Lynch (2010), potassium availability by the process of

diffusion was significantly affected by rainfall and temperature. Similarly, according to

Peck and McDonald (2010) increased concentration of nutrients like K in plants might

result to increase resistance against biotic and abiotic stresses which could be resulted

due to climate change.

Since K uptake was significantly affected due to cropping patterns therefore, it‟s

essential to use such cropping patterns which are not too much exhaustive. Intensive

cropping system might result to imbalance mining of nutrients which might result to

deficiency of essential elements in the soil.

115

a. Horizontal bars show value of potassium uptake.

b. Vertical bars mean show range of potassium uptake between minimum

and maximum values.

c. Cropping patterns (CP-1 to CP-6) are given on x-axis while nutrient

uptake on y-axis.

Fig 4.3 K-uptake by crops under different cropping patterns in irrigated and rain-fed

environments (individual and combined) during two years

Irrigated Rain ed

Combin ed

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4.4 QUALITY PARAMETERS

4.4.1 Wheat Grain Protein Contents under Different Cropping Patterns in Irrigated

and Rain-fed Environments during Two Years

Statistical analysis on grain protein data recorded in response to cropping

patterns, year and environment (Irrigated and Rain-fed) showed that cropping patterns

have not any significant effect on wheat grain protein (Table 4.5) while environments

differed potentially for wheat grain protein. The main effect of cropping patterns

revealed that there was no significant difference for wheat grain protein. In the same

way, non-significant difference recorded for grain protein during both growing years.

While both the environments varied significantly for wheat grain protein. The

significantly minimum wheat grain protein (8.28 %) was recorded in irrigated

environment while maximum grain protein (13.12 %) was recorded from rain-fed

environment. The 37% increment in wheat grain protein was observed due to wheat crop

shift from irrigated to rain-fed environment under different cropping patterns. The

interactive effect of Cropping patterns x Environment x Years on grain protein contents

of wheat crop have depicted non-significant differences. The difference in grain protein

due to environment was related due to stress during grain maturation. Shrivelled grains

accumulate higher protein, as eleuron layer increases in shrivelled wheat grains which

is largely protein.

Since concentration of N might be high in legume based cropping patterns therefore protein

concentration needs to be high under legume based systems. N is the Table 4.5 Wheat grain

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protein contents under different cropping patterns in irrigated and rain-fed environments

during two years

Cropping patterns Grain Protein (%)

Fallow-Wheat 10.62NS

Mash bean-Wheat 10.94

Sorghum-Wheat 10.62

Maize-Wheat 10.61

Y1 10.43NS

Y2 10.96

Irrigated 8.28B

Rain-fed 13.12A

CP*E NS

CP*Y NS

E*Y NS

CP*E*Y NS

LSD for E 0.5752

LSD for Y 0.5752

CP*E 1.1503

CP*Y 1.1503

E*Y 0.8134

CP*E*Y 1.6268


probability level

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main nutrient which plays significant role in the productivity of crops therefore, by

introducing legume in the cropping patterns might increases wheat crop protein (Ahmad,

2000). However, they further elaborated that if moisture is adequate and N is limited it

might result to low protein contents. Therefore, based upon our results outcomes it could

be concluded that protein have significant relationship with environments and soil

features like water and N. Similar conclusion was made by Rawluk et al. (2000) who

reported that growing environments influence the protein content in wheat and

significant contribution is from N.

4.4.2 Oil Contents of Canola Seed under Mung Bean-Canola Cropping Pattern (CP-6)

in Irrigated and Rain-fed Environments during Two Years

Oil contents of canola recorded in response to years statistically significant

variations. Maximum oil contents (42.57%) were recorded during first year while

minimum (39.76%) during second year (Table 4.6). The 11% increase of oil contents

was recoded in first year over the second year of the experiment. Whereas, both the

environments (irrigated and rain-fed) did not differ significantly for oil contents. The

interactive effect of Environment x Years on oil contents depicted non-significant

differences. The work by Koutroubas et al. (2008) reported that crop quality could be

improved by managing rainfall water in the form of irrigation.

4.4.3 Oleic Acid (%) of Canola Seed under Mung Bean-Canola Cropping Pattern (CP-

6) in Irrigated and Rain-fed Environments during Two Years

Canola Oleic acid contents under different cropping patterns during two years among

two environments for canola crop recorded did not show significant variations (Table 4.6).

Both the years did not differ statistically for oleic acid contents. In the same way, both the

119

environments did not differ significantly for oleic acid contents of canola crop. The

interactive effect of Environment x Years on oleic acid contents also depicted non-

significant differences. The work of Flagella et al., (2002) related to impact of irrigation on

fatty acid composition of sunflower depicted that under irrigation oleic acid ratio decreases

while quantity of linoleic and palmitic acid increased significantly.

