IMPACT ASSESMENT OF CLIMATE CHANGE ON CROP …

IMPACT ASSESMENT OF CLIMATE CHANGE ON CROP

PRODUCTION SYSTEM IN PUNJAB, PAKISTAN

MUHAMMAD WAQAR YASIN

14-arid-500

INSTITUTE OF GEO-INFORMATION & EARTH OBSERVATION

PIR MEHR ALI SHAH

ARID AGRICULTURE UNIVERSITY RAWALPINDI

PAKISTAN

2018

ii

IMPACT ASSESMENT OF CLIMATE CHANGE ON CROP

PRODUCTION SYSTEM IN PUNJAB, PAKISTAN

by

MUHAMMAD WAQAR YASIN

(14-Arid-500)

A thesis submitted in partial fulfillment of

the requirement for degree of

Doctor of Philosophy

in

Remote Sensing & GIS

INSTITUTE OF GEO-INFORMATION & EARTH OBSERVATION

PIR MEHR ALI SHAH

ARID AGRICULTURE UNIVERSITY RAWALPINDI

PAKISTAN

2018

CERTIFICATION

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CERTIFICATION

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

thesis falls under plagiarism, If found otherwise at any stage, I will be responsible

for the consequences.

Name: Muhammad Waqar Yasin Signature: ______________

Registration Number: 14-ARID-500 Date: ______________

Certified that the contents and form of thesis entitled Impact Assessment

of Climate Change on Crop Production System” submitted by “Muhammad

Waqar Yasin” has been found satisfactory for requirements of the degree.

Supervisor: ____________________

(Prof. Dr. Mobushir Riaz Khan)

Member: _______________________

(Prof. Dr. Rai Niaz Ahmad)

Member: _______________________

(Prof. Dr. Muhammad Yasin)

Director: _________________________

Director Advanced Studies: ___________________________

iv

DEDICATION

I dedicate this thesis to my beloved wife “Humaira Waqar” who has

shared in all my joys and sorrows, whose love, care and devotion has

been the strength of my striving

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CONTENTS

List of Abbreviations xii

List of Tables xi

List of Figures xiv

Acknowledgements xvii

Abstract xviii

Chapter 1 Introduction 1

1.1 CLIMATE CHANGE 1

1.1.1 Extreme Weather Events 3

1.1.2 Carbon Dioxide Increase 4

1.2 IMPACTS OF CLIMATE CHANGE 4

1.2.1 Reduced Nutrient Levels of Produce 5

1.2.2 Increased Competition of Pests 5

1.2.3 Irrigation and Rainfall 5

1.2.4 Global Food Security Challenges 6

1.3 PROBLEM STATEMENT 7

1.3.1 Floods and Droughts 7

1.3.2 Shift in Spatial Boundaries of Crop Potential Areas 7

1.3.3 Changes in Productivity 7

1.3.4 Reduced Water Availability 8

1.3.5 Changes in Cropping Pattern 8

1.3.6 Changes in Land Use Pattern 8

1.3.7 Coastal Agriculture 9

1.4 OBJECTIVES 9

1.4.1 Assessment of Climate Change 10

1.4.2 Impact Assessment of Climate Change on Agriculture 10

1.4.3 Unsupervised classification 11

1.4.4 Supervised Classification 11

1.5 FINDING RESILIENCE IN CROP AREAS VERSUS CLIMATE

CHANGE 12

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Chapter 2 REVIEW OF LITERATURE 14

2.1 CLIMATE CHANGE 14

2.1.1 Greenhouse Gases 15

2.2 CAUSES OF CLIMATE CHANGE 16

2.2.1 Globalization 16

2.2.2 Natural Causes 16

2.2.3 Droughts 16

2.3 GENERAL IMPACTS OF CLIMATE CHANGE 17

2.3.1 Impact on Human Migration 17

2.3.2 Impact on Coral Reefs 18

2.3.3 Impact on Specie Extinction 18

2.4 IMPACT ASSESSMENT OF CLIMATE CHANGE ON

AGRICULTURE 18

2.5 ADAPTATIONS AND MITIGATIONS 21

2.6 ADAPTATIONS TO CLIMATE CHANGE W.R.T AGRICULTURE 22

2.7 MONITORING OF AGRICULTURE 24

2.7.1 Monitoring and Management of Agriculture with Remote Sensing 25

2.8 RESILIENCE OF CROPS AND CLIMATE 26

Chapter 3 MATERIALS AND METHODS 28

3.1 DATA USED 28

3.1.1 Moderate Resolution Imaging Spectroradiometer - Normalized

Difference Vegetation Index 28

3.1.2 Climate Data 29

3.1.3 The Administrative Map 30

3.1.4 Specific Crop Data 30

3.1.5 Crop Calendar Data 30

3.2 INTERPOLATION OF CLIMATIC DATA 30

3.3 CROP MAPPING AND MONITORING 32

3.3.1 ISODATA Algorithm 32

3.3.2 Selection of Optimal Classification Map of NDVI 34

3.3.3 Signature Output 34

3.3.4 Linking Vegetation Behavior with Crop Calendar 34

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3.3.5 Area of NDVI Classes per District 35

3.3.6 Mapping Specific Crops 35

3.4 INFLUENCE OF CLIMATIC CHANGES ON CROP

PERFORMANCE 36

3.4.1 Impact of Climatic Change on Crop Performance 36

Chapter 4 RESULTS AND DISCUSSION 49

4.1 CLIMATE CHANGE IN PUNJAB 49

4.2 IMAGE CLASSIFICATION 52

4.3 LINKING VEGETATION BEHAVIOR WITH CROP CALENDAR 60

4.4 MAPPING SPECIFIC CROPS 60

4.4.1 Wheat Area Map of Punjab 60

4.4.2 Maize Area Map of Punjab 62

4.4.3 Sugarcane Area Map of Punjab 64

4.4.4 Cotton Area Map of Punjab 65

4.4.5 Gram Area Map of Punjab 65

4.4.6 Validation-Remote Sensing Vs Reported Data Of Wheat Crop 66

4.4.7 Validation – Remote Sensing vs Reported Data- of Maize Crop 66

4.4.8 Validation – Remote Sensing vs Reported Data- of Cotton Crop 66

4.4.9 Validation – Remote Sensing vs Reported Data- of Sugarcane Crop 66

4.4.10 Validation – Remote Sensing vs Reported Data- of Gram Crop 67

4.4.11 Impact of the Rainfall on Wheat Vegetation 67

4.4.12 Impact of the Rainfall on Wheat Vegetation 67

4.4.13 Impact of Climate Change on Crop Performance 67

SUMMARY 82

LITRETURE CITED 85

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LIST OF ABBREVIATIONS

GIS

RS

Geographic Information System

Remote Sensing

MODIS Moderate Resolution Imaging Spectro-Radiometer

NDVI Normalized Difference Vegetation Index

NOAA National Oceanic and Atmospheric Administration

NASA National Aeronautics and Space Administration

CSA Climate Smart Agriculture

GPS Global Positioning System

IWSR Irrigation Water Supply Safety

IPCC Intergovernmental Panel On Climate Change

UNFCCC United Nations Framework Convention On Climate Change

PMD Pakistan Metrological Department

IDW Inverse Distance Weighting

ISODATA Iterative Self Organizing Data Analysis Technique

LULC Land Use/Land Cover

SPSS Statistical Package For The Social Sciences

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LIST OF TABLES

Table 4-1: Stepwise Linear Regression results to map wheat 61

Table 4-2: Stepwise Linear Regression results to map maize 62

Table 4-3: Stepwise Linear Regression results to map sugarcane 64

Table 4-4: Stepwise Linear Regression results to map cotton 65

Table 4-5: Stepwise Linear Regression results to map gram 65

Table 4-6: Showing Impact of climate variables on Kharif crop production 78

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LIST OF FIGURES

Figure 1-1: Conceptual Diagram of the research 13

Figure 3-1: Stacked Image of NDVI (2000 – 2014) 29

Figure 3-2: District Map of Punjab Along with Weather Stations 32

Figure 3-3: General Crop Calendar for field crops of Pakistan 33

Figure 3-4: General Crop Calendar for field crops in Districts of Punjab 33

Figure 3-5: Monthly Comparison of Average Rainfall January 36

Figure 3-6: Monthly Comparison of Average Rainfall February 37

Figure 3-7: Monthly Comparison of Average Rainfall March 37

Figure 3-8: Monthly Comparison of Average Rainfall April 38

Figure 3-9: Monthly Comparison of Average Rainfall May 38

Figure 3-10: Monthly Comparison of Average Rainfall June 39

Figure 3-11: Monthly Comparison of Average Rainfall July 39

Figure 3-12: Monthly Comparison of Average Rainfall August 40

Figure 3-13: Monthly Comparison of Average Rainfall September 40

Figure 3-14: Monthly Comparison of Average Rainfall October 41

Figure 3-15: Monthly Comparison of Average Rainfall November 41

Figure 3-16: Monthly Comparison of Average Rainfall December 42

Figure 3-17: Monthly Comparison of Average Temperature January 42

Figure 3-18: Monthly Comparison of Average Temperature February 43

Figure 3-19: Monthly Comparison of Average Temperature March 43

Figure 3-20: Monthly Comparison of Average Temperature April 44

Figure 3-21: Monthly Comparison of Average Temperature May 44

Figure 3-22: Monthly Comparison of Average Temperature June 45

Figure 3-23: Monthly Comparison of Average Temperature July 45

Figure 3-24: Monthly Comparison of Average Temperature August 46

Figure 3-25: Monthly Comparison of Average Temperature September 46

Figure 3-26: Monthly Comparison of Average Temperature October 47

Figure 3-27: Monthly Comparison of Average Temperature November 47

Figure 3-28: Monthly Comparison of Average Temperature December 48

Figure 4-1: Average Rainfall (mm) 1951 – 1980 map of Punjab 50

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Figure 4-2: Average Rainfall (mm) 1981 – 2015 map of Punjab 50

Figure 4-3: Average Temperature Map (0C) 1951 – 1980 map of Punjab 51

Figure 4-4: Average Temperature Map (0C) 1981 – 2015 map of Punjab 51

Figure 4-5: Rainfall Change (mm) 1951 – 2015 map of Punjab 53

Figure 4-6: Temperature Change (0C) 1951 – 2015 map of Punjab 53

Figure 4-7: Average divergence of classification runs 54

Figure 4-8: Map of NDVI classes representing a (mix) land cover/land use data 54

Figure 4-9: Grouping of NDVI classes representing Group-1 55
















Figure 4-25: Wheat area map of Punjab 61

Figure 4-26: Maize area map of Punjab 62

Figure 4-27: Sugarcane area map of Punjab 63

Figure 4-28: Cotton area map of Punjab 63

Figure 4-29: Gram area map of Punjab 64

Figure 4-30: Validation – Remote Sensing vs Reported data 68

Figure 4-31: Validation – Remote Sensing vs Reported Data 69



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Figure 4-35: Impact of Rainfall on wheat Vegetation 71

Figure 4-36: Impact of Temperature on wheat Vegetation 75

Figure 4-37: Impact of Climate Change on Crop Performance 72


Figure 4-39: Impact of Rainfall on Crop Performance in Bahawalnagar 73

Figure 4-40: Impact of Temperature on Crop Performance in Bahawalnagar 74


Figure 4-42: Impact of Climate Change on Crop Performance in Jhang 75



Figure 4-45: Impact of Climate Change on Crop Performance in Chakwal 76

Figure 4-46: Impact of Climate Change on Crop Performance in Chakwal 77

Figure 4-47: Comparison of area and production of Gram crops 79

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ACKNOWLEDGEMENTS

I feel pleasure to extend my deep sense of gratitude to my kind supervisor Prof.

