eChapters

Table 1: Descriptive statistics of the socio-economic and communication characteristics of the respondents in terms of Range (Minimum and Maximum), Mean, Standard Deviation (SD) and Coefficient of Variation (CV) of Alluvial Zone in West Bengal

Table 1 Represents the distribution of variables in terms of range i.e. maximum and minimum values, mean, standard deviation and coefficient of variation for alluvial zone of West Bengal.

It has been found that for the independent variable age (x₁), the range lies between 82 and 27 years, where the oldest respondent is of 82 years and the youngest respondent being 27years old. The mean value of the age of the respondents in this study was found to be 51.49 years with a standard deviation of 12.42 in the dataset. Coefficient of variation for this variable was found to be 24.14 per cent, which indicates a high level of consistency in the distribution.

There is widespread belief now that forests can be used to reduce the costs of slowing climate change. While the role of forests in the global carbon cycle has long been acknowledged, recent discussions within the context of the United Nations Framework Convention on Climate Change, as well as efforts to write climate change legislation in the United States, have emphasized the role forests might play. The most recent policy efforts have focused on near-term actions to reduce deforestation in tropical countries. The rationale for considering forests at all in the policy mix stems partly from the physical components of the issue. The world’s forest estate is exceedingly large:

3.1    Air Pollution
    3.1.1 Classification of Air Pollutants
    3.1.2  Properties of Air Pollutants
    3.1.3  Health Effects
    3.1.4 Particulate Pollution Control Equipments
    3.1.5 Gaseous Pollution Control Equipments
    
3.2    Water Pollution
    3.2.1 Causes of Pollution
    3.2.2 Origin of Wastewater
    3.2.3 Waste Water Composition
    3.2.4 Types of Water Pollutants
    3.2.5 Basic Processes of Water Treatment
    
3.3    Land
    
3.4    Marine Pollution
    3.4.1 Sources and Effects of Marine Pollution Toxics
    3.4.2 Control Measures
    
3.5    Noise
    3.5.1 Effects of Noise
    3.5.2 Major Noise Sources
    3.5.3 Noise Control Measures
    
3.6    Thermal Pollution
    3.6.1 Effects of Thermal Pollution
    
3.7     Nuclear Hazards
    3.7.1 Nuclear Weapons
    3.7.2 Types of Nuclear Bursts
    3.7.3 Nuclear Injuries
    3.7.4 Residual Radiation
    
3.8    Nuclear Waste and their Treatment
    
3.9    Solid Waste Management
    3.9.1 Role of Human in Prevention of Pollution
    
3.10    Disaster Management
    3.10.1 Floods
    3.10.2 Earthquake
    3.10.3 Cyclone
    3.10.4 Landslides

3.1 Air Pollution

Air pollution is a phenomenon by which particles either a solid or liquid and gases contaminate the environment. Such contamination can result in health effects on the population, which might be either chronic arising especially from long-term exposure or acute due to accidents. Other effects of pollution may be damage to materials, agricultural damage namely reduced crop yields and tree growth, impairment of visibility namely tiny particles scatter light very efficiently, and even climate change global warming.

3.1.1 Classification of Air Pollutants

Air Pollutants are classified as Natural as well as Anthropogenic. Natural pollutants are those that are found in nature or are emitted from natural sources. For example, volcanic activity produces sulfur dioxide, and particulate pollution may derive from forest fires or windblown dust. Anthropogenic pollutants are those that are produced by humans or controlled processes. For example, sulfur dioxide is produced by fossil fuel combustion and particulate matter comes from diesel engines.

Air pollutants are further classified as primary or secondary. Primary pollutants are those that are emitted directly into the atmosphere from an identifiable source. Examples include carbon monoxide and sulfur dioxide. Secondary pollutants are those that are produced in the atmosphere by chemical and physical processes from primary pollutants and natural constituents. For example, ozone is produced by hydrocarbons and oxides of nitrogen (both of which may be produced by car emissions) and sunlight.

6.1. Data Interpretation and Visualization of West Bengal

Any kind of production management system or decision-making process is being organised in social ecology. No variable cannot behave discrete or free without having any kind of influence as well as impact from the vary operating ecosystem. That is how it is imperative to know and visualize the basic features in terms of its distributive characters. The distribution of features in variables through this networking and interactions ultimately contribute to a holistic visualization of social ecologization under study.

Table 6.1.1 represents the distribution of variables in terms of range i.e. maximum and minimum values, mean, standard deviation and coefficient of variation for Terai Zone, Old Alluvial Zone and New Alluvial Zone of West Bengal.

The average age (x1) of the respondent stands at 49.02. It denotes that so far as responsibility of agriculture and age base livelihood runs at the population so far as the respondent under study is peaks are at the hands of farmers approaching or passing through old ages. On the contrary the absence of young population or withdrawal of young age farmers from agriculture have got a serious implecation near and far. The cv value runs at 25.78 per cent to indicate that the distribution of variable is highly consistant.