4.4.4 Linoleic Acid (%) of Canola Seed under Mung Bean-Canola Cropping

Pattern (CP-6) in Irrigated and Rain-fed Environments during Two Years

Linoleic acid contents recorded in response to years and environments

(Irrigated and Rainfed) showed significant variations. Maximum linoleic acid contents

(11.28%) were recorded during first year while minimum linoleic acid contents

(10.22%) were recorded during second year (Table 4.6). A 10% reduction in linoleic

acid contents recoded in second year than the first year of the experiment. Furthermore,

both the environments differed significantly for linoleic acid contents. Maximum

linoleic acid contents (11.26 %) were observed from rain-fed environment while

minimum (10.23%) were recorded from irrigated environment. There was about 11%

reduction in linoleic acid contents when crop was shifted from irrigated to rainfed

environment.

The interactive effect of Environment x Years on linoleic acid contents depicted

significant difference. The significant difference in the linoleic acid contents under rain-

fed and irrigated environments as well as during both the years might be due to

variability of water at different growth stages and developmental of canola crop.

Similar results were reported by Dordas and Sioulas, (2008) who concluded that water

have impact on quality of crop. Lovelli et al. (2007) were of the view that crop quality

120

could be improved by giving irrigation to crop at different growth stages. Water stress

significantly limits crop growth and development which ultimately leads to reduction in

oil quality of crops.

4.4.5 Erucic Acid (%) of Canola Seed under Mung Bean-Canola Cropping

Pattern (CP-6) in Irrigated and Rain-fed Environments during Two Years

Family Brassicaceae is mainly associated with erucic acid (22:1) contents.

Erucic acid contents recorded in response to years showed significant variations (Table

4.6). Maximum erucic acid contents (35.59%) were recorded during first year while

minimum (13.25%) during second year. Whereas, non-significant difference was

recorded for erucic acid contents under both the environments. The 66% reduction in

erucic acid contents was observed during second year than the first year of the

experiment. The interactive effect of Environments x Years for erucic acid contents

depicted non-significant differences. In our conditions, erucic acid values are usually

higher because of cross pollination of canola types with local species. The significant

difference in the erucic acid content during both the years was due to variability in the

water availability at different critical growth stages of canola crop. Water is main factor

which can affect the quality of oil as crop yield and quality is dependent upon availability

of water (Igbadun et al., 2006). Similarly, Koutroubas et al. (2008) reported that crop

yield and quality could be improved by managing rainfall water in the form of irrigation.

Similar results were reported by Dordas and Sioulas (2008) who concluded that water

have impact on quality of crop. Meanwhile, Lovelli et al. (2007) Table 4.6 Different

quality parameters of canola seed under mung bean-canola cropping pattern (CP-6) in

irrigated and rain-fed environment during two years

121

Oil contents (%) Oleic acid (%) Linoleic acid (%) Erucic acid (%)

Y1 42.57A 53.14NS 11.28A 35.59A

Y2 39.76B 48.47 10.22B 13.25B

Irrigated 41.88NS 51.01NS 10.23B 26.59NS

Rain-fed 41.31 50.6 11.26A 26.258

Interactions NS NS ** NS

LSD for E 1.801 6.761 0.9617 2.6182

LSD for Y 1.801 6.761 0.9617 2.6182



were of the view that crop quality could be improved by giving irrigation to crop at

different critical growth stages. The effect of drought on palmitic acid was reported by

Mekki et al. (1999) who reported decreased contents of palmitic acid and unsaturated

fatty acids due to drought.

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4.4.6 Fibre Contents of Sorghum in Sorghum-Wheat Cropping Pattern (CP-3) under

Irrigated and Rain-fed Environments during Two Years

Fibre contents of sorghum crop in response to environments (Irrigated and

Rainfed) showed statistically significant variations (Table 4.7). Both the years did not

differ significantly for fibre contents of sorghum crop. Whereas, significantly maximum

fibre contents (9.22 %) were recorded under irrigated environment of sorghum

plantation while minimum fibre contents (6.53 %) were recorded from rainfed

environment. A 29% increase in sorghum fibre contents recoded in irrigated

environment than that in rain-fed environment. This increase was due to more water

availability in irrigated conditions which resulted in better crop growth and development

compared to that under rain-fed environment. The interactive effect of Environment x

Years on fibre contents of sorghum crop depicted non-significant difference.