Dr. Mobushir Riaz Khan, Institute of Geo-Information & Earth Observation,

PMAS Arid Agriculture University, Rawalpindi, Pakistan for his immense

guidance, suggestions, significant insight and unswerving encouragement

throughout the course of study as it was just because of his motivation and drive

with I was able to complete my work. I would always remain indebted to his

kindness.

I have no words to express thanks to Supervisory committee members, Prof.

Dr. Rai Niaz Ahmad and Prof. Dr. Muhammad Yasin for extending their keen

interest, sincere guidance, continuous encouragement and sympathetic attitude that

enabled me to complete these studies. I am highly obliged for indemonstrable and

endearment guidance and support of my teachers at Institute of Geo-Information &

Earth Observation, PMAS-AAUR.

I wish to thank, Institute of Geo-Information & Earth Observation for providing

me an opportunity to conduct a part of PhD research at his laboratory, learning

advanced. I also appreciate Mr. Muhammad Amin as an amazing man for giving

me great inspiration to step ahead towards this achievement. I could not find better

advisors for my Ph.D. study. I am also blessed to have love and support of

Muhammad Usman, Ramsha Malik, Mobashir Ali, Kashif, Col Saif, Col Tahir, my

brothers Yasir, Rizwan and Numan.

xviii

ABSTRACT

Climate Change is undeniable fact with pronounced effects on human life

especially food produce. Agricultural policy makers require accurate and timely

information regarding crop performance, shifting of cropping patterns and allied

information pertaining to changing climate. A method was developed to efficiently

monitor and predict changes in crop pattern with the changing climate in the

province of Punjab using satellite data process and GIS. In this research firstly,

changes in temperature and rainfall from 1980 to 2015 were assessed at district

level in comparison with the data of 1951 to 1980. Secondly, using multi-temporal

remote sensing in combination with crop statistics crop area maps was developed.

Thirdly, remote sensing based crop profiles were generated from 2000 till 2014.

Lastly, the changes in crop performance were compared with temperature and

rainfall variations. The outcomes demonstrated that increase in temperature

influences production of crops and a critical adverse relationship on yield and

production was found with temperature rise or fall. Punjab province was divided

into three equivalent imaginary parts (Northern, Central and Southern divisions)

and was found that the most extreme increment in the temperature occurred in the

Southern division i.e. lower Punjab (DG khan, Rahim Yar Khan, Rajanpur). In

Central portion i.e. Faisalabad, likewise raised temperature was observed. While in

Northern Punjab temperature remained less than the normal range. As far as

precipitation is concerned, increase in precipitation took place in Central Eastern

and Northern segment i.e. Gujrat, Lahore, Sheikhupura and Kasur; while extreme

decline in precipitation happened in Central Western and Southern potions i.e.

Bahawalnagar, Sargodha, Chiniot and Pakpattan. Enormous negative effects of

temperature increase were found on cotton crop in July and August. For maize,

adverse effect of expanding temperature in the month of May was observed; while

rain in the period of September positively contributed. Rise in November

temperature has a positive impact on wheat; however, December temperature has a

negative impact on wheat production. Major important facts like mentioned above

can be easily derived from this study and may be taken as guide line for various

agriculture departments to predict their crop production / yield very accurately at

every stage of crop cycle through remote sensing images and GIS. Farmers and

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agriculturists may be educated about the effects of these variables i.e. temperature

and rainfall on crop growth from sowing to harvesting, even on monthly basis. This

would entail better crop monitoring, requirement of water at every stage of crop

cycle, accurate estimation of yield and better adaption / mitigation strategies for the

future.

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CHAPTER 1

INTRODUCTION

1.1 CLIMATE CHANGE

Warming of the climate system is undeniable and marked by increased global

average air and ocean temperatures, melting of snow and rising global average sea

levels (Metz, 2007).

Globalization has its own advantages and disadvantages; adverse changes in

environment is one of the greatest challenges associated with the fate changing

globalization (Cowens, 2009). Most of the states are making an effort to convert

their rural economy to the industrial while setting aside the environmental concerns

(Nasir & Rehman, 2011). Now the question is that which practices are causing

abrupt climatic changes. Humans are mainly responsible for emerging CO2

enriched and warm world giving least consideration to the environmental impact

assessments of industrial and development projects. Global warming has many ill

effects such as human migration, depleting water resources, shifts in crop patterns,

declining yields and threats of food insecurity. Climate change can be treated as a

free factor that can influence the generation of agriculture items as well as bothers

the financial unfaltering quality, influencing the free market activity adjust of

sustenance (Leichenko, Thomas, & Barnes, 2010).

Climate change is the fundamental driver of food insecurity since it influences

the profitability of the agribusiness, its steadiness, and different segments of the

nourishment framework, including capacity, access, and use. Environmental

change impacts on farming and discovering adoption techniques through evaluating

the size and nature of these effects on trim efficiency. This is also true in case of

developed countries as well because, technology to mitigate adverse effects of

climate change is either still under development or very expensive. This

necessitates looking for various adaptation strategies to changing climates in all

domains of life (Abid, Schneider, & Scheffran, 2016).

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Climate change is a global phenomenon yet the developing and least developed

states are more prone to the climatic changes. Pakistan is not only a developing

country but also an agrarian economy; therefore, climate change can be a serious

hurdle in its progress. Since, Pakistan is a developing country with less

technological expertise which can be used as an alternative approach to counter

greenhouse emissions. Normal reserve of Pakistan is arable land output of which is

contributing 21 percent in GDP through agriculture. Two most important crop

seasons in Pakistan are Rabi and kharif requiring certain levels of rainfall and

precipitation which if exceeds or decreases than the required amount our

agricultural productivity is reduced due to droughts and floods (Naheed & Rasul,

2010).

Monsoon is a season comprised of rainfalls and considered as one of the

biggest natural blessing as it provides ample water needed for the production of

crops. Inter annual fluctuations occur during monsoon (dry seasons/flood events)

causing a genuine test for the maintainable harvest generation. Pakistan's

hydrological administration upstream and downstream is exponentially associated

with north getting excess of water from precipitation or snow melting. Many a

times, it instantly keeps running down to the low height fields of Punjab and Sind

flooding the developed grounds and devastating standing field crops. If there

should be an occurrence of powerless rainstorm and less precipitation in the

northern-half, again the farming fields of south endures a great deal because of

higher water requirement due to high temperatures and yet less accessible water.

Another threat associated with the climate change is increased use of input

restricting healthy competition among the poor farmers. Like other countries,

edibles security is a main concern of Pakistan's relevant authorities. Wheat, rice,

and maize are the primary edible crops with cotton and sugarcane as significant

cash crops of Pakistan. In this way, sustenance security strategy basically

concentrates on the creation of these harvests. Subsequently, it is critical to look at

the nexus between evolving atmosphere (temperature, precipitation, mugginess,

and daylight) and the yield of significant products including wheat, rice, maize, and

sugarcane (Ali et al., 2017).

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Impacts of climate change on crop production (both developed and the least

developed) has been studied numerous times (Ayub, 2010). The common climate

change variables in such studies are temperature and rainfall. Change in growing

season and shifting of cropping patterns as a consequence of changing temperatures

have also been studied. Research also reveals some alarming adverse effects of

climate on crop production in Asia. It is perceived that temperature can rise to 30

C

by the year 2040 and 5°C - 6°C by the end of 21ST

century due to unhealthy

economic practices. This temperature increase can challenge the fate of few crops

forever. To state an example, due to the existing scenario of deteriorating

environment and climate Asia’s wheat production will be lowered to half (Singh &

Bantilan, 2009).

Climate research on agriculture depicts both the negative and positive

relationships between climate change and crop production. For example, no crucial

negative impacts of environmental change on wheat production in Pakistan (Janjua,

Samad, Khan, & Nasir, 2010) but another study conducted in 2011 proved negative

impact of temperature rise on agriculture production. It has also been found that

rain fall changes in Pakistan affects agriculture production. The vulnerability of

Pakistan’s agrarian sector needs serious consideration especially after experiencing

regular floods in 2010, 2011 and 2012 (Schütte & Kreutzmann, 2016). This raises

key concerns among the policy makers of the state; whether cultivate level

adjustment is satisfactory to manage environmental change impacts on agricultural

profitability.

1.1.1 Extreme Weather Events

Some scientists assume that environmental changes will incite more

phenomenal atmospheric events such as heat waves, dry seasons, strong breezes

and overpowering deluges. Environment of models have reliably predicted such

atmospheric events due to climatic deviations. For example, models don't surrender

to whether the amount of tempests in a more sizzling world would be essentially

than ebb and flow regards, however, analysts also concluded that the nature of

ocean storms will change along with time span of ocean storm season.

4

Dry seasons are immediate aftereffects of climatic changes and cause severe

shortage of water available to plants. Dry seasons have been accountable for a bit

of the more real starvations on the planet, sociological components are also

indispensable (Keshavarz, Karami, & Vanclay, 2013). Heat waves can cause

phenomenal strains in crops and thereby reduces yields particularly when occur in

the midst of particular conditions of the plant’s life-cycle (preparation, unit or

natural item set). Also, heat waves can achieve wilted plants (as a result of raised

transpiration rates) which can cause yield adversely if not checked by water

framework. Strong breezes can cause leaf and member hurt, and what's more

"sandblasting" of the earth against the foliage. Considerable deluges that routinely

achieve flooding can, in like manner be ruining to crops and to soil structure

(Jovanovic & Stikic, 2012). Several Plants can't make dew in deferred waterlogged

conditions in light of the way that the roots need to unwind. Besides, flooding can

crumble topsoil from prime creating ranges; achieving irreversible living space

hurt. Generous breezes joined with precipitation can uproot trees, destroy houses,

and other structures.

1.1.2 Carbon Dioxide Increase

Researchers are also in agreement that the levels of ambient CO2 (carbon

dioxide) have expanded from 280 to 380 ppmv. These levels have been

consistently expanding by 1.9 ppm on every year basis since 2000 to a great extent

because of petroleum derivative consuming.

1.2 IMPACTS OF CLIMATE CHANGE

Climate change has marked influence on food security around the world,

neighborhood, and close-by level. Environmental transformation can disturb food

access both in terms of quantity and quality. Expected additions in temperatures,

changes in precipitation, changes in exceptional atmosphere events, and abatements

in water openness may all result in decreased agricultural yield. Additions in the

repeat and reality atmospheric events can similarly meddle with food movement,

and rise in sustenance costs after phenomenal events are required to be more

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progressive later on. Extending temperatures can add to squander and polluting

(Wheeler & Von Braun, 2013).

1.2.1 Reduced Nutrient Levels of Produce

Carbon dioxide is basis of photosynthetic process and thus important for flora

expansion. Little but optimal additions in carbon dioxide cause more plant

advancement dut to enhances assimilation of CO2. It is expected that higher levels

of CO2 will attain higher harvestable products. In any case, this depends essentially

on the availability of sufficient water and supplements for plant growth. A couple

of analysts assume that one interruption to this extended productivity with less

nutrient levels. Accepting certified, this could have an huge influence on human

prosperity if additional manures were not joined into trim age (Atta-Krah &

Sumberg, 1988).

1.2.2 Increased Competition of Pests

Whilst crops are required to react to expanded CO2 with solid vegetative

expansion, different flora are additionally thought to react in a comparative design.

Weeds have ended up being more gainful and are required to assault new living

spaces as worldwide temperature alteration increments. For instance, scientists

found that toxin ivy is really ending up more toxic environmental carbon dioxide

levels. Studies also demonstrated that herbicides end up being less intense in a

higher carbon dioxide condition, implying that higher rates of herbicides will be

important to accomplish similar levels of control. On the off chance that

deteriorates life more and duplicate all the more every year, it is conceivable that

they could spread harvest maladies into new generation ranges. It is additionally

conceivable that increments in temperature, dampness and carbon dioxide could

bring about higher populaces of damaging nuisances.

