eChapters

Livestock farming is an important activity to a large population of small and marginal farmers as well as landless agricultural labourers of Tamil Nadu. It also provides employment opportunities for most of the unemployed and underemployed in the rural areas. Tamil Nadu is bounded by the Eastern Ghats on the north, by the Nilgiris, the Anamalai Hills and Kerala on the west, by the Bay of Bengal in the east, and by the Indian Ocean on the south. Tamil Nadu is the eleventh largest state in India by area. The land area has been classified into seven agro-climatic zones based on soil characteristics, rainfall distribution, irrigation pattern, cropping pattern and other ecological and social characteristics. Tamil Nadu endowed with 13.97 per cent of recognised breeds of the domestic ruminants (cattle, buffalo, sheep and goats) with 22.55 millions of population (19^thLivestock Census-2012).

Introduction

Climate change represents a threat not only to the existence of individual species, but also to the genetic diversity hidden within them. The finding promises to complicate assessments of how climate change will affect biodiversity, as well as conservationists’ task in preserving it.

Climate change comes as an additional factor affecting a livestock sector that is already highly dynamic and facing many challenges. Important objectives of AnGR management include ensuring that AnGR are effectively deployed to meet these challenges (i.e. are well matched to the production environments in which they are kept) and that the genetic diversity needed to adapt production systems to future changes is maintained. Climate change is likely to create a number of problems in many areas of animal husbandry (housing, feeding, health care, etc.) and threaten the sustainability of many livestock production systems and their associated AnGR. At the same time, many of the specific challenges associated with climate change (high temperatures, disruptions to feed supplies, disease outbreaks, etc,) as well as the general unpredictability it brings to the future of the livestock sector, highlight the importance of retaining diverse genetic options for the future. The upper critical temperature of dairy cattle is lower than other livestock species (Wathes et al.,1983).

Introduction

Diversity connotes multiplicity of variety. In a given ecosystem, diversity refers to presence of number of different species of ?ora and fauna that have ecological niches in agreement with the environment. Scaling up, there exists diversity between different ecosystems. All life forms of ?ora and fauna on earth contains genetic material, that is the repository of an immense amount of genetic information in the form of traits, characteristics, etc. Focusing on the species level, genetic diversity refers to genetic variation that is observed in a population. Thus, genetic diversity is the total number of genetic characteristics in the genetic makeup of a species. This provides in built latitude to the nature and/or the livestock breeders (including farmers) to manoeuvre/ manipulate so as evolve an improved population of desired qualities and traits. Further, genetic diversity is essential because the presence of adequate and appropriate genes in a population will make the species tide over adversaries owing to climate change and other emerging threats like new disease outbreaks. The ‘desired’ divergent gene pool provides the inherent resilient mechanism that equip the species to cope with likely challenging situation.

Over the years, with human interventions, the genetic diversity has been interfered with to meet specific needs; mostly with respect to garnering higher production and productivity. In the process, the diversity has been compromised for achieving the unitary goal. In certain cases, geographical barriers and other bio-physical constraints have also led to loss of diversity within a population. Studies on impact of climate change especially the effect of rise in environmental temperature on milk production of dairy animals indicate that global warming will negatively impact milk production. During 2020, the annual loss in milk production of cattle and buffaloes due to thermal stress will be around 3.2 million tonnes of milk costing more than Rs 5000 Crore at current price. Similarly, there would be prevalence of many emerging diseases including those with zoonotic implications owing to change in climatic conditions. Thus, the present day society is confronted with an imminent development dilemma of ecology vs. economics more specifically genetic diversity vs. higher production. It has led to volume of debates and discussions on the livestock development agenda to be pursued. The apparently con?icting interests has also generated antipathy amongst two schools of development.

Introduction

Birds lack sweat glands; hence non-evaporative cooling mechanism (radiation, conduction and convection) is the major route of heat dissipation within thermoneutral range. Beyond this TNZ, majority of heat loss occurs as insensible heat loss i.e., Panting. Panting is one of the visible responses in poultry during exposure to heat stress associated with considerable energy expenditure. This specialized form of respiration dissipates heat by evaporative cooling at the surface of the mouth and respiratory passage. Panting increase, the loss of CO2 from the lungs, this leads to a reduction in the partial pressure of CO2 and also reduces bicarbonate levels in blood plasma. In turn, the lowered concentration of hydrogen ions leads to rise in plasma pH, a condition generally referred to alkalosis (Daghir, 2009). Birds also respond by reducing their feed intake and thus metabolizable energy (ME) intake to reduce thermo genesis as an adoptive mechanism. If panting fails to prevent their body temperatures from rising, the birds become listless, comatose and soon die due to respiratory, circulatory or electrolyte imbalances (Saif et al., 2003).