4.4.7 Acid Detergent Fibre (ADF) of Sorghum in Cropping Pattern (CP-3) under

Irrigated and Rain-fed Environments during Two Years Acid detergent

fibre of sorghum crop in response to both the environments

(irrigated and rain-fed) showed statistically significant variations (Table 4.7).

Maximum acid detergent fibre contents (36.68 %) were recorded under irrigated conditions

while minimum acid detergent fibre contents (30.75 %) were recorded from rain-fed

conditions. A 16% increase in sorghum acid detergent fibre contents was recoded in irrigated

environment than that of rain-fed environment. Whereas, both the years did not differ

significantly for acid detergent fibre of sorghum crop. The interactive effect of Environment

x Years on acid detergent fibre contents of sorghum crop depicted non-significant

123

differences. Chapko et al. (1991) concluded lower NDF and ADF in cereal–legume cropping

system compared to other systems.

4.4.8 Nutrient Detergent Fibre (NDF) of Sorghum in Sorghum-Wheat Cropping

Pattern (CP-3) under Irrigated and Rain-fed Environments during Two

Years

Nutrient detergent fibre (NDF) of sorghum crop in response to Cropping patterns, year,

and environments (irrigated and rain-fed) showed statistically significant variations

(Table 4.7). Maximum nutrient detergent fibre contents (60.71%) were recorded under

irrigated environment while minimum (53.82 %) from rain-fed environment. An 11%

increase in sorghum nutrient detergent fibre contents recoded in irrigated environment

than the rain-fed environment. This increase was due to more water availability in

irrigated conditions which resulted in efficient nutrient and moisture utilization leading

to better crop growth and development compared to that under rain-fed environment

Whereas, both the years did not differed significantly for nutrient detergent fibre. The

interactive effect of the Environment x Years on nutrient detergent fibre contents of

sorghum crop have depicted non-significant differences.

Table 4.7 Different quality parameters of sorghum fodder in sorghum-wheat cropping

pattern (CP-3) under irrigated and rain-fed environments during two years

Fibre contents ADF NDF

Y1 7.43NS 32.89NS 56.21NS

Y2 8.32 34.533 58.32

Irrigated 9.22A 36.68A 60.71A

Rain-fed 6.53B 30.75B 53.82B

Interactions

LSD for E

NS NS NS

1.0651 3.879 6.1626

LSD for Y 1.0651 3.879 6.1626

124


probability level

125


Seasonal water use efficiency of summer and winter crops (Mash bean,

Sorghum, Maize and Mung, Wheat, Chick pea and Canola) varied considerably in

response to cropping patterns, year and environments (irrigated and rain-fed). For winter

crops, the highest water use efficiency (15.47 kg/mm) was recorded for wheat crop in

Mash bean-Wheat cropping patterns under rain-fed conditions (Table 4.8). However, the

lowest water use efficiency for wheat crop (8.74 kg/mm) was recorded in Maize-Wheat

cropping pattern under irrigated conditions. The increment in wheat water use efficiency

due to change in moisture environment from irrigated to rain-fed environment was 44%.

The water use efficiency of other crops like chick pea and canola remained significantly

different and lower than wheat in Maize-Chick pea and Mung bean-Canola cropping

patterns and under both the environments. WUE for chick pea did not vary significantly

under both the environments. While, for canola maximum water use efficiency (9.04

kg/mm) was recorded under rain-fed conditions and minimum water use efficiency was

calculated (6.71 kg/mm) when planted under irrigated environment in Mung bean-

Canola cropping pattern. The reduction in water use efficiency of canola due to its shift

from rain-fed to irrigated conditions was 26% in Mung bean-Canola cropping pattern.

Water use efficiency remained significantly different under both environments (irrigated

and rain-fed). However the maximum water use efficiency calculated under rain-fed

conditions (10.84 kg/mm) while minimum (8.99 kg/mm) was calculated under rain-fed

conditions. The increase in water use efficiency from irrigated environment to rain-fed

environment was 11%. The interactive effect of Environment x Year depicted significant

variation on water use efficiency of winter crops. The highest water use efficiency (11.59

126

kg/mm) was calculated during first year under rain-fed conditions while it remained less

and non- significantly different (9.19 and 8.79 kg/mm) during both years (Table 4.8) in

irrigated environment. The interactive effect of Cropping patterns x Environment x

Years on water use efficiency of winter crops depicted non-significant differences.

Water use efficiency for both the seasons when compared, varied significantly.

Maximum water use efficiency (9.92 kg/mm) was calculated for winter crops while

minimum water use efficiency (6.53 kg/mm) was calculated for summer crops. The

reduction in water use efficiency of summer crops than winter crops was 34%. Cropping

patterns x Environment x Years on water use efficiency of winter crops depicted non-

significant differences.