1.2.3 Irrigation and Rainfall

Alter in environment may in like manner influence the water availability and

water requirement for agricultural consumption. In case temperature augmentations

6

and more sporadic precipitation events result from an unnatural climate change, it

is possible that water framework needs could augment later on. Precipitation in

south-east of US states has extended around 10% over the previous century.

Regardless, some part of this development may be a result of changes in the repeat

of tropical tornados. Tornados generally speaking outcome in precipitation events

more unmistakable than 2 sneaks in a day which occur at erratic between times; in

this less important in a green sense than are precipitation events that happen

additional constantly, even with cut down collections (Rost et al., 2009). Plants

creating more carbon dioxide condition may have cut down water needs. Moreover,

no matter how you look at it extended dampness will direct transpiration,

furthermore diminishing the necessity for water. In any case, these points of

interest will probably be obscured by the nonappearance of available water as a

result of extended dry seasons and warmth waves. The yields will happen more

seriously than when below "common" creating environments, and would possible

need additional water to comply with these climactic variations. In retribution of

movements, plant reproducers are starting at now endeavoring to grow new

collections of harvests that are believed to be dry season lenient, and extra

adaptable to fluctuating stages of temperature (Barrow, 2014).

1.2.4 Global Food Security Challenges

Universally, these things of normal change on cultivating and support source

are apparently going to look like those initiated in the United States. Regardless,

unique stressors, for example, masses development might exposed up the things of

natural variation on nutrition safety. In production countries, modification options

identical changes in trim institute or cultivating practices, or changes up to water

outline are extra bound in the United States other than industrialized nations

(Change, 2014). Climate associated agitating impact to nutrition spread and

transport, generally or locally, may perfectly affect prosperity and quality and also

on food availability. For example, the sustenance transportation system in the

United States as regularly as conceivable moves broad volumes of grain by water.

By virtue of a remarkable atmosphere event, there are barely any substitute

pathways for transport. Transportation changes, for instance, lessen the limit of

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farmers to exchange their grains to all-inclusive markets, and can impact overall

food costs (Handmer et al., 2012). High temperatures and an inadequacy of rain in

the mid-year of 2012 incited a champion among the most outrageous summer dry

seasons the nation has seen and acted bona fide impacts to the Mississippi River

watershed, an imperative cross-country delivering course for Midwestern

cultivating. This drought realized basic sustenance and financial hardships in view

of abatements in cargo transport development, the volume of stock passed on, and

the amount of Americans used by the tugboat business.

1.3 PROBLEM STATEMENT

Although Pakistan is amongst the lowest contributing nation for global

greenhouse gas emissions but is amongst the one of the most vulnerable countries.

Followings are major climate change effects on agriculture of Pakistan:

1.3.1 Floods and Droughts

Increased intensity and recurrence of extraordinary climate occasions,

especially during the last decade, is causing floods and droughts. Both the natural

phenomena have direct or indirect impact on agriculture and have serious

consequences on food security.

1.3.2 Shift in Spatial Boundaries of Crop Potential Areas

Global changing in climate (higher temperature and reduced soil moisture) is

affecting the length of growing period and resultantly affecting quantity and quality

of biomass, maladies and involve spatial moves in potential ranges of agrarian

products.

1.3.3 Changes in Productivity

The worth and amount of harvest yield is being influenced by environmental

changing in two ways: a) immediate impacts from changing in temperature, less

water availability, CO2 fixations and extraordinary occasions; and b) circuitous

8

impacts through variations in circulation, recurrence and seriousness of irritation

and infection episodes, rate of flame, weed invasion, or through changes in soil

properties.

1.3.4 Reduced Water Availability

Our maximum water supply depends upon glaciated northern areas of Hindu

Kush Hamalaya (HKH) which are under the phenomena of recession owing to

enhanced global warming. The increment in temperature has coordinate impact on

yield evapotranspiration and thrashing of soil dampness. The expansion in crop

evapotranspiration when joined by less precipitation in rain fed situations comes

about into diminish in crop evapo-transpiration because of constrained accessibility

and utilization of soil dampness by crops. Owing to this factor, there is increased

stress among upper and lower riparian farmers and irrigation managers of canal

command areas of Pakistan.

1.3.5 Changes in Cropping Pattern

An ascent in hotness, decrease and moves in precipitation might expand the net

water system, water prerequisites and other info needs of yields, along these lines,

constraining agriculturists to roll out improvements in cropping examples to

conform to environmental change.

1.3.6 Changes in Land Use Pattern

An earth-wide temperature boost, climatic boundaries and CO2 concentrations

would lead towards changes in arrive utilize frameworks because of change in the

developing period of products. The climatic variations in dry atmospheres would

come about expanded saltiness and additionally water logging; which would

absolutely interest for combination of ranger service and aquaculture with the yield

based cultivating frameworks.

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1.3.7 Coastal Agriculture

Saline water intrusion from Sea to the Indus delta has also affected costal

agriculture, mangroves and breeding habitats of fish. This saline water intrusion is

happening due to climate change.

Additional issues have occurred because of an increment the planet’s typical

temperature. A more blazing world has recently achieved modification of surface

physical properties and a couple of areas which in this manner provoked changes in

water and temperature regimes, conduit stream and reinforcing of land debasement.

Each one of these movements has successfully impacted human prosperity,

agribusiness, open arable grounds and boondocks resources, sensible progression

and life of people.

Crop production is affected by changing in climate is an extensive variety of

ways which requires programs to counter the threat posed by climate change to

agriculture. Efficiency, alleviation and adjustment moves can make put at various

innovative, authoritative, institutional and political levels (Arakelyan, Moran, &

Wreford, 2017).

Precision agriculture is an integrated solution to address with these inter-linked

problems of nourishment security and environmental change that explicitly goes

for financially extending agrarian benefit while modifying and gathering

adaptability of provincial systems to ecological change. CSA works at various

levels while lessening ozone depleting substance outflows from agribusiness

including products, animals and fisheries (Lipper et al., 2014).

1.4 OBJECTIVES

Keeping in view the aforementioned threats of changing climate on agriculture,

it is imperative to look for various options to counter them in order to maintain

food security of the country in a sustainable manner. As mentioned earlier, there

are two ways to combat the adverse climate change effects on crops, viz,

technological inventions and adaptation actions. As Pakistan is less technically

10

advanced country, therefore, we should concentrate on adaptability measures while

performing research and development for mitigation technologies.

The proposed study makes use of satellite data, GIS and weather stations’ data

to evaluate the effect of climate change on agricultural land use in Punjab,

Pakistan. In this proposed study, aimed at detecting resilience in agricultural land

uses of Pakistan in order to find options for changing in climate adaptations. The

objective of study is to development of remote sensing and GIS based methods,

showing change in agricultural land use of Punjab, Pakistan over a period of time

and then linking it to climatic change.

1.4.1 Assessment of Climate Change

Before embarking upon the adaption of changing in climate in the field of

crops, it is important to know the quantum of changing climate and where exactly

these changes are happening. Further, what are the adverse impacts of such

changes and which areas witnessed changes in climate but are resilient in terms of

agricultural productivity.

1.4.2 Impact Assessment of Climate Change on Agriculture

In order to study changes in agricultural land use in response to changing

climate, it is required to know what is grown, how much is grown, how it is

performing over time at various places and how much it will produce.

Estimation of crop area is main components of crop production and yield

estimation. To assess crop production correctly, the crop area must be projected

precisely. Some of the usually adopted methods for estimating of crop area are

terrestrial surveying methods, farmers’ evaluation and satellite remote sensing

(Lobell, Ortiz-Monasterio, Asner, Naylor, & Falcon, 2005). The information on

crop area statistics forms the backbone of an agricultural statistical system. The

crop area should cover the entire area under each crop and statistical evaluations

are generally obtained through survey of sampling sights which are then upscale to

administrative levels using statistical methods. As farmers grow crops under

11

diverse cropping classifications, such as mono-cropping, mixed cropping,

continuous cropping or repeated cropping, a comprehensive method is mandatory

to attain the area under a specific crop. This can be attained with comparative ease

when a single crop is grown in a ground in a specific season, but it becomes

difficult under mixed cropping or intercropping. In this case, at least twice a year

sampling is done in the field. It develops even more problematic in areas where the

land archives and crop records are not sustained properly.

Advancements in computer and satellite remote sensing in recent past have led

to the accessibility of dense and high performance computing technology that are

well suited to the demands of remote sensing and geographic information system

(Mather & Koch, 2011). In geographic information system and remote sensing data

advantageous because of the vast area and repeated reporting during a cropping

season (Leckie, 1990). A quantity of methods are available for providing land

estimates for main crops by use of satellite data. Remote sensing and geographic

information system (GIS) technologies are also being used for crop area estimation

(Menon & Bawa, 1997) . Global Positioning System (GPS) and remote sensing is

being used to identify the two noteworthy classifications of image arrangement

procedures incorporate unsupervised (ascertained by programming) and supervised

(human-guided) classification.

1.4.3 Unsupervised classification

Where results (groupings of pixels with regular qualities) rely upon the item

examination of an image without the user providing case classes. The PC utilizes

strategies to make sense of which pixels are associated and groups them into

classes. The customer can indicate which calculation to be utilized and the coveted

number of yield classes.

1.4.4 Supervised Classification

Relies upon the likelihood that a user can pick test pixels in a picture that are

illustrative of specific classes and after that direct the picture planning

programming to use these arrangement goals as references for the gathering of each

12

and every other pixel in the photo. Preparing locales (otherwise called testing sets

or information classes) are picked in perspective of the learning of the customer.

The customer similarly sets the cut-off points for how practically identical diverse

pixels must be to pack them together. These breaking points are consistently set in

light of the other worldly qualities of the preparation territory, give or take a

specific augmentation (frequently in view of "shine" or quality of appearance in

particular ghostly groups). The client likewise assigns the quantity of classes that

the picture is grouped into. Numerous examiners utilize a mix of directed and

unsupervised characterization procedures to create last yield examination and

grouped maps.

1.5 FINDING RESILIENCE IN CROP AREAS VERSUS CLIMATE

CHANGE

Climate-smart agriculture (CSA) is an integrative method to deal with address

these interlinked problems of nourishment safety and climate change, that

specifically points economically expanding rural profitability, to help fair

increments in cultivate livelihoods, sustenance security and advancement, adjusting

and constructing versatility of horticultural and sustenance security frameworks to

environmental change at numerous levels; and diminishing ozone depleting

substance emanations from farming including harvests, animals and fisheries

(Lipper et al., 2014).

Agriculture influenced by environmental change is an extensive variety of

courses and there are various passage focuses for starting CSA programs or

improving existing exercises. Efficiency, relief and adjustment moves can make

put at various mechanical, hierarchical, institutional and political levels.

(Arakelyan et al., 2017). Thus, following objectives of the presented study were

coined to assist policy makers and researchers working in agriculture and food

safety.

13

The specific objectives of the proposed project are:

To identify and compile changing climate parameters (temperature and

rainfall) at administrative levels of Punjab (1980 -2015).

Mapping and monitoring of crop areas using multi temporal satellite

imagery dataset over the period from year 2000 to year 2014 (i.e. 15 years).

Linking of changing temperature and rainfall with crop performance

(profiles) to identify the impact of changing climate.

To assist policy makers / researchers in finding resilience against changing

climate (temperature and rainfall). The aforementioned objectives are

covered in the presented thesis as explained by the following Figure 1.1.