Introduction

The Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC, 2013) emphasized that warming of the climatic system is unequivocal and that anthropogenic warming will continue for centuries to come due to timescales associated with climate processes and feedbacks. While stresses caused by climate change are likely to negatively impact the agricultural and livestock sector and escalate existing small-scale farming challenges; agricultural and livestock productivity and sustainability have to improve to meet the needs of the growing human population (DEA, 2013; Zamykal and Everingham, 2009). Climate change is seen as a major threat to the survival of many species, ecosystems and the sustainability of poultry production systems in many parts of the world. Nevertheless, global demand for poultry products is expected to increase, as a result of the growing human population, and its growing af?uence. The extreme climatic conditions impose various stresses on poultry birds which adversely affect their growth, production and reproduction.

While climate change is a global phenomenon, its negative impacts are more severely felt by poor people in developing countries who rely mostly on the natural resource base for their livelihoods. Moreover, rural poor communities rely greatly for their survival on agriculture and poultry which belong to the most climate-sensitive economic sectors. Climate change is expected to intensify existing problems and create new combinations of risks and shift hazard zones. The incidences of draughts, snow-storms and blizzard like events have risen. The situation is made worst due to factors such as widespread poverty, inequitable land distribution, limited access to capital and technology inadequate infrastructure, long term weather forecasts and inadequate research and extension. Intensive poultry production has much less impact on global warming than organic or free-range production (Van der Sluis, 2007). Seo and Mendelsohn (2006) reported that small farms of poultry are better able to adapt to warming. However, organic egg production needs more energy than non-organic and increases most environmental burdens. The most disadvantageous, from environmental point of view, is litter-free breeding of birds which causes great amounts of liquid manure.

Introduction

The egg production in India is 75 billion and the broiler production is 4.2 million tonnes per annum. The growth rate of layer market is 6-7 percent per annum and broiler market is 8-10 percent per annum. The Indian poultry sector is valued at INR 1 lakh cr or USD 15.38 bn. Approx. 80 percent of egg production is contributed by commercial poultry farms, remaining comes from household/ backyard poultry. Backyard poultry plays a key role in supplementary income generation and family nutrition to the poorest of the poor. It is estimated that there are around 30 million farmers engaged in backyard poultry as per 19^th Livestock Census. Backyard poultry are being practiced for its inherent strengths. Free range birds reared by the farmers have the advantage of small body size, coloured plumage, broodiness to hatch chicks, adaptation to harsh climatic condition, lower diseases and production with least investment. The birds are scavenging in the backyard, supplemented by kitchen waste and are kept in low height houses for shelter using locally available materials. It helps to improve the nutritional status of rural poor and also improve the subsidiary income of the poor people. Such stocks make minimal use of land, labour and capital and integrate well into the backyard. Backyard poultry production system is self-sustaining because of minimal needs and can be afforded by poor people of the rural areas. The high yielding white feathered chicken varieties being reared in the intensive poultry farming are not suitable for backyard / free range backyard poultry farming due to the adverse climatic and high pathogen load prevailing in such rearing practices. Backyard poultry farming is an in-separable part of the poor tribal people and it will continue to be due to its advantages. Although backyard poultry production is not an occupation per se, it often supplements household income, primarily for poor families. Strengthening backyard poultry production may help overcome poverty and malnutrition in impoverished communities. However, the effect of climate change on these birds needs to be studied for its sustenance.

Introduction

There is an ongoing debate on global warming worldwide as it is seriously impacting climate change. The global temperature has been increased by 0.74±0.18°C during the last century and the climate model projections summarized in the Intergovernmental Panel on Climate Change (IPCC) report indicate that the global surface temperature will probably rise a further 1.1 to 6.4°C during the twenty-first century (IPCC, 2007). High ambient temperature is one of the most important stressors to poultry, which has a direct relationship on profitability of chicken meat and egg production. A seasonal problem in many parts of the world, high environmental temperature (35-43^oC in tropical countries) causes economic losses through reducing feed intake while decreasing nutrient utilization, live weight gain, egg production, egg quality and feed efficiency in poultry (Corzo et al., 2003; Panda et al., 2007a; 2008). The ideal temperature for broilers is 10-22^oC for optimum body weight and 15-27^oC for feed efficiency (NRC, 1994). Layers will produce egg constantly in the temperature range of 10-30^oC. Above 30^oC, performance in terms of growth and egg production declines.