For summer crops, the highest water use efficiency (14.5 kg/mm) was recorded

for sorghum crop in Sorghum-Wheat cropping patterns under rainfed conditions.

However, the lowest water use efficiency (3.25 kg/mm) was calculated for mung bean

crop under Mung bean-Canola cropping pattern under irrigated environment fallowed

by mash bean crop (3.62 kg/mm) under irrigated environment. The reduction in water

use efficiency in summer season from rain-fed to irrigated environment was 78%. Water

use efficiency remained significantly different under both environments

(irrigated and rain-fed) during both the years. The interactive effect of Environment x

Year depicted significant variation on water use efficiency of summer crops. However

maximum water use efficiency was calculated under rain-fed conditions (7.41 kg/mm)

during 1st year, while, minimum (6.02 kg/mm) water use efficiency was

112

Table 4.8 Water use efficiency of summer and winter crops under six cropping patterns during two years among two environments

2009-10 2010-11 2009-10

Fallow-Wheat 0x 0x 0K 0x 0x 0K 10.70ij 10.35jk 13.29d 11.72ghi 12.51C

Mash bean-Wheat 3.47t-w 3.77s-w 3.62J 4.59rs 4.28r-v 4.43HI 12.13efg 11.04ghij 11.59D 16.427a 14.51bc 15.47A

Sorghum-Wheat 12.96def 11.95fgh 12.46C 15.35ab 13.65cd 14.50B 9.21l-p 8.26p 8.74F 9.55k-o 8.57op 9.06F

Maize-Wheat 9.25k-p 8.86nop 9.06F 10.29jkl 8.52op 9.41F 11.99fg 10.85hij 11.42D 15.78a 13.21de 14.50B

Maize-Chick pea 9.08m-p 8.20p 8.64F 10.18j-m 8.49op 9.34F 4.63rs 5.27r 4.95H 4.54rst 4.41r-u 4.48H

Mung bean-Canola 3.14w 3.35uvw 3.25J 4.06s-w 3.28vw 3.67IJ 6.46q 6.97q 6.71G 9.94j-n 8.14p 9.04F

6.31E 6.02E 7.41D 6.37E 9.19C 8.79C 11.59A 10.09B

6.17D

LSD for C 0.2268 LSD for CxCP 0.5556 LSD for CP x Y 0.5556

LSD for CP 0.3929 LSD for C x E 0.3208 LSD for C*CP*E 0.7858

LSD for E 0.2268 LSD for C x Y 0.3208 LSD for C*CP*Y 0.7858

LSD for Y 0.2268 LSD for CP x E 0.5556

Cropping patterns

Kharif Rabi Irrigated

Means Rainfed

Means Irrigated

Means Rainfed

Means 2009 2010 2009 2010 2010 - 11

10.53 E

Means

C 6.89 8 B .99 A 10.84 B 6.53 9.92 A

129

calculated under irrigated conditions. The increase in water use efficiency from irrigated

environment to rain-fed environment was 19%. In the same way, water use efficiency

under summer season differed for both the environments. Maximum water use

efficiency (6.89 kg/mm) was calculated under rain-fed environment while minimum

water use efficiency (6.17 kg/mm) was calculated for irrigated environment. There was

11% difference among both the environments for water use efficiency. The interactive

effect of Cropping patterns x Environment x Years on water use efficiency of winter

crops have depicted non-significant differences.

Water use efficiency in rain-fed regions and agricultural production could be

boost up by harvesting rainwater and using that water as supplemental irrigation. This

system is significantly good for increased crop production in rain-fed regions and it

could increase cropping potential of small holder of dry land agriculture (Batchelor et

al. 2003). The rainwater harvesting could improve the crop status under rain-fed regions

as in our studies and it can be instrumental to ensure local food security. Local food

security is essential tool for national food security. The previous findings proved that

rainwater harvesting increased the crop production and kitchen gardening which

increased food availability for domestic consumption (Joshi et al., 2005).

Based upon our findings it could be concluded that rainwater harvesting could

enables farmers to go for supplemental irrigation and improves the crop status under

rain-fed farming system. Furthermore, rainwater harvesting makes secure access to

water issues, build culture of natural conservation, and solve problems of ecological

poverty and decentralized water conservation and management system to ensure local

food security. Our findings were at par with Xiao and Wang, (2003) who emphasized

on the use of rainwater harvesting and supplemental irrigation with integration of

130

modern techniques to improve crop yield in rain-fed areas. The option of

microcatchment as rainwater harvesting could maximize rainwater use under rainfed

regions as concluded by Tsiouris et al. (2002).