Figure ‎1-1: Conceptual Diagram of the research

14

CHAPTER 2

REVIEW OF LITERATURE

2.1 CLIMATE CHANGE

Climate change results from the long haul weather designs that portray the

areas of the world. The expression "weather" allude to the fleeting (every day)

changes in temperature, wind, as well as precipitation of an area (Moran et al.,

1998).

During the consume time season, practically climatic models have expected

hail to increase. The Himalayan glaciers (75%) are expected to retire and will go to

the bottom by 2035. The slump or escalation in length can handle to droughts and

flooding. Climate climax will affect productivity and thus create food insecurity

problems (Gagné, 2016).

By 2050, the planet will have expanding crop production to feed an anticipated

9 billion individuals who have the changing examples of ingestion, the results of

neighborhood climate move and furthermore the expanding shortage of real land

and water (Godfray et al., 2010).

Change in climate is relied upon to have antagonistic result on Pakistan. This is

unexpected for a nation, which positions 135th on the planet as far as worldwide

greenhouse gasses (GHG) outflows per capita, yet positions sixteenth as far as

powerlessness to environmental change. Environmental change represents a

noteworthy danger to all measurements of reasonable improvement and has

boundless effects crosswise over different divisions and biological communities,

for example, nourishment, water and vitality; woods and biodiversity; seaside and

marine condition; and in addition on the event and power of atmosphere related

perils, for example, surges and dry spell. It additionally conveys potential for

inward and outer clashes.

15

2.1.1 Greenhouse Gases

“Greenhouse gases” got their name since they trap warm (vitality) like a green

house in the lower some portion of the air. As a result greater amount of these

gasses are added to the air, more warmth is caught. This additional warmth prompts

higher air temperatures close to the Earth's surface, changes climate and raises the

temperature of the seas.

There is solid proof that greenhouse gasses have just started to warm the

planet. If this situation remains the same, it is expected that the supply of

greenhouse gases will grow mostly from the burning of vestige, but also from the

change in landuse over the next century. This in turn will lead to changes in

precipitation patterns, and thus, changing regional climate and ecological zones.

Although various impacts on a range of domains are expected from the global

climate change, the agriculture sector would be affected largely as it is directly

depending on the climatic variables such as temperature and precipitation

(Nordhaus, 1991).

Every single climatic model demonstrates a rising temperature. The

precipitation design has changed with lessened precipitation over South and

Southeast Asia. Since the 1970s, more exceptional and delayed dry seasons have

happened. Everlasting snow cover has fallen both on the level and additionally in

the profundity of the snow cover. The worldwide mean ocean level is relied upon

to ascend by 0.18 to 0.59 m before the century is over (Mahato, 2014).

The danger of substantial scale change in climate is one of the focal problems

confronting the world. There is across the board agreement among mainstream

researchers that the planet is being warmed, that this warming is caused by living

being discharges of nursery gasses (GHGs), and that the outcomes of kept warming

are probably going to be extreme. There is likewise worldwide worry about the

issue: in a current Pew survey, greater parts in every one of the 40 countries

surveyed said that environmental change was a significant issue, and a worldwide

middle of 54% said that it was an intense issue (Carle, 2015).

16

2.2 CAUSES OF CLIMATE CHANGE

2.2.1 Globalization

Globalization has its own particular points of interest and impediments;

environmental change is one of the greatest disservices related with the destiny

evolving globalization (Cowen, 2009). The vast majority of the countries are trying

to change over their agrarian economy to the mechanical and putting aside the

ecological concerns (Nasir & Rehman, 2011).

Climate is primarily produced by the issue of heat-trapping gases, mainly

carbon dioxide, generated by vehicles, industrial processes, power stations and

deforestation. The gases we emit today will remain in the atmosphere for decades.

The longer we wait to take action to reduce emissions, the steeper and more costly

the emission reductions must be in the future to prevent dangerous rates of global

climate change.

2.2.2 Natural Causes

Vast changes in the recurrence and power of tropical violent winds obstruct

the waves and also their distribution to expanding barometrical greenhouse gasses.

Pattern discovery is additionally hampered by noteworthy impediments in the

accessibility and nature of worldwide chronicled records of tropical typhoons. It

stays questionable, subsequently, regardless of whether past variations in tropical

violent wind action have surpassed the fluctuation anticipated from common

reasons (Knutson et al., 2010).

Anthropogenic factors are controllable, however, these being commanding

characteristics, because of which global warming has been aggravated and more

heat has been caught in the biosphere than normally required to direct the life

forms.

2.2.3 Droughts

More exceptional and longer dry spells observed over more extensive

territories since 1970s, especially in the tropics and subtropics. Expanded aeration

17

connected by means of high temperatures and less rainfall has added to changes in

dry season. Changes in sea surface temperatures (SST), wind directions,

diminished snow cover have additionally been connected to dry spells.

2.3 GENERAL IMPACTS OF CLIMATE CHANGE

Worldwide climate change is just one of the bigger arrangement of

destabilizing ecological changes, each of them mirroring the expanding human

settlement of the ecosphere. These incorporate major worldwide changes, for

example, stratospheric ozone consumption, biodiversity misfortune, overall land

debasement, freshwater exhaustion, and others, for example, the disturbance of the

basic cycles of nitrogen and sulfur, and the worldwide spread of diligent natural

contaminations. All have awesome results for the maintainability of environmental

frameworks: sustenance creation; human monetary exercises and human populace

wellbeing.

Myers (2002), for instance, evaluated that in 1995 there were around 25

million individuals uprooted as an outcome of ecological change. Further, he

anticipated that by 2050 this number would ascend to roughly 200 million,

considering statistic change and falling apart natural conditions.

2.3.1 Impact on Human Migration

By focusing on the ecological danger and individual choices, the calculated

models outputs show trends of relocation in numerous areas due to natural

anxieties and anticipated future change. This originates from the expository

concentrate on danger, as opposed to considering relocation to be a social marvel

and entrenched arrangement of social and statistic communication and change. This

is a critical point, since affirmation of existing relocation infers that natural drivers

should be considered close by different drivers. This is expressly acknowledged by

(Kolmannskog, 2010) ,who take note of the many-sided quality of relocation as a

human procedure, despite the fact that they keep on focusing on the scan for a

meaning of 'ecological transients', as unmistakable from different sorts of vagrants.

18

2.3.2 Impact on Coral Reefs

Oceanic temperatures in various tropical areas have extended by 1°C, over

the span of late decades, and are at the present time growing at ~1– 2°C

consistently. Coral whitening happens when the warm versatility of corals and their

photosynthetic symbionts (zooxanthellae) is outperformed. Mass coral blurring has

occurred in association with lifted sea temperatures throughout late years and

incorporates the loss of the zooxanthellae following unending photo-inhibition. The

rate of the progressions that are anticipated shows a noteworthy issue for tropical

marine biological communities and recommends that excessive warming can't

happen without the misfortune and corruption of coral reefs on a worldwide scale

(Hoegh-Guldberg, 1999).

The connection of water, imperativeness, cultivating and climate is an

essential one. Regularly increasing degree, that association is reducing out of

change gambling food, water and essentialness safety. Natural changing is ponder

we can never again reject as its things have over up being dynamically evident

around the world. On the once-over of most sultry years on record, for all intents

and purposes reliably since 1992 is fused and, as demonstrated by NASA and

NOAA data, 2015 was the most bursting.

2.3.3 Impact on Specie Extinction

High temperature is most likely going to be the best clarification behind

species decimations in this century. The Intergovernmental Panel on Climate

Change says a 1.5°C increasing may put 20-30% of classes in threat of destruction.

In situation the planet warms by more than 3°C, most situations will struggle.

Many of the world's undermined species live in ranges that will be extremely

influenced by environmental change. Furthermore, environmental change is going

on too rapidly to alter their live cycles for adoption to such change.

2.4 IMPACT ASSESSMENT OF CLIMATE CHANGE ON AGRICULTURE

Global Climate change is probably going to globally effect of food security

situation, as food production is mainly related to the agriculture sector. Increment

19

in the mean temperature can lessen the span of many harvests and consequently,

decrease the yields of many crops. In territories where temperatures are near the

physiological maxima for crops, warming will largely affect their yields.

Climate change circumstances result from rise in temperatures, changes in

precipitation, and higher CO2 obsessions. There are three courses in which the

nursery impact is basic of agribusiness. At first, extended natural CO2 centers can

straightforwardly influence the improvement rate of product plants and weeds.

Besides, CO2 impelled variations of atmosphere may adjust levels of temperature,

precipitation and solar radiation that can affect plant and animal gainfulness

(Mahato, 2014).

Another threat related with change in climate is that the use of inputs would be

increased, thus limiting healthy competition for the poor fellows. Like other

countries, food security is one of the most prominent policies of the Pakistani

government. Wheat, rice and maize are the most important crops with cotton and

sugarcane as large currency deposits of Pakistan. This is why food strategy focuses

on the production of these crops. Thus it is important to examine the correlation

between the changing climate (temperature, precipitation, humidity and sunshine)

and the yield of the most important crops counting wheat, rice, corn and sugar cane

(Wheeler & Von Braun, 2013).

With the help of harvest reproduction models, these research showed that the

yields of grain in many countries would drop with increase in temperature. The

economists have also been concerned that crops in many developing countries

would be more complex to the climate than in the US (Mendelsohn, Dinar, &

Williams, 2006).

The climate change on horticulture and human prosperity is: 1) the organic

effect on trim yields; (2) the subsequent effect on the outcomes, including costs,

generation and utilization; and 3) the effect on per capita calorie utilization and

infant sustenance. The biophysical impacts of environmental change on

horticulture prompt changes underway and cost, which are changing without

anyone else's input, for example, agriculturists and other market members,

20

changing the harvest blend, input usage, creation, sustenance request, nourishment

utilization and exchange, notwithstanding the adjustments in precipitation, the

higher temperatures affected by climate change additionally increment the water

request of the yields. The proportion of water utilization to prerequisites is alluded

to as irrigation water supply safety (IWSR). The littler the proportion, the more

noteworthy the water stack on inundated gather yields (Backlund, Janetos, &

Schimel, 2008).

Food security is specifically and in a roundabout way associated to climate

change. Any adjustments in the climatic parameters, for example, temperature and

stickiness that decide plant development will specifically influence the measure of

sustenance delivered. Circuitous linkage alludes to cataclysmic occasions, for

example, surge and dry spell, which are expanding because of climate change,

bringing about an incredible harvest calamity and sendoff extensive patches of

arable land which are unsatisfactory for development and along these lines

undermine nourishment security. The net effect of food security relies upon

introduction to worldwide natural changes and the capacity to adapt to worldwide

ecological change and recuperate it. On a global scale, gradually eccentric climate

conditions will prompt a diminishment in farming generation and higher

sustenance charges, instigation nourishment weakness (Mahato, 2014).

The effects of change in climate on plant production in different countries;

both developed and least developed has been the subject of many research. The

common climate change variables in such studies are temperature and precipitation.

Changes in the vegetation period and shifting of patterns as a result of changing

temperatures were also investigated. The research also shows some alarming

statistics on the effects of climate change on plant production in Asia. It is

perceived that the temperature can rise to 30 ° C by the end of this century due to

unhealthy economic practices until 2040 and 5 ° C to 6 ° C. This temperature

increase can challenge the fate of a few harvests forever. For example, due to the

existing scenario of the deteriorating environment and the climate.

Half of the total populace (2.9 billion individuals) lives not exactly $ 2 every

day. Effectively 800 million individuals are undernourished and sustenance

21

creation needs to twofold in the following 35 years to address future issues. These

advances must be accomplished even with environmental change, the current

accord that it will influence our lives from numerous points of view. The effect on

individuals' work will be most noteworthy in the tropics and subtropics,

particularly in Africa, primarily in light of the fact that numerous poor little

ranchers rely upon horticulture and have couple of options (M. Parry et al., 2001).