Blood Sample collection

Blood samples (5 ml per goat) will be collected aseptically from the jugular vein in a vacutainer tube (B.D. Bioscience, Germany) containing EDTA as anticoagulant and transferred to the laboratory in an ice box.

Isolation of Genomic DNA

The Genomic DNA will be isolated from the blood samples by ‘Phenol: chloroform isolation’ method as described by Sambrook and Russel (2001). The steps for isolation of genomic DNA are as follows:

Introduction

Poultry production is one of the fastest growing sectors of livestock industry in developing countries. Environmental variation is one of the major factors that affect sustainability of poultry production systems in tropical climate (Sinha et al., 2017a). Adverse climatic stress declines the production performance of chicken which causes high economic loss to the enterprises as well as the farmers. Heat stress has an adverse effect on egg production, egg weight and shell quality of laying hen (Whitehead et al., 1998). Climatic variables like temperature, humidity, radiation and wind speed that directly affect the mechanism of thermoregulation and rates of heat exchange by all animals (NRC, 2001). Heat stress is a major factor that decreases productivity and reproductive efficiency of livestock due to lower feed intake and negative energy balance (De Rensis and Scaramuzzi, 2003). Moreover, it badly affect the production due to decline feed digestibility such as proteins, fats, starch (Bonnet et al., 1997). In addition, the acute heat stress drastically decreases the reproductive performance of hens due to alterations in acid-base balance and ion exchange mechanism (Mahmoud et al., 1996). Climate change in?uences the emergence of disease and their transmission due to increases vectors, pathogens. All the above effects impact a huge economic loss to the farmers as well as the country.

Introduction

Nanotechnology and nanoparticles are increasingly recognized for their potential applications in various aspects of human animal and animal welfare like development of various healthcare or cosmetic products, nano-electronics and techniques for environmental remediation, and many consumer products. Nanoparticles, by definition, are structures that have one dimension in the 1–100 nm range. Nanotechnology involves the application of materials at the nanoscale to new products or processes. Over the past few decades, inorganic nanoparticles, whose structures exhibit significantly novel and improved physical, chemical, and biological properties, phenomena and functionality due to their nanoscale size, have elicited much interest. Nanophasic and nanostructure materials are attracting a great deal of attention because of their potential for achieving specific processes and selectivity, especially in biological and pharmaceutical applications. Nanotechnology has made a new generation industrial revolution by developing product and formulations for medical, agriculture (nano-fertilizers, nano-herbicides, nano-pesticides, recalcitrant contaminants from water, nano-scale carriers, nanosensors), veterinary care, fisheries and aquaculture, detection of nutrient deficiencies, preservation, photocatalysis, nanobarcode, quantum dots etc. It is a rapidly growing industry currently worth billions of U.S. dollars, with many potential benefits to society. Nanotechnology has the environmental applications to protect climate change and environmental pollution as nanocatalyst, light weight nanocomposite materials, nanocoatings, improved renewables and nanosensors.

This fast growing technology is already having a significant commercial impact, which will certainly increase in the future. Because of their widespread application, the commercial nanotechnology industry is predicted to increase significantly to more $3 trillion in few years. While nanotechnologies offer many opportunities for innovation, the use of nanomaterials in food, agriculture and environment has also raised a number of safety, environmental, ethical, policy and regulatory issues.

Introduction

Aquatic ecosystems are critical components of the global environment and provide a variety of resources for human population, including food, water for drinking and irrigation, recreational opportunities, and habitat for economically important fisheries. Fish is the main source of animal protein for 3 billion people worldwide, providing a valuable protein complement to the food platter worldwide. Fish is also an important source of essential vitamins and fatty acids. Furthermore, it provides an important source of cash income for many poor households and holds a great potential as a source of foreign exchange. However, aquatic ecosystems are increasingly being threatened directly and indirectly by climate change. Global warming with consequent climate variability and extreme weather conditions has a great impact on the aquatic ecosystems resulting in increased temperature variation, ocean acidification and freshwater salinization etc. As per report of IPCC 2018, global warming of 1.5 ºC above pre-industrial levels has been declared (IPCC, 2018) and expected to rise further in future. Thus, variations in temperature, salinity, acidity and dissolved oxygen along with the effects of other anthropogenic activities may exert strong in?uences on aquatic ecosystems and the aquatic animls will be facing these climate-related stressors in near future. Such environmental stressors is projected to alter fundamental ecological processes, geographic distribution of aquatic species, reproductive and immunological performance of species etc.