4.6 ECONOMIC ANALYSIS

The economic analysis of the experimental data is essential to look at the

experimental results from farmer‟s point of view as they are often interested in the

benefits and the cost of the technology and also like to know the risks in adopting new

practices. Keeping in view the current scenario pooled data was analysed for economic

analysis. Partial budgeting was prepared for each cropping pattern under study to assess

the cost and benefits related to each cropping pattern. Prices of inputs and outputs

available from the local market were used for analysing the data economically using the

methodology as described by CIMMYT (1988).

4.6.1 Partial Budget of Different Crops and Cropping Patterns under Irrigated

Environment during Two Years

Partial budgets of various winter and summer crops for all the cropping patterns

under irrigated environments during two years is represented in table 4.9

The gross benefits of summer crops under all the cropping patterns when pooled

for years under irrigated environment ranged from Rs. 45125-107575 and zero in case

of fallow. The highest gross benefits were taken from sorghum fodder while lowest gross

benefits of Rs. 45125 were obtained from mung bean. According to data, total cost that

varied from Rs. 26374 for mung bean to Rs. 33328 for maize grain in both cropping

patterns, for the summer crops under irrigated environment.

131

Regarding the net benefits, sorghum fodder gave maximum net benefits of

Rs.80392, followed by maize grain of Rs.60641 and lowest net benefits of Rs.18751 was

recorded from mung bean under irrigated environment.

Among winter crops, highest gross benefits of Rs.189777 was obtained from

wheat under Mash bean-Wheat, followed by Rs.176265 from wheat in Maize-Wheat

cropping patterns, while lowest gross benefits of Rs.73290 were received from chick pea

from Maize-Chick pea cropping pattern. Highest net benefits of Rs.143298 were

obtained from wheat under Mash bean-Wheat and lowest net benefits were obtained by

chick pea (Rs.42816) under Maize- Chick pea cropping pattern.

4.6.1.2 Partial budget of different crops and cropping patterns under rain-fed


Partial budget of various winter and summer crops for all the cropping patterns

under rain-fed environment during both the years is represented in table 4.10. The

highest gross benefits of Rs.64758 were secured from sorghum fodder, followed by

mash bean which gave Rs.55786, whereas, lowest gross benefits (Rs.22733) were

obtained from maize crop (Maize-Chick pea). Total costs varied from Rs.24223 for

mung bean to Rs.28829 for each maize crop under rain fed environment. Regarding net

benefits, sorghum gave highest net benefits of Rs.36384 but maize crop suffered loss of

an amount of Rs.6096 (Maize-Chick pea) under rain-fed environment. In the same way

partial budget for winter crops revealed that highest gross benefits (Rs.97428)

were received from wheat under Mash bean-Wheat and lowest were obtained (Rs.25960)

from chick pea under Maize-Chick pea cropping patterns. In the meanwhile, the highest

net benefits (Rs.65558) were obtained from wheat crop in Mash bean-Wheat and lowest

132

net returns received from chick pea from Maize-Chick pea cropping pattern which gave

loss from the field i.e. its variable costs exceeds than its net returns.

4.6.2 Benefit Cost Ratio (BCR) of Different Cropping Patterns under Irrigated

Environment during Two Years

The results of BCR under irrigated environment for all the cropping patterns

revealed the highest BCR of 7.17 was from Mash bean-Wheat cropping pattern (CP2),

followed by Sorghum-Wheat cropping pattern (6.89) while, lowest cost benefit ratio of

5.12 was obtained from Maize-Chick pea cropping pattern under irrigated conditions

(Table 4.11). Highest BCR from CP-2 was due to higher yield of wheat under legume-

cereal cropping pattern. Fallow-wheat cropping pattern revealed BCR of 6.77.

4.6.2.1 Benefit cost ratio of different cropping patterns under rain-fed environment

during two years

The results of BCR under rain-fed environment for all the cropping patterns

revealed the highest BCR of Rs.7.17 from Mash bean-Wheat cropping patters (CP-2), 4

followed by Sorghum-Wheat cropping pattern (Rs. 4.787) while, lowest cost benefit

ratio of 1.37 was obtained from Mung bean-Canola cropping pattern (Table 4.12).

Highest BCR from CP-2 was due to higher yield of wheat under legume-cereal cropping

pattern. Highest BCR from CP-2 was because of the involvement of legume in cropping

pattern, due to which wheat yield was enhanced. Lowest BCR from CP-6 indicated that

it was due to poor performance of mung bean crop under rain-fed environment.