Heat waves can cause outrageous hotter environment which can decrease the

yields when they happen amid critical times of the vegetation's cycle (fertilization,

husk or natural product amount). Warmth waves can likewise prompt non

domesticated plants (because of expanded transpiration rates) which can cause loss

of yield on the off chance that they are not neutralized by water system. Solid

breezes can cause leaf and appendage harm and also "sand impacting" of the dirt

against the foliage. Substantial downpour, which regularly prompts flooding, can

likewise be hindering to yields and earth arrangement (Jovanovic & Stikic, 2012).

An assortment of potential effects (and potential advantages) of climate change

on urban areas have been distinguished up until now. Various late surveys have

portrayed these, including the IPCC Third and Fourth Assessment, their accord is

that the principle effects of environmental change on urban areas are likely: effect

of ocean level ascent on beach front urban areas (counting the impacts of tempest

surges); Effects of outrageous occasions on manufactured frameworks (e.g. wind

tempests and tempest surges, surges of overwhelming precipitation occasions,

warm emissions and dry spells); Effects on well-being (warmth and icy related

mortality and bleakness, nourishment and water borne malady, vector-borne

ailment) coming about because of higher normal temperatures and/or extraordinary

occasions; Effects on vitality utilization (warming and cooling, vitality for water);

Impact on water accessibility and assets (Hoegh-Guldberg, 1999).

2.5 ADAPTATIONS AND MITIGATIONS

Adaptation is an essential aspect countering ex devours for the effects of

climate and the appraisal of the risk and is one of the strategic alternatives (Smit et

al., 1999). The vital part of the modification as a political reaction of the

administration was perceived universally. Article 4.1b of the United Nations

22

Framework Convention on Climate Change (UNFCCC, 1992) states that the

gatherings "are obliged to figure and execute national and, where suitable,

territorial projects, alleviation measures and measures to encourage fitting

adjustment to environmental change "The Kyoto Protocol (Article 10) obliges the

gatherings to advance and encourage adjustment and to utilize adjustment

innovations to handle environmental change (Halvorssen, 2007).

Soil carbon sequestration is a technique to alleviate climate change.

Unsustainable land use climate practices have increased the attentiveness of carbon

dioxide in to the atmosphere. Effects of climate change and global warming are

increasing at unprecedented rate. Current research has recommended that proper

management practices can reduce this risk up to some extent. (Lal, 2004).

Work on adaptation option to climate change is assessed. These are largely

focused on development of topology of adaptation and classify and characterize

most suitable option. It basically highlights the adaptation of all different sectors to

cope with the adverse impacts of Climate change. The study was carried out in

Canadian agriculture fields and the results reveled that with the proper policy

management and implementation of environmental friendly risk of climate change

on crop sector and food scarcity can be minimized (Shahid, 2011).

2.6 ADAPTATIONS TO CLIMATE CHANGE W.R.T AGRICULTURE

Many potential adjustment options for agricultural adjustments have been

proposed which are measures or practices that could be practiced to diminish the

normal antagonistic effect. They include an extensive variety of structures

(specialized, budgetary, administrative), scales (worldwide, local, neighbourhood)

and members (governments, businesses, agriculturists) (Smithers and Smit 1997,

Skinner et al., 2001). A large portion of them speak to conceivable or potential or

adjustment measures, as opposed to those really embraced. The impacts of

environmental change frequently influence certain adjustments, in spite of the fact

that the alteration procedure itself stays hazy (Smit & Skinner, 2002).

It is important to comprehend what sorts and types of adjustment are

conceivable, possible and plausible; who might be engaged with their execution;

23

and what is required to encourage or advance their improvement or reception. A

fundamental initial phase in tending to these worries is the distinguishing proof and

portrayal of adjustment conceivable outcomes in agriculture (McCalla & sh, 2006).

Despite the significant impact of climate change, including changeability and

extremes, change in agriculture does not work and creates in connection to these

climatic boosts alone. Non-climatic powers, for example, monetary conditions,

governmental issues, nature, society and innovation unmistakably significantly

affect horticultural basic leadership, including versatile basic leadership (M. L.

Parry, Rosenzweig, Iglesias, Livermore, & Fischer, 2004).

Primary climate change affect appraisals did not study any modification. With

the possibility to adjust the negative effects of environmental change, adjustment is

imperious for evaluating the effect of environmental change (Reilly, 1995). In spite

of the fact that farming is a standout amongst the most vigorously examined

divisions in connection to the impacts of environmental change the adjustment in

agribusiness has not yet been unequivocally considered in the effect evaluation

writing. This is expected to a limited extent to the way that many investigations

don't go past the estimation of the collect pay responses and basically overlook

human basic leadership in the farming segment. Customary, situation based

investigations that accommodate forecasts of potential impacts in agribusiness have

driven the adjustment for the most part through suppositions about human reactions

(Adger, Brooks, Bentham, Agnew, & Eriksen, 2005).

Recognizing that climate change could negatively affect rural generation has

communicated the desire to incorporate flexibility with horticultural frameworks.

An objective and financially savvy technique can be the usage of expanded farming

dietary enhancement. Nourishment expansion can enhance strength in an

assortment of routes: by making a more noteworthy capacity to control bother

episodes and to moderate pathogen transmission that may compound under future

climatic situations, and also by buffering plant generation from the impacts of more

prominent atmosphere fluctuation and extraordinary occasions. Such focal points

point to the undeniable estimation of the presumption of product expansion to

enhance protection, yet the suspicion was moderate (M. L. Parry et al., 2004).

24

Natural changes can influence a wide range of parts of rural generation. With

more prominent atmosphere inconsistency, moving temperature and precipitation

designs, and other worldwide change segments, we expect a progression of harvest

and biological system reactions that will influence the vital agrarian procedures.

These impacts incorporate changes in supplement cycling and soil dampness, and

also moves in nuisance and malady infections, all of which unequivocally influence

nourishment creation and sustenance security (Fuhrer et al., 2003). These

progressions are relied upon to increment abiotic and biotic anxiety and power

farming frameworks to meddle all the more unequivocally later on.

Another threat linked with climate change is that the use of inputs would be

increased, thus limiting healthy competition for the poor fellows. Like other

countries, food security is one of the most prominent policies of the Pakistani

government. Wheat, rice and maize are the most important food crops with cotton

and sugar cane as large cash deposits of Pakistan. This is why food policy is on the

production of these crops. Thus it is important to examine the correlation between

the changing climate (temperature, precipitation, humidity and sunshine) and the

yield of the most important crops including wheat, rice, maize and sugar cane.

2.7 MONITORING OF AGRICULTURE

Varieties of agricultural ecosystems have turned out to be more essential for

farming, as environmental change has expanded. Study has demonstrated that

product yields are exceptionally touchy to changes in temperature and

precipitation, particularly amid blooming and natural product improvement stages.

Temperature maxima and minima and additionally regular movements can greatly

affect plant development and creation. A more noteworthy inconstancy of

precipitation, including surges, dry season and outrageous precipitation occasions

has influenced nourishment security in many parts of the world (M. Parry,

Rosenzweig, & Livermore, 2005).

Such watched agriculture susceptibilities to temperature and precipitation

changes recommend the need to create flexible frameworks that can support plants

against atmosphere inconstancy and outrageous atmosphere occasions, particularly

25

in vital formative stages, for example, anthesis. There are assortments of courses in

which enhanced cultivating frameworks demonstrate that all the more

fundamentally complex frameworks can relieve the impacts of climate change on

plant creation (Adams, Glyer, & McCarl, 1989).

A similar investigation of agriculture frameworks in Sweden and Tanzania,

two locales where horticulture has experienced environmental change and

outrageous occasions, has demonstrated that agrarian assorted variety has expanded

the strength of generation frameworks. Sweden endured frosty resilience issues,

while Tanzania experienced issues of warmth resistance and sporadic El Niño

cycles. The two areas encountered a noteworthy regular dry season. In these cases,

examine demonstrated that fruitful administration rehearses fit for buffering and

moderating environmental change frameworks were those that were by and large

biologically unpredictable and wild assortments coordinated into the farming

framework and the fleeting and spatial decent variety of the way of life (Tengö &

Belfrage, 2004).

Managing the numerous obstructions to compelling adjustment requires an

exhaustive and dynamic strategy approach that envelops a scope of scales and

issues, for instance, from the comprehension of ranchers to change the hazard

profiles to build up proficient markets that encourage reaction procedures. Science

should likewise adjust. Multidisciplinary issues require multidisciplinary

arrangements, H. Concentrate on incorporated and non-disciplinary science and

reinforce the interface with chiefs. A key component of this approach is the usage

of adjustment appraisal structures which are significant, powerful and simple to

work by all partners, experts, government officials and researchers (Howden et al.,

2007).

2.7.1 Monitoring and Management of Agriculture with Remote Sensing

The inborn properties of agriculture makes remote sensing a perfect skill for

observing and administration (Chen et al., 2004). These attributes include: (a)

farming exercises are by and large done in expansive spatial areas, which

influences the regular field to review or evaluation tedious and normally costly; (b)

the agrarian generation per unit isn't so huge contrasted with different ventures; (c)

26

Most yields are yearly herbs with various development and improvement organizes

in various seasons, which implies that horticultural exercises have clear

phonological rhythms and the intra-yearly change can be extremely extraordinary;

(d) Agriculture is unequivocally influenced by human exercises and administration

where opportune and exact observing data is required. These inborn qualities of

agribusiness require novel methods for observing plant development and rural

generation (Chen et al., 2008).

Remote sensing innovation meets these prerequisites by its speed, precision,

economy, timing, dynamic and redundant observing limit. Remote detecting

innovation has been connected in agribusiness widely since its beginning time in

the 1960s. Presently a few worldwide and national operational frameworks of

observing agriculture with remote sensing has been worked. The quantity of

comparable operational frameworks at provincial scale is much more. These

frameworks give auspicious and significant data to agrarian generation,

administration and arrangement making. Then again, the requests emerging from

the applications in farming divisions have likewise upgraded the advance and

development in remote detecting innovation. The primary uses of remote detecting

in agribusiness administration and observing include: trim recognizable proof and

cropland mapping, edit development checking and yield estimation/forecast,

reversal of key biophysical, biochemical and natural parameters, trim harm/debacle

observing, accuracy cultivating, and so on (Chen et al., 2008).

2.8 RESILIENCE OF CROPS AND CLIMATE

Resilience is characterized as the affinity of a framework to hold its

hierarchical arrangement and efficiency subsequent an irritation (Holling, 1973)

.Subsequently, a versatile agro ecosystem will keep on providing an imperative

administration, for example, nourishment generation if tested by extreme dry

season or by a substantial diminishment in precipitation. In agrarian frameworks,

crop biodiversity may give the connection amongst stress and flexibility on the

grounds that a decent variety of life forms is required for biological systems to

work and give administrations (Heal, 2000).

27

Acknowledgment that climate change could have negative results for farming

creation wants to incorporate strength with horticultural frameworks. One

levelheaded and financially savvy strategy might be the execution of expanded

horticultural harvest broadening. Yield broadening can enhance strength in an

assortment of routes: by inducing a more noteworthy capacity to stifle bother flare-

ups and hose pathogen transmission, which may intensify under future atmosphere

situations, and in addition by buffering crop generation from the impacts of more

noteworthy atmosphere fluctuation and extraordinary occasions. Such advantages

point toward the conspicuous benefit of embracing crop enhancement to enhance

versatility, yet selection has been moderate (Lobell et al., 2008).