The average of functional education (x2) of the respondent stands at 8.20.The variable education charecterization the social ecology speaks that the most of the respondents are having mid level school education.So, this has got spil over effect on the function and operation of other variables i.e. precisely the perception variables.The cv value stands at 43.69 per cent that’s good enough to infer that the distribution of the variable is highly consistent.

Good Practices for Drought Management
    7.1    Drought Planning and Preparedness
    7.1.1    Drought Planning in India
    7.2    Global Experiences in Drought Planning
    7.2.1    USA
    7.2.2    South Africa
    7.2.3    African Nations
    7.2.4    Namibia
    7.2.5    Ethiopia
    7.2.6    Botswana
Drought management plans in Europe
    7.2.7    Spain
    7.2.8    England and Wales
    7.2.9    Portugal
    7.2.10    Australia
    7.2.11    China
    7.2.12    Palestine
    7.2.13    Global Initiatives
    7.3    Drought Preparedness Action Plans in India
    7.3.1    Drought Preparedness Checklist
    7.4    Drought Preparedness Action Plans in Different Countries
    7.4.1    Arizona State in USA
    7.4.2    Texas State in USA
    7.4.3    Italy
    7.4.4    Mediterranean Region
    7.4.5    Pakistan
    7.4.6    Darfur in Sudan
    7.4.7    Bangladesh
    7.5    Indigenous Knowledge for Drought Management
    7.5.1    Astro Meteorological Methods
    7.5.2    Traditional Knowledge in Weather Prediction
     7.5.3    Traditional wisdom in Drought Risk Region (DRR)
        Rajasthan and Bundelkhand region in Uttar Pradesh
    7.5.4    Indigenous technologies for moisture conservation 
        in dry lands of semi-arid regions of India
    7.6    Traditional Wisdom for Drought Management in a few selected countries
    7.7    Role of Weather-based Agro-advisories
    7.7.1    Strategies for dissemination of advisories
    7.7.2    Role of ICAR and AICRPAM on Agromet Services
    7.7.3    Agromet Advisory services through mobile phone
    7.7.4    Status of Agromet Advisory Services in other Countries
    7.8    Value Addition to Agromet Information
    7.8.1    AICRP on Agrometeorology
    7.8.2    Thumb rules for weather-pest/diseases
    7.9    Contingency Crop Planning for Minimizing Risks
    7.9.1    Contingency Crop Planning for Rainfed Regions of India
    7.10    Drought Proofing Mechanisms
    7.10.1    Watershed Development for Drought Management
    7.10.2    Watershed Development in India
    7.10.3    The Success Stories of Watershed as a Drought Proofing Mechanism
    7.10.4    Watershed Policies and Recommendations
    7.10.5    Farm Ponds for Drought Management
    7.10.6    Success Stories of Farm Ponds
    7.11    Rainfed Technologies for the Drought Management in different agro climatic region
    7.12    Farmer’s Awareness on Climate Management
    7.12.1    India
    7.12.2    In other Countries
    7.13    Role of ICT in Climate Risk Management through AAS
    7.13.1    India
    7.13.2    Application of ICT in Providing Advisories in other Countries
    7.14    Integrated Drought Management Strategies through Farming Systems Approach
    7.14.1    Drought Adaptation Strategies in Rajasthan and in Maharashtra
    7.14.2    Improvement and Rural Livelihood
    7.15    Drought Mitigation Measures
    7.15.1    Drought Mitigation Plans
    7.15.2    Soil water Conservation and Recharging of Ground water Methods
    7.15.3    Traditional Water Harvesting and Conservation 
        Techniques
        References

First Part: Multivariate Statistical Analysis
6.1. Results
The chapter deals with the result and discussion of the study using multivariate statistical analysis and participatory Rural Appraisal (PRA) techniques with the purpose to provide sufficient information so as to arrive at valid conclusion. Tables and models are commonly used in this chapter to support and supplement the textual materials. Discussion helps to interpret the results of the study in proper perspective and to relate them with other relevant studies.
Axiomatic statement on the variable interactions and behaviour selected for the study
Research setting in the present study presents a dynamic social-ecology which is evolving along and across the undulation of social process. Both the structural and functional dimensions and behaviour are in constant interaction and response with the subject respondents in terms of innumerable variables. For the present research only some selected characters have been selected for empirical titration. There is no wonder these variables can be catalogued in terms of dependent or independent variables rather should not be distributed in a binary mode. Rather these variables are with seamless transformations like the algorithm of quantum physics. That is how whenever a set of x variables are supposed to behave with a contributor characters the same can be responsive with a recipient behaviour, e.g. in stepwise regression, set of ‘x variables’ are not considered as the only source of causal variables along with the couple of ‘y variables’ which are promoting rest other y variables have also been considered as causal variables. Hence, in case of y1, the rest of the y variables have to be treated like: y2 = y2' ; y3' = y3; y4 = y4' and so on.