4.6.3 Marginal Analysis of Different Cropping Patterns

In the partial budget analysis total cost that vary and net benefit that each

cropping pattern was calculated but did not compare the cost that vary with the net

133

benefits. For such comparisons marginal analysis are required. Marginal analysis

involved dominance analysis and marginal rate of returns. To determine most profitable

cropping pattern by comparing the costs that vary with the net benefits obtained,

marginal analysis was performed. In order to do dominance analysis cropping patterns

were arranged in the ascending order of increasing variable costs.

A cropping pattern was dominated if its variable costs were higher than the

preceding cropping pattern, but its benefits were lower. Such cropping patterns was

termed as dominated cropping pattern and denoted by “D”. The dominance analysis for

irrigated and rain-fed environments is represented in tables (4.13 and 4.14).

The results depicted that Mung bean-Canola and Maize-Chick pea cropping

patterns under irrigated environment were dominated from other cropping patterns. The

results indicated that legume based cropping pattern under irrigated environment is more

profitable than non-legume based cropping pattern. While in case of the rainfed

environment Maize-Chick pea, Sorghum-Wheat and Maize-Wheat based cropping

Table 4.9 Partial budget of crops under irrigated environment during two years

Crops Gross Benefits

(Rs. ha-1)

Total Cost that vary

(Rs. ha-1)

Net Benefits

(Rs. ha-1)

Summer crops

Mash bean (CP-2) 75399 27474 47925

Sorghum (CP-3) 107575 27183 80392

Maize-Grain (CP-4) 93969 33328 60641

Maize-Grain (CP-5) 90210 33328 56882

Mung bean (CP-6) 45125 26374 18751

Winter crops

Wheat (CP-1) 173772 45757 128015

Wheat (CP-2) 189777 46479 143298

Wheat (CP-3) 159210 43991 115219

134

Wheat (CP-4) 176265 45586 130679

Chick pea (CP-5) 73290 30474 42816

Canola (CP-6) 117537 31615 85922

Table 4.10 Partial budget of crops under rain-fed environment during two years

Crops Gross Benefits

(Rs. ha-1)

Total Cost that vary

(Rs. ha-1)

Net Benefits (Rs.

Ha-1)

Summer crops

Mash bean (CP-2) 55786 25323 30463

Sorghum (CP-3) 64758 28374 36384

Maize-Grain (CP-4) 48651 28829 19822

Maize-Grain (CP-5) 22733 28829 -6096

Mung bean (CP-6) 30531 24223 6308

Winter crops

Wheat (CP-1) 70721 30209 40512

Wheat (CP-2) 97428 31870 65558

Wheat (CP-3) 75410 30257 45153

Wheat (CP-4) 73364 30379 42985

Chick pea (CP-5) 25960 27285 -1325

Canola (CP-6) 62206 26179 36027

Table 4.11 Net benefits and benefit cost ratio (BCR) of cropping patterns under

irrigated environment during two years

Cropping Pattern

Gross

Benefits

(Rs.ha-1)

Total cost

that vary

(Rs.ha-1)

Net Benefits

(Rs.ha-1)

Benefit

Cost

Ratio

(BCR)

Fallow-Wheat (CP-1) 173772 25660 148062 6.77

Mash bean-Wheat (CP-2) 265175 36976 228199 7.17

Sorghum-Wheat (CP-3) 266904 38686 228218 6.89

Maize grain-Wheat (CP-4) 270233 39457 230776 6.84

Maize grain-Chick pea

(CP-5)

163501 31901 131600 5.12

135

Mung bean-Canola (CP-6) 162662 28995 133667 5.61

Table 4.12 Net benefits and benefit cost ratio (BCR) of cropping patterns under

rain-fed environment during two years

Cropping Pattern Gross

Benefits

(Rs.ha-1)

Total cost

that vary

(Rs.ha-1)

Net

Benefits

(Rs.ha-1)

Benefit

Cost Ratio

(BCR)

Fallow-Wheat (CP-1) 70721 17886 52835 3.95

Mash bean-Wheat (CP-2) 153213 28596 124617 5.35

Sorghum-Wheat (CP-3) 140167 29315 110852 4.78

Maize grain-Wheat (CP-4) 122015 29604 92411 4.12

Maize grain-Chick pea (CP-5) 71426 28057 43369 2.54

Mung bean-Canola (CP-6) 92781 25200 67581 1.37

Table 4.13 Dominance analysis of different cropping patterns under irrigated


Cropping Pattern Total cost that vary

(Rs.ha-1)

Net Benefits (Rs.ha-

1)