Unmistakably farmers are looked with expanding worry from climate change

and that the expanded execution of enhanced rural frameworks can be a beneficial

approach to incorporate versatility with agriculture frameworks. The difficulties for

the expanding reception of broadened agrarian administration techniques are both

logically and politically arranged. In the logical field, the selection of expanded

cultivating frameworks could be fortified if agriculturists could have a superior

thought of how to improve an enhanced structure to augment generation and

benefits. Reap and scene reenactment models that can display a progression of

atmosphere situations and scene demonstrating with agriculture gainfulness

situations would enable agriculturists to discover ideal techniques to keep up

generation and benefit. Partner based participatory research would likewise be

extremely valuable as the specialists could display techniques that appear to be

conceivable to agriculturists (Fan & Pandya-Lorch, 2012).

In agriculture frameworks, crop biodiversity may give the connection amongst

stress and strength in light of the fact that a decent variety of creatures is required

for biological systems to work and give administrations (Heal, 2000). Evacuating

entire utilitarian gatherings of species or expelling whole trophic levels can make

biological systems move from a coveted to less-wanted state, influencing their

ability to produce environment administrations (Folke et al., 2004). This impact

features the likelihood that rural frameworks as of now might be in a less-wanted

state for the proceeded with conveyance of biological community administrations.

28

CHAPTER 3

MATERIALS AND METHODS

Agriculture remains one of the main human activities and is being affected by

changing climate. In this context, policy makers need updated information on the

changing climate and its impacts on agricultural produce. Thus, there is need to

acquired regular and up-to-date spatial information about important climatic

variables, their influence crop producing areas to improve decision making in this

regard. Following material and methods were used to develop means to provide the

requisite information.

3.1 DATA USED

Following data have been used to study the impact of changing climate on crop

productions systems of Punjab, Pakistan:

3.1.1 Moderate Resolution Imaging Spectro-Radiometer - Normalized

Difference Vegetation Index

MODIS instrument offers new potential regional crop mapping by giving a

close every day worldwide scope of remote sensing, transitional resolution (250 m)

information since February 2000 at no cost to the end client (Justice and

Townshend, 2002). MODIS information empowers unmistakable multi-temporal

VI signs of particular yields to be identified at the 250 m pixel level.

MOD13Q1 NDVI product of MODIS sensor of TERRA satellite at 250 m

resolution has been used for this research. A 16-date time cycle of MODIS 250 m

NDVI data (MOD13Q1,) across 2000-2014 was downloaded and stacked for

District Punjab. For this purpose, 334 images of NDVI (Figure 3-1) were extracted

from those HDF files which were re-projected in GCS-WGS-84 projection system

and batch processing was used to subset the study area. A solitary 250 m pixel

found totally inside each field limits was chosen to speak to each site. A solitary

pixel was utilized instead of a pixel window (e.g., 2×2 pixels) to dispense with

blended edge pixels made out of different LULC sorts from being incorporated into

29

the preparation and approval information. The pixel's NDVI time arrangement was

extricated and outwardly surveyed to confirm its spectral– transient attributes were

predictable with the crop type. These re-projected subset images were stacked in a

single image for classification so that crop calendar can be generated.

Figure 3-1: Stacked Image of NDVI (2000 – 2014)

3.1.2 Climate Data

This study required climate data which was obtained from Pakistan

Meteorological Department’s (PMD) weather stations across Punjab. The data

included temperature (0C) and rainfall (mm) for different regions in District Punjab

for the following time spans:

a) Average data for two normal periods (1951 to 1980 and 1980 to 2015)

The data were used to calculate the climatic change occurred in the study

area since last 65 years.

b) Monthly average data from 2000 to 2014

The last fifteen years climatic data was used to observe the effect of

changing climate on crop producing areas of Punjab. These two parameters

(Temperature and Rainfall) have the longest and largest information scope

in the nation.

30

3.1.3 The Administrative Map

The administrative map of District Punjab (Figure 3-2) was obtained from

the repository of Institute of Geo-Information and Earth Observation. The map

presented the boundaries of districts of study area.

3.1.4 Specific Crop Data

Specific crop data was gathered from 2000-2014 by the department of

Agriculture. The study involved 90 arbitrarily random fragments of 700 x 700 m.

The ranges by means of superior thickness of harvests are tested more seriously.

i.e., extra fragments are likewise tested in the regions where crops command the

land utilize. The information was gathered by visits to every one of the fields for

each fragment. The spatial circulation of the overviewed portions was dictated by

isolating the region in squares of 10 x 10 km. Each square was sub-isolated in 100

cells of 1x km2. In each square, three cells were haphazardly chosen for reviewing.

In every cell, cropped area, generation of each yield per unit zone, water system

conspires, and so forth of rural regions show in the portion of 700x700 was

measured.

3.1.5 Crop Calendar Data

Crop calendar data (Figure 3-3 & 3-4) for the respective vicinity were

accessible on the website of Pakistan Meteorological Department. These were

compared crop reporting data.

3.2 INTERPOLATION OF CLIMATIC DATA

Firstly, the interpolation of two climatic normal was executed (1951 to 1980

and 1980 to 2015) to produce a map of climate change for over last approximately

65 years. The information was gathered by visits to every one of the fields for each

fragment. The information was gathered by visits to every one of the fields for each

fragment. The spatial circulation of the overviewed portions was dictated by

isolating the region in squares of 10 x10 km. Each square was sub-isolated in 100

cells of 1x km2. In each square, three cells were haphazardly chosen for reviewing.

31

In every cell, cropped area, generation of each yield per unit zone, water system

conspires, and so forth of rural regions show in the portion of 700 x 700 was

measured.

Figure ‎3-2: District Map of Punjab Along with Weather Stations

Secondly, interpolation of monthly averages of temperature and rainfall was

done from 2000-2014.

Interpolation of temperature and rainfall was performed in Geographic

Information System environment. Inverse Distance Weighting (IDW) and kriging

interpolation techniques were used to predict the climatic values for the

unmeasured areas throughout the study area. While executing interpolation, ninety

percent of the values were used whereas remaining ten percent were used as

validation data set. The predicted values for the corresponding validation data set

were obtained by overlay function in ArcGIS and scatter plots were made to check

the accuracy of the interpolation results for validation purpose. Giving

climatological and meteorological data things covering the whole country as maps

or gridded datasets is a basic task. To register these maps, the view of

meteorological stations ought to be included. Late research on presentation of

climatological and meteorological information with the assistance of GIS has

shown that introduction has an immense progression potential inside climatology

and meteorology. Meanwhile the enthusiasm for gridded (added) data things is

extending: numerical atmosphere models are working at higher spatial resolutions

32

and may be begun by gridded data from recognitions, or may give "first figure

handle" that may be used as a piece of the inclusion methodology. Picking the right

method in like manner consolidates the decision of arrange systems and cell

measure. The mastermind system ideally should arrange the structure that is being

utilized and the choice of an encourage system ends up being less basic in light of

the fact that the proposed transport structures in perspective of OGC organizations

support changes between critical sort out structures and projections.

3.3 CROP MAPPING AND MONITORING

Classification of multi-temporal MODIS NDVI images was used to identify

specific land uses to map various crops grown in the study area. Unsupervised and

supervised classifications are two major classification techniques. In the present

study unsupervised classification technique was used because it fulfills the

objectives of the study as it not only produces classified image but also their

temporal signature. Temporal signature of the NDVI depicts temporal behavior of

the crops. Since long term images were used, therefore, unsupervised classification

technique helped in knowing the temporal behavior of the crops. This was then

linked with changing climate (temperature and rainfall).

3.3.1 ISODATA Algorithm

Iterative Self-Organizing Data Analysis computation enables the amount of

bundles to change from the one cluster to the accompanying, by uniting parts and

deleting gatherings. ISODATA recognizes from the user the number of classes to

be found the data. The figuring by then subjectively finds that number of gathering

centers in the multidimensional estimation space. Regardless, in every accentuation

then the trading of pixels to the clusters, the figures depicting each gathering is

surveyed. If the space among the mean motivations behind two clusters isn't as

much as some predefined humblest partition, the two gatherings are consolidated.

On the other hand, if a singular cluster has standard deviation in any estimation that

is more critical than the predefined most essential regard, the gathering is isolated

in to two. Groups with not as much as the particular most unobtrusive number of

33

pixels are cleared. Groups with not as much as the particular most unobtrusive

number of pixels are cleared.

Figure 3-3: General Crop Calendar for field crops of Pakistan

Figure 3-4: General Crop Calendar for field crops in Districts of Punjab

Finally, all pixels are then re-arranged into the balanced course of action of

gatherings, and the change rehashes, until either there is no basic alteration in the

group experiences or some most outrageous number of emphases is come to. It

34

begins from discretionary clusters. In each progressive grouping, the methods for

clusters are moved. The ISODATA replicate the grouping of pixels in the image,

till either a most extreme number of set emphases has been done (50), or a greatest

scope threshold is achieved (0.98). Playing out an unsupervised characterization is

less difficult than supervised classification, because the clusters marks are naturally

created by the ISODATA calculation.

3.3.2 Selection of Optimal Classification Map of NDVI

Unsupervised classification technique was used to separate major land

covers from the 15 years stacked imagery. ISODATA clustering algorithm was

used for unsupervised classification and many test classes (5, 10, 15, 20, 25, 30,

35……… 90 and 95) were executed. After that an optimal run was identified by

using divergence statistics. The peculiarity of all the test classes was computed on

the basis of divergence, and the class which shows the maximum divergence

(maximum separablility among the classes of a classified image) was selected for

further processing as a result of output classified image.

3.3.3 Signature Output

For normalization, mean statistics was computed and mean NDVI values

were extracted. These NDVI values were plotted against time and the overall trend

of all classes was mapped and categorized into number of group on the biases of

their temporal behavior.

3.3.4 Linking Vegetation Behavior with Crop Calendar

Temporal behavior of all the NDVI Classes was then matched with the

practiced crop calendar in various districts of Punjab. This helped in linking the

output of 3-1 with 3-3 and 3-4 (crop calendar) to know what is grown where. Since,

the NDVI data was long term data so the analysis went one step ahead from “what”

is “grown” and “where” it is grown to “how it is performing” in the field. The

fundamental goal is to exploit all the more viably the time arrangement of NDVI,

connecting them however much as could reasonably be expected to edit developing

conditions, extracting markers which can be connected all the more intently to crop

35

yield exhibitions and fitting estimating models. All the more correctly, the last

target is to discover a marker worked from the NDVI profiles, which can be

identified with managerial yield insights. To accomplish these targets, the

vegetation marker must be re-aligned to a yield pointer. The utilization of an

autonomous land cover can build the data substance of the extracted NDVI profiles

such that the data is nearer to crop conduct than a basic blended vegetation profile.

3.3.5 Area of NDVI Classes per District

The NDVI-class (selected classification) raster map was converted in to a

vector map by preserving the polygon shapes NDVI classes. Further, it was

intersected with the administrative map of Punjab through overlay analysis and

area of each NDVI class was then computed by calculating geometry in ArcGIS.

3.3.6 Mapping Specific Crops

For mapping, area of each major crop (as dependent variable) was then

regressed with the NDVI-classes’ areas (as predictor) to estimate the coefficients

by which fraction each class of relevant NDVI predicts the area of each specific

crop. For this purpose, step-wise-linear regression with no constant and all the

positive but not exceeding 1 coefficient were computed and used for mapping of

specific crops grown in Punjab. The worldwide natural change look into group

requires enhanced and cutting-edge land use/land cover (LULC) datasets on local

to worldwide rank to help an assortment of discipline and strategy purposes.