Introduction

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. In a nutshell, R is an open-source software environment for statistical computing that is rapidly becoming the tool of choice for data analysis in the life sciences and elsewhere. It’s developed by a large international community of scientists and programmers and is at the forefront of new developments in statistical computing.

Introduction

Statistics generally refers to information about an activity or a process that is expressed in quantitate as well as qualitative traits. The quantitative research process is well structured whereas the qualitative one is fairly unstructured. It depends upon how a piece of information have been collected and analyzed. A good researchers need to have both types of skill. This article highlights about the operational procedure, about methods, procedures and techniques that are used in analysis of climate related data.

Parameter vs Statistic

When a data is collected with survey of all the individuals (like in a census) and analysed for certain values, it is called a parameter. But when, we guess the same value with a random sample and interpret for whole population it is called a statistic. Environmental parameters are: mean, variance, correlation, regression, heritability, repeatability etc. Again, there are two types of variables: quantitative and qualitative. Quantitative variables can take any value in a specified range. So, they are of continuous type. But qualitative variables are very few types ( 2 ,3 or 4 types) and they are of discrete type. Body weight is an example of quantitative variable whereas, comb type is a qualitative variable.

Introduction

In India, almost 85 % of our population resides in rural areas and the same proportion is dependent on agriculture for sustenance and animal husbandry for additional income. Distribution of livestock is more equitable compared to that of land and about 85 percent of livestock are owned by the landless, marginal and small landholding families. This sector provides employment for the farmers through livestock farming as well as in processing, value addition and marketing of livestock products.

Rapid population, economic growth, increased demand for livestock products such as meat and dairy products, in?uences the present livestock production systemtowards intensive production. There is increased con?ict over scarce resources such as land, water, food grains etc. Losing livestock assets for rural communities might lead to abject poverty with serious effect ontheir livelihoods. The climate change impact will worsen the vulnerability of livestock systems and those farmers depending on livestock, particularly the poor.

1.1. Soil Resource Evaluation

Soil is the essence of life on the planet Earth. It has sustained humanity and human civilizations through five functions (Karlen et al. 1997) viz., i) sustaining biological activity, diversity and productivity; ii) regulating and partitioning water and solute flow; iii) filtering, buffering, degrading, immobilizing and detoxifying organic and inorganic materials, including industrial and municipal byproducts and atmospheric decomposition; iv) storing and cycling nutrients and other elements within the earth’s biosphere; and v) providing support to socioeconomic structures and protection for archaeological treasures associated with human habitation. Reeling under the pressure of increasing food, fodder, feed, fibre and fuel production, soil has been used as a medium of plant growth with considerable reliance on external supply of major nutrients, irrigation water, plant protection chemicals, etc. Although soil, a product of millions of years of weathering, has supported various forms of terrestrial life, its sustainable management holds the key for meeting the basic requirements of burgeoning population and development commitments. So intense has been the pressure of increasing food, fodder, feed, fibre and fuel production that the soil has been exploited often exceeding its carrying capacity. The soils are increasingly used for agricultural and non-agricultural uses, such as disposal of farm, urban and industrial wastes, ensuring environmental safety etc. This is possible by the judicious use of this resource.

A clear and intimate knowledge of the kinds of soils and their extent of distribution are essential prerequisites in developing rational land use plans for agriculture, forestry, irrigation, drainage, etc. soil resource inventory or mapping provides an insight about their potentials and limitations. It is therefore imperative to prepare an inventory of this resource for developing optimum land use plan and conservation plans.

Soil survey is essentially a study and mapping of soils in their natural environment. It is the systematic examination, description, classification and mapping of soils of an area.

2.1. History of Soil Survey in India

The original investigations on Indian soils were carried out by Voelcker dates back to 1893 and by Leather to 1898.They classified the soils of the country into four major groups, namely the Indo-Gangetic alluvium, the black cotton soil or regur, red soil and laterite soil. Schokalskaya (1932) has published a soil map of India based on the Russian concept which describes 16 soil groups such as climate,vegetation, soil-forming materials, salinity, alkalinity, swamps and peats (Bhattacharyya et al. 2013). In 1935,Wadia and his co-workers collected a soil map of India with significance on geological formations and classified the soils as red, black (regur), laterite and lateritic soils of Peninsular India and also Indo-Gangetic Plains (IGP) which includes delta, desert, bhabar, terai and alkali soils. Vishwanath and Ukil (1943) published a soil map of India by placing the soils in different climatic zones for the first time. This was prepared by Imperial Agricultural Research Institute, New Delhi, and published in the year 1943. There were 17 kinds of soils identified, viz., deep black soils, black clay soils, black loamy soils, black sandy soils, red sandy soils, brown sandy soils, red and yellow soils, dark reddish brown soils, mixed soils (white and yellow), alluvium (Indus, Gangetic, Brahmaputra), coarse alluvium, soils of swamp lands, calcareous soils, usar soils, kallar soils, coastal alluvium soils and soils of Great Himalayan Ranges.