Fallow-Wheat (CP-1) 25660 148062

Mung bean-Canola (CP-6) 28995 133667 D

Maize-Chick pea (CP-5) 31901 131600 D

Mash bean-Wheat (CP-2) 36976 228199

Sorghum-Wheat (CP-3) 38686 228218

Maize Grain-Wheat (CP-4) 39457 230776

Table 4.14 Dominance analysis of different cropping patterns under rain-fed

136


Cropping Pattern

Total cost that

vary

(Rs.ha-1)

Net Benefits (Rs.ha-

1)

Fallow-Wheat (CP-1) 17886 52835

Mung bean-Canola (CP-6) 25200 67581

Maize-Chick pea (CP-5) 28057 43369 D

Mash bean-Wheat (CP-2) 28596 124617

Sorghum-Wheat (CP-3) 29315 110852 D

Maize-Wheat (CP-4) 29604 92411 D

Table 4.15 Marginal analysis of different cropping patterns under irrigated


Cropping Pattern

Total cost

that vary

(Rs.ha-1)

Marginal

Cost

(Rs.ha-1)

Net

Benefits

(Rs.ha-1)

Marginal

Net

Benefits

(Rs.ha-1)

Marginal

Rate Of

Return

(%)

Fallow-Wheat (CP-1) 25660 - 148062 - -

Mash bean-Wheat (CP-2) 36976 11316 228199 80137 708

Sorghum-Wheat (CP-3) 38686 1710 228218 120 7.01

Maize Grain-Wheat (CP-4) 39457 771 230776 2558 331

Table 4.16 Marginal analysis of different cropping patterns under rain-fed


137

Cropping Pattern Total

cost that

vary

(Rs.ha-1)

Marginal

Cost

(Rs.ha-1)

Net

Benefits

(Rs.ha- 1)

Marginal

Net

Benefits

(Rs.ha-1)

Marginal

Rate Of

Return

(%)

Fallow-Wheat (CP-1)

17886 - 52835 - -

Mung bean-Canola (CP-

6)

25200 7314 67581 14746 201

Mash bean-Wheat

(CP-2)

28596 3396 124617 57036 679

patterns dominated from other cropping patterns in rain-fed conditions. The results

depicted that under the rain-fed conditions, wheat based cropping patterns are more

profitable than other cropping patterns.

To further refine the cropping pattern recommendations, marginal rate of returns

was calculated. The marginal rates of returns of different cropping patterns under

irrigated environment are represented in table 4.15. The analysis revealed that instead of

Fallow-Wheat, Mash bean-Wheat cropping pattern was recommended under irrigated

conditions.

The marginal rate of returns (MRR) was highest with Mash bean-Wheat

cropping pattern (708 %), compared to other cropping patterns. This was mainly due to

the differences in costs that vary, between cropping patterns were little but the

differences in the net benefits were huge under irrigated environments. However, for

Maize-Wheat and Sorghum-Wheat the marginal rate of returns were 301 and 7.01 %

respectively.

On the other hand under rain-fed conditions the marginal rate of returns of

different cropping patterns represented in table 4.16. The analysis revealed that instead

of Fallow-Wheat, Mash bean-Wheat cropping pattern was recommended under rainfed

conditions.

138

Marginal rate of returns (MRR) remained highest with the Mash bean-Wheat

cropping pattern (679 %), compared to other cropping patterns. This was mainly due to

the differences in costs that vary between cropping patterns were little but the differences

in the net benefits were huge under rained environments. However, for Mung bean-

Canola the marginal rate of returns was 201% under rain-fed conditions.

SUMMARY

The current study was carried out with the objective to get agro-economic efficiencies

of different cropping patterns under the command and un-command area of Pira Fatehal

small dam of Pothwar region (northern rain fed area of the Punjab province comprising

of civil districts of Rawalpindi, Jehlum, Attock and Chakwal). The experiments

constituted of six cropping patterns (Fallow-Wheat, Mash beanWheat, Sorghum-Wheat,

Maize-Wheat, Maize-Chick pea and Mung bean-Canola) and two growing environments

(irrigated and rain-fed) during summer and winter seasons of 2009 to 2011. The

experiments were laid out in Randomized complete Block Design (factorial) with three

factors i.e. (i) environment (irrigated and rain fed condition), (ii) year (iii) treatments

(cropping patterns) and three replications. The net plot size was 6m x 4m. Maize,

Sorghum, Mung bean, Mash bean were sown during each summer season for two years

on the same plots whereas, wheat, chick pea and canola were grown in succeeding winter

season for two years. The experiments under both the environments were initiated during

summer 2009 and completed during winter season 2010-11.