Extensive steps include enhancing vast territory LULC dataset, yet slight

accentuation has been set on specifically itemized crop mapping, in spite of the

impressive impact of administration exercises in the cropland segment on different

ecological procedures and the financial system. Time-arrangement MODIS 250 m

Vegetation Index (VI) datasets grasp extensive guarantee in favor of substantial

territory trim mapping in an ergonomically concentrated locale. The arrangement

of MODIS NDVI-determined harvest maps for the most part had grouping

correctness more prominent than 80%. General exactness ran from 94% in favor of

the all-purpose yield guide to 84% intended for the late spring food crop outline.

36

The country yield designs arranged in the maps were predictable with the all-

purpose editing designs.

3.4 INFLUENCE OF CLIMATIC CHANGES ON CROP PERFORMANCE

3.4.1 Impact of Climatic Change on Crop Performance

Output of 3-3 (using data mentioned at 3.1.2 a) was used to identify those

districts of Punjab which showed conspicuous changes in climate. Further, it was

compared with the crop statistics data and crop calendar data to study the effect of

climate change on major crops of Punjab and shifts in cropping pattern.

Moreover, output of 3.2 (using data mentioned at 3.1.2 b) was compared with the

output of 3.3.3, 3.3.4 and 3.3.6 to classify the impact of change climate on crop

performance. Output of 3.3.3, 3.3.4 and 3.3.6 to identify the impact of change

Figure 3-5: Monthly Comparison of Average Rainfall January

37

Figure 3-6: Monthly Comparison of Average Rainfall February

Figure 3-7: Monthly Comparison of Average Rainfall March

38

Figure 3-8: Monthly Comparison of Average Rainfall April

Figure 3-9: Monthly Comparison of Average Rainfall May

39

Figure 3-10: Monthly Comparison of Average Rainfall June

Figure 3-11: Monthly Comparison of Average Rainfall July

40

Figure 3-12: Monthly Comparison of Average Rainfall August

Figure 3-13: Monthly Comparison of Average Rainfall September

41

Figure 3-14: Monthly Comparison of Average Rainfall October

Figure 3-15: Monthly Comparison of Average Rainfall November

42

Figure 3-16: Monthly Comparison of Average Rainfall December

Figure 3-17: Monthly Comparison of Average Temperature January

43

Figure 3-18: Monthly Comparison of Average Temperature February

Figure 3-19: Monthly Comparison of Average Temperature March

44

Figure 3-20: Monthly Comparison of Average Temperature April

Figure 3-21: Monthly Comparison of Average Temperature May

45

Figure 3-22: Monthly Comparison of Average Temperature June

Figure 3-23: Monthly Comparison of Average Temperature July

46

Figure 3-24: Monthly Comparison of Average Temperature August

Figure 3-25: Monthly Comparison of Average Temperature September

47

Figure 3-26: Monthly Comparison of Average Temperature October

Figure 3-27: Monthly Comparison of Average Temperature November

48

Figure 3-28: Monthly Comparison of Average Temperature December

49

CHAPTER 4

RESULTS AND DISCUSSION

4.1 CLIMATE CHANGE IN PUNJAB

Figures 4-1 to 4-6 show the climate change between the two normal time

spans i.e. 1951 to 1980 and 1980 to 2015. Figures 4-5 and 4-6 show the change in

rainfall and temperature respectively over various districts of Punjab.

Figure 4-1 shows the Average Rainfall in the period of first climate normal

(1951-80) showed that Rawalpindi (1073.686 mm), Attock (862.293 mm), Jhelum

(829.066 mm) and Gujrat (808.808 mm) were the wettest districts of Punjab.

Whereas, Rahimyar Khan (137.674 mm), Rajanpur (197.092 mm), Lodhran

(205.276 mm) and Bahawalpur (227.270 mm) were the driest districts of Punjab.

Average Rainfall in the period of second climate normal (1980-15) showed that

Rawalpindi (1024.626 mm), Lahore (960.177 mm), Gujrat (870.646 mm) and

Sheikhupura (827.209 mm) were the wettest districts of Punjab. Whereas,

Rahimyar Khan (125.099 mm), Rajanpur (171.937 mm), Lodhran (187.092 mm)

and Bahwalpur (190.781 mm) were the driest districts of Punjab.

Figure 4-3 shows the Average temperature in the period of first climate normal

(1951-80) showed that Lodhran (25.810 0C), Bahwalpur (25.437

0C), Multan

(25.386 0C) and Rahim Yar Khan (25.148

0C) were the hottest districts of Punjab.

Whereas, Rawalpindi (19.684 0C), Attock (21.780

0C), Jehlum (22.653

0C) and

Gujrat (22.934 0C) were the coldest districts of Punjab.

Figure 4-4 shows the Average temperature in the period of second climate

normal (1980-15) showed that Rahim Yar Khan (26.419 0C), Lodhran (26.199

0C),

Rajanpur (26.107 0C) and Bahwalpur (26.013

0C) were the hottest districts of

Punjab. Whereas, Rawalpindi (20.209 0C), Attock (21.754

0C), Sialkot (22.322

0C)

and Jehlum (22.454 0C) were the coldest districts of Punjab.

50

District

Rainfall

(mm)

Rawalpindi 1074

Attock 862

Jhelum 829

Gujrat 809

Rahim Yar

Khan 138

Rajanpur 197

Lodhran 205

Bahawalpur 227

Figure 4-1: Average Rainfall (mm) 1951 – 1980 map of Punjab

Figure 4-2: Average Rainfall (mm) 1981 – 2015 map of Punjab

51

Figure 4-3: Average Temperature Map (0C) 1951 – 1980 map of Punjab

District

Temp (0C)

Decrease

Rahim Yar

Khan 26.42

Lodhran 26.2

Rajanpur 26.11

Bahawalpur 26.01

District

Temp (0C)

Increase

Rawalpindi 20.21

Attock 21.75

Sialkot 22.32

Jhelum 22.45

Figure 4-4: Average Temperature Map (0C) 1981 – 2015 map of Punjab

52

Figure 4-5 shows the Maximum increase in rainfall was observed in Lahore

(359.637 mm), Sheikhupura (178.647 mm), Kasur (132.967 mm) and Gujrat

(61.838 mm). Whereas, Sargodha (316.185 mm), Chiniot (230.778 mm),

Pakpattan (225.682 mm) and Bahwalnagar (205.086 mm) showed maximum

decrease in rainfall.

Figure 4-6 shows the Maximum increase in temperature was observed in

Rahim Yar Khan (1.271 0C), Rajanpur (0.987

0C), Faisalabad (0.600

0C), and

D.G.Khan (0.582 0C). Whereas, maximum decrease in temperature was observed

in Sialkot (0.8710C), Narowal (0.616

0C), Mianwali (0.610

0C), and Gujrat (0.477

0C).

4.2 IMAGE CLASSIFICATION

Unsupervised classification technique was used to separate major land

covers from the 15 years stacked imagery. ISODATA clustering algorithm was

used for unsupervised classification and many test classes (5, 10, 15, 20, 25, 30,

35……… 90, 95,100, 105 and 110) were executed. Threshold was set to 0.98 and

number of iterations was set to 50 in each class. The divergence showed clear peak

for the classification run with 90 classes (Figure 4-7). Divergence is a measure of

separability of classes present in the classes of a classified image and the run with

maximum divergence shows maximum separabiltiy. Therefore, 90 class images

and its signature was selected and it showed that Punjab can be classified into 90

different mix of land cover /land uses from 2000-14 at 250 m resolution (Figure 4-

8). This image was used for further analyses to map and monitor crop producing

areas of Punjab. As mentioned earlier, there are two outputs of ISODATA

clustering algorithm, out of which one is the classified image and second is the

signature which is temporal behavior of each NDVI class. All 90 Signatures were

then plotted and grouped into similar behaving NDVI class signatures (also known

as profiles). Figure 4-9 to Figure 4-24 show, 15 different and distinct groups of

NDVI classes. Figure 4-9 to figure 4-24 show the grouping of similar behaving

NDVI classes in to 16 distinct groups.

53

Figure 4-5: Rainfall Change (mm) 1951 – 2015 map of Punjab

Figure 4-6: Temperature Change (0C) 1951 – 2015 map of Punjab

54

Figure 4-7: Average divergence of classification runs

Figure 4-8: Map of NDVI classes representing a (mix) land cover/land use data

55

Figure ‎4-9: Grouping of NDVI classes representing Group-1



56

Figure 4-12: Grouping of NDVI classes representing Group-4



57




58




59




60


4.3 LINKING VEGETATION BEHAVIOR WITH CROP CALENDAR

Mean statistics of 90 class signature was computed and mean NDVI values

were extracted. These NDVI values were plotted against time and the overall trend

of all classes was analyzed. Since the NDVI is an indicator of greenness, therefore,

temporal behavior of each NDVI class exhibited the behavior of a particular

vegetation or mixture of vegetation. Temporal behavior of all the NDVI Classes

was then matched with the practiced crop calendar in various districts of Punjab.

Figure 4-11 shows the average behavior of an NDVI class which follows a specific

crop calendar. Figure 4-12 show that this particular NDVI class presents gram crop

calendar.

4.4 MAPPING SPECIFIC CROPS

Specific crop maps were generated by using NDVI CLASS Areas as predictors

and crop area as dependent variable. Separate statistical analyses were executed to

map various crops.

4.4.1 Wheat Area Map of Punjab

Table 4-1 shows the coefficients of all the related NDVI classes with wheat and

theses coefficients were used to generate wheat map of Punjab (Figure 4-25).

61

Figure ‎4-25: Wheat area map of Punjab

Table ‎4-1: Stepwise Linear Regression results to map wheat

Model R R Squareb Adjusted R Square

Std. Error of the Estimate

.978g .956 .948 111.220

Predictors of Wheat Area

Unstandardized Coefficients

Standardized Coefficients

Sig.

B Std. Error Beta

VAR00092 .001 .002 .125 .000

VAR00019 .002 .000 .516 .000

VAR00042 .001 .000 .237 .002

VAR00044 .003 .000 .263 .000

VAR00059 .002 .001 .145 .002

VAR00021 .002 .001 .263 .001

62

4.4.2 Maize Area Map of Punjab

Table 4-2 shows the coefficients of all the related NDVI classes with maize

and theses coefficients were used to generate rice area map of Punjab (Figure 4-

26).

Table ‎4-2: Stepwise Linear Regression results to map maize

Model R R Squareb Adjusted R Square


.828c .702 .675 36.220

Predictors of Maize Area


Standardized Coefficients Sig.

B Std. Error Beta VAR00050 .001 .002 .126 .000

VAR00019 .032 .000 .731 .000

VAR00076 .010 .000 .297 .002

VAR00078 .001 .000 .246 .000

Figure ‎4-26: Maize area map of Punjab

63

Figure 4-27: Sugarcane area map of Punjab

Figure 4-28: Cotton area map of Punjab

64

Figure ‎4-29: Gram area map of Punjab

4.4.3 Sugarcane Area Map of Punjab

Table 4-3 shows the coefficients of all the related NDVI classes with

sugarcane and theses coefficients were used to generate sugarcane crop area map

of Punjab (Figure 4-15).

Table ‎4-3: Stepwise Linear Regression results to map sugarcane

Model R R Squareb

Adjusted R Square


3 .925d .856 .843 32.108

Unstandardized Coefficients Standardized Coefficients Sig.

B Std. Error Beta

VAR00050 .000 .000 .253 .000

VAR00012 .000 .000 .450 .001

VAR00076 1.769 .553 .561 .003

65

4.4.4 Cotton Area Map of Punjab

Table 4-4 shows the coefficients of all the related NDVI classes with wheat

and theses coefficients were used to generate cotton map of Punjab (Figure 4-16).

Table ‎4-4: Stepwise Linear Regression results to map cotton

Model R R Squareb

Adjusted R Square

Std. Error of

the Estimate

3 .860c .740 .724 99.918

Unstandardized Coefficients Standardized Coefficients Sig.