139

During the course of study, second year summer and winter season crops received more

rainfall compared with those of first year. Therefore, the economic yields of crops were

affected due to shortage of moisture during first year, under rainfed conditions, whereas,

performance of crops under irrigated environment during each season and year remained

better because the low rainfalls were compensated by supplementary irrigation water of

the small dam.

123

Among summer crops, sorghum green fodder yield was highest followed by grain yield

of maize and mash bean. Mung bean reflected poor response in terms of grain yield.

Among winter crops wheat crop productivity enhanced after the incorporation of

legumes in the cropping pattern (Mash bean-Wheat). While discussing the cropping

patterns Mash bean-Wheat cropping pattern performed better under both the

environments during both the years. NPK uptake was substantially higher under

irrigated environment when compared with those under rain-fed. Wheat crop following

mash bean showed more nutrient uptake in both the environments.

Oil quality of canola did not differ significantly in both the environments but showed

significant difference during two years.

Water use efficiency of wheat, following mash bean under both the environments

exhibited higher values when compared with those from sorghumwheat, fallow-wheat

and maize-wheat cropping patterns.

Highest net returns were obtained from Maize-Wheat (CP-4) cropping pattern 4) under

irrigated environment while Mash bean-Wheat (CP-2) cropping pattern ranked 1st for

140

net returns under rain-fed environment. Mash bean-wheat cropping pattern had

maximum benefit cost ratio (BCR) from both the environments. Marginal rate of returns

from Mash bean-Wheat (CP-2) cropping pattern were maximum when compared with

those from Fallow-Wheat (CP-1) cropping pattern. Maize-Wheat (CP-4) and Sorghum-

Wheat (CP-3) exhibited promising marginal rate of returns as compared to Fallow-

Wheat cropping Pattern. On the other hand, Mash bean-Wheat provided maximum

marginal rate of return followed by Mung bean-Canola cropping pattern when compared

with that of fallow-wheat cropping pattern under rain-fed environment.

Hence, it can be concluded from this study that farmers having

supplemental irrigation water resources should adopt maize (grain)-wheat cropping

pattern, based on economical return as well as efficient utilization of available

supplemental water, whereas, based on improved nutrient utilization and monetary

outputs, mash bean-wheat cropping pattern should be followed under rain-fed areas for

better resource management. , this study concludes that summer-fallowing practice is

not economical for the farming community of both supplemental irrigated environment

of command area of small dams and un-commanded (rain-fed) areas.

141

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Appendix 1: Average prices of seeds (Rs.kg-1) of different crops and fertilize

in different years, used in economic analysis.

Sr. # Item

Price (Rs. Kg-1)

Year 1 Year 2

1 Wheat 39 39

2 Chick pea 50 70

3 Canola 100 110

4 Mash bean 100 110

5 Mung bean 40 60

6 Maize 40 50

7 Sorghum 40 50

8 DAP (Fertilizer) 38 56

9 Urea (Fertilizer) 16.5 18.4

10 SOP (Fertilizer) 35 56

160

Appendix 2: Average market prices of crop produce (Rs.kg-1) of different crops

in different years, used in economic analysis.

Sr. # Item

Price (Rs. Kg-1)

Year 1 Year 2

1 Wheat 21.25 23.75

2 Chick pea 40 50

3 Canola 42.5 50

4 Mash bean 60 70

5 Mung bean 35 50

6 Maize 20 24

7 Sorghum 2 2.50

161

ANNEX-II

CERTIFICATE

(For: Incorporation Foreign Examinaers’ Suggestions) (To be

submitted with Hard-Bound Ph.D. Thesis (Final) for Result

Declaration)

(Ref. Rule No. 158, Catalogue 2010-2011)

(Updated / Circulated vide No. PMAS-AAUR/5086 Dated 11-11-2010)

Certified that the thesis entitled “ Evaluation Of Different Cropping Patterns Under The

Command Area of Small Dam In Pothwar: (A case Study of Pira Fatehal Dam)”

submitted by Mr. Masood Akhtar, Registration No. 08-arid-738, a Ph. D. student, has

been corrected/improved as suggested by the foreign examiners. Further, their

suggestions have incorporated and “ Annotated compliance Report” is also Submitted.

Supervisory Committee:

Supervisor: ________________________ Date ___________________

(Prof. Dr. Fayyaz-ul-Hassan)

Member: ______________________ Date ____________________ (Dr. Muhammad Rasheed)

Member: _______________________ Date ____________________

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(Dr. Rifat Hayat)

Chairperson: __________________________

EVALUATION OF DIFFERENT CROPPING PATTERNS UNDER …

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