B Std. Error Beta

VAR00019 .001 .000 .119 .000

VAR00019 .001 .000 .283 .000

VAR00072 .312 .129 .756 .001

4.4.5 Gram Area Map of Punjab

Table 4-5 shows the coefficients of all the related NDVI classes with wheat

and theses coefficients were used to generate wheat map of Punjab (Figure 4-17).

Table ‎4-5: Stepwise Linear Regression results to map gram

Model R R Squareb

Adjusted R Square


2 .995c .991 .990 20.788


Standardized Coefficients

Sig.

B Std. Error Beta

VAR00024 .003 .000 .821 .000 VAR00062 .001 .000 .200 .000

66

4.4.6 Validation-Remote Sensing vs Reported Data of Wheat Crop

The regression analysis of wheat crop shows that the validation of wheat crop

is 91% (Figure 4- 30). These regressions were done in SPSS software. The results

show that remote sensing results were very accurate with respect to the survey data

those were collected from agriculture directorate of Punjab of same time series as

acquired date of satellite imagery.

4.4.7 Validation – Remote Sensing vs Reported Data- of Maize Crop

The regression analysis demonstrates that the validation of Maize crop is 96%

(Figure 4- 33).Data collected from survey and through satellite based data. If we

compare of both data remote sensing results were very accurate with respect to the

survey data. The outcomes demonstrates that remote sensing comes about were

exceptionally precise regarding the survey data those were gathered from

directorate of Punjab of same time arrangement as obtained date of satellite

symbolism.

4.4.8 Validation – Remote Sensing vs Reported Data- of Cotton Crop

The regression analysis of cotton crop demonstrates that the validation of

cotton crop is 93% (Figure 4- 31). Through SPSS software these regression are

calculated. The regression data was calculated from survey and through satellite

based data. The result was exceptionally precise regarding the survey data those

were gathered from directorate of Punjab of same time arrangement as obtained

date of satellite imagery.

4.4.9 Validation – Remote Sensing vs Reported Data- of Sugarcane Crop

The above figure and scatter plot shows regression analysis that shows the

validation of Sugarcane crop which marks to 95% ((Figure 4- 32). The regression

information was figured from review and through satellite based information. Due to

high resolution Satellite imagery data used for the regression analysis. The outcomes

demonstrates that remote sensing comes about were exceptionally exact as for the

67

review information those were gathered from agriculture directorate of Punjab of same

time arrangement as gained date of satellite imagery.

4.4.10 Validation – Remote Sensing vs Reported Data- of Gram Crop

The regression analysis of Gram crops shows that the validation of Gram Crop is

95 % of both remote sensing based results and with survey data (Figure 4- 34). The

outcomes demonstrates that remote detecting comes about were exceptionally exact as

for the study information.

4.4.11 Impact of the Rainfall on Wheat Vegetation

The regression analysis shows that impact of the rainfall on vegetative period of

wheat is 27%. So rainfall factor is very important for production of wheat. According

to topography of the research area upper Punjab has totally dependent on rainfall

factor.

4.4.12 Impact of the Rainfall on Wheat Vegetation

The regression analysis shows that impact of the temperature on wheat

vegetative is 21%. So temperature factor is very important for production and

maturity of wheat. Temperature impact is very significant during month of the

March and April for maturity level of the crops.

4.4.13 Impact of Climate Change on Crop Performance

District Bahawalnagar is one of most productive district for wheat growing

crop. The above results of wheat NDVI with respect to the climatic factors of

Rainfall and temperature then analyzed with crop production. according to the

results of Bahawalnagar wheat production comparatively better with respect to

rainfall and relation between rainfall and wheat crop behavior is 34% and other

factor of climate ; temperature is 0.023%.

Dist Jhang comparison graph shows that increasing trend of the rainfall has

very strong impact on the wheat production of the wheat crop. If we look at area

it’s almost constant but due to increasing and decreasing trend of rainfall

68

production of wheat crop is increased due to increase in rainfall and production is

decreased if rainfall trend is decreased.

Dist. Attock is rainfed area and majority of the crops depends on the impact

of the rainfall. In this district production and crop behavior is directly proportional

to the rainfall even from showing season to maturity of the crops. Also in this

region March and April temperature has very keen due to maturity level of the

crops.

Afall. In this area production and crop behavior is directly proportional to the

Figure 4-30: Validation – Remote Sensing vs Reported data

69

Figure 4-31: Validation – Remote Sensing vs Reported Data

Figure ‎4-32: Validation – Remote Sensing vs Reported Data

70



71

Figure 4-35: Impact of Rainfall on wheat Vegetation

Increase in temperature causes decline in NDVI values as shown in above figure 4-

35. Whereas, increase in rainfall produces positive effect on wheat vegetation

(Figure 4-36).

Figure 4-36: Impact of Temperature on wheat Vegetation

72

Figure 4-37: Impact of Climate Change on Crop Performance

Figure 4- 37, shows relationship of rainfall wheat production and wheat area

from year 2000 to 2014. The important fact is that despite increase in the year

2009-10, the production of wheat decline because of conspicuously less rainfall.

Moreover, Figure 4- 38 also supports the same fact where rainfall is overlayed

with NDVI of wheat. Those years where increase in rainfall was evident,a

positive increment in NDVI is observed.

73


Figure 4-39: Impact of Rainfall on Crop Performance in Bahawalnagar

Figure 4- 40, shows relationship of rainfall wheat production and wheat area from

year 2000 to 2014. The important fact is that despite increase in the year 2009-10,

the production of wheat declined but not too much because of conspicuously less

rainfall what met with extra irrigation in Bahawalnagar area. Moreover, Figure 4-

39 also supports the same fact where rainfall is overlayed with NDVI of wheat.

Those years where increase in rainfall was evident a positive increment in NDVI is

observed.

74

Figure 4-40: Impact of Temperature on Crop Performance in Bahawalnagar


75

Figure 4-42: Impact of Climate Change on Crop Performance in Jhang

Since Attock and Chakwal are rainfed district, rainfall and wheat production is

exactly following the same trend while temperature has adverse effect on

wheat production as shown by the figures 4.43 to 4-46.


76


Figure 4-45: Impact of Climate Change on Crop Performance in Chakwal

77

Figure 4-46: Impact of Climate Change on Crop Performance in Chakwal

Impact of various climatic variable on crop production was explored through

regression analyses using their production as dependent variable along with

monthly values of rainfall and temperature as predictors. Table 4.5 shows

temperature increase in the temperature of the months of July and August has

negative impact on cotton yield. The reason being cotton lint is at mature stage and

a small increase in temperature during that period effects its production. While,

increase in rain in the month of September causes increase in production of maize.

Table also shows that increase in rainfall during September and august causes

increase in sugarcane production as sugarcane is a water demanding crop. Increase

in temperature of September has a positive impact of sugarcane production.

78

Table 4-6: Showing Impact of climate variables on Kharif crop production

Regression Statistics Coefficients P-value

R square 0.428 Intercept 9183.309 0.007

Temp July -199.159 0.014

Cotton Temp Aug -183.409 0.031

Rain Kharif -5.864 0.042

R square 0.43 Intercept 812.184 0

Maize Temp May -6.058 0

Rain Sep 0.406 0.025

Rain Kharif -0.347 0.053

R square 0.366 Intercept 1317.704 0.003

Sugarcane Temp Sep 26.128 0.008

Rain Aug 0.593 0.013

Rain Sep 0.604 0.004

Rain kharif -0.42 0.021

Wheat R square 0.505 Intercept 28.672 0.001

Rain Jan -0.034 0.004

Rain Feb -0.024 0.003

Temp Oct 1.674 0

Temp Nov 0.663 0.041

Temp Dec -1.006 0

79

Figure ‎4-47: Comparison of area and production of Gram crops

Long term monitoring of all the crops grown with respect to changing climatic

conditions is an added advantage of the methodology adopted in this research.

For example, Figure 4-47 shows the temporal behavior of gram profile (blue

line) which is extracted using MODIS NDVI data and it is plotted against area

(blue digits) and production of gram (black digits). In cropping season of 2004-5

1076000 acres of gram were grown and their production was 342000 tones. In

2005-06 season 1007000 acres of gram were grown and their production was

168000 tones. Only 6 % reduction of area is observed from 2004-05 and 2005-06

but the production was 51% reduced and it is clearly captured by the remote

sensing image analysis. Hence, methodology adopted by this research can be

successfully used to timely monitor the changes in crops grown in response to

changing weather/climatic conditions.

80

CONCLUSIONS AND RECOMMENDATIONS

Apportioning Punjab into three equal portions (upper, middle and lower portion)

, it is evident that maximum increase in temperature occurred in the lower

portion i.e. south Punjab (Rahim Yar Khan, Rajanpur and DG Khan)

To some extent in the in middle portion i.e. Faisalabad, increase in temperature

is observed

Maximum decrease in temperature occurred only in upper portion of Punjab

While considering the Rainfall, maximum increase occurred in middle eastern

portion and upper portion i.e. Lahore, Sheikhupura, Kasur and Gujrat; whereas

maximum decrease occurred in middle western and lower portions i.e

Sargodha, Chinot, Pakpattan and Bahawalnagar

NDVI values when compared with the Crop Calendar, gives a very clear match

and using multi-temporal / multispectral data set, vegetation change can very

easily be detected

Multi-temporal continuous satellite images is now available to be used for

long-term monitoring of crop producing areas

Currently, crop areas are being compiled by statistical up scaling and the

validation results show that NDVI images can be used as a stratum while

producing crop area estimates

The study focused on the effect of changes in climatic indicators on major

crops production in Punjab

The Results shows that increase in temperature is expected to affect the

crops production as significant negative correlation is found between

temperature and crop production

For cotton a significant negative impact of rainfall is reported by the results

of this study

81

For cotton significant adverse impact of temperature increase in the months

of July and August is shown

For maize a significant negative impact of increasing temperature in the

month of May; whereas rain in the month of Sep positively contributed

For wheat crop, Nov temperature has positive impact whereas Dec

temperature has negative impact. Rainfall in the month of Feb has

negligible adverse impact on wheat production

In Potohar region rainfall has significant positive impacts on crop

production

In Potohar region temperature has significant negative impacts on crop

production

This research has a major contribution in providing a monitoring framework

for researchers and policy makers, both at spatial and temporal scale

Location specific monitoring provides opportunity to evaluate performance

of various crops at various locations; whereas long term monitoring helps

in analyzing the impact of variations in climate.

82

SUMMARY

This study ponders the effect of changes in rainfall and temperature of Punjab,

Pakistan on the production of five major crops viz. wheat (triticum), maize (zea

mayes), cotton (gossypium), sugarcane (Saccharum officinarum) and gram (Cicer

arietinum). Firstly, changes in mean temperature and rainfall from 1980 to 2015

were assessed at district level in comparison with the data of 1951 to 1980.

Secondly, the changes in crop performance were computed through remote sensing

and crop reporting data obtained from the agriculture department, Government of

Punjab, Pakistan. Lastly, changes in crop performance were compared with

changes in studied climate variables (temperature and rainfall). It was found that

increased in mean annual temperature decreased the crop yield. In addition, the

climatic variation imparts negatively on studied crop production in the Central

Punjab (i.e. Jhang) and southern Punjab (i.e. Bahawalnagar) parts and a positive

effect in the northern Punjab (i.e. Attock and Chakwal). It is therefore, suggested

that various agriculture departments be educated regarding outcomes / conclusions

of this study so that formers be trained to take necessary precautions while looking

after their crops at different stages of crop cycle regarding these climatic variables

i.e. temperature and rainfall. This would entail better crop monitoring, requirement

of water at every stage of crop cycle, accurate estimation of yield and better

adaption / mitigation strategies for the future.

83

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