Soil survey works were started in 1954, in most of the states as a part of their agricultural development programme, and Bihar (Soil Survey and Land use Planning Scheme, Sabour) was pioneer. In 1954, the soil survey work was started under the leadership of Dr. P. P. Jha following the conventional procedures in Bihar (including Jharkhand) to delineate the soil associations based on geology, relief, physical and chemical characteristics of soils for the first time in India by the then Soil Survey Scheme at Sabour (Mishra et al. 2001; Mishra 2015, 2016). Meanwhile, the National Atlas Organization, Kolkata, prepared a soil map of India in 1957 classifying Indian soils into six major groups and 11 broad types. Murthy and Pandey (1983) applied the working principles of the USDA 7^th Approximation in classifying the Indian soils with available data on soil classes and thus used the US system for the first time at great soil group level, which was transformed into a soil map of India on 1:6.3 million scale. Mishra et al. (1994) attempted to propose “Fluvisols” as the 13th Order in USDA Soil Taxonomy for active floodplain soils frequently occurring in different parts of India and elsewhere. Mishra (2015, 2016) proposed a framework of Indian system of soil classification mainly based on land use options.

Soil Classification

Soil classification deals with the systematic categorization of soils based on distinguishing characteristics as well as criteria that dictate choices in use.

Soil classification is grouping of objects in orderly and logical manner into classes based on the properties of soils (differentiating characters) for the purpose of studying and identifying them. The soil individuals are grouped into classes of the lower category (soil series) which are grouped into classes of higher categories (soil orders). The lower categories are defined by large number of differentiating characters and higher category by a few differentiating characteristics.

For soil resources, experience has shown that a natural system approach to classification, i.e. grouping soils by their intrinsic property (soil morphology), behaviour, or genesis, results in classes that can be interpreted for many diverse uses. Differing concepts of pedogenesis and differences in the significance of morphological features to various land uses can affect the classification approach. Despite these differences, in a well-constructed system, classification criteria group similar concepts so that interpretations do not vary widely. This is in contrast to a technical system approach to soil classification, where soils are grouped according to their fitness for a specific use and their edaphic characteristics.

In recent times, the interpretations cover a wide spectrum of soil uses, both for agricultural and non agricultural (sewage and human waste disposal, high way construction, water and gas supply, recreation, wild life, urban and industrial structures) and many specialized uses. Soil survey if correctly carried out according to scientific principles, can provide the data base for various interpretations. This is because the same soil properties determine the behaviour of a soil for a variety of different uses. For instance, the content of clay, silt and sand, the shrink and swell behaviour, the permeability of soil to water and air, content of calcium carbonate, salts, organic matter, gypsum, soil depth, depth of ground water, hard pan formation, co-efficient of linear extensibility, water holding capacity etc. are examples of such properties that are important to plant growth and these properties also strongly influence non farm uses. Some examples in support are

A hard or petrocalcic horizon or other slowly permeable layer (clay-pan) in a soil restricts the movement of air and water, thus adversely affecting the plant root growth. Such a layer limits the soils suitability for septic tanks and other onsite waste disposal. It also makes a poor sub grade for high way construction.

A soil high in soluble salts is a poor medium for plant growth; it is also corrosive to iron and steel structures, such as pipelines and fences, causing high maintenance and repair costs.

High organic matter content is generally favourable to plant growth, but it is a limitation for road construction.

The strongly gypsiferous soils (gypsum content >10%) are considered unsuitable for arable crop cultivation because of its dissolution under irrigated or even moderate rainfall and making the surfaces uneven through the developments of sink holes. Such soils are unsuitable for irrigation and drainage canal networks and also for construction of roads, bridges etc.

The shrink- swell soils of India (vertisols) because of summer monsoon rains are suitable for growing a “kharif” crop but are very dangerous for construction of roads, air strips, buildings, laying gas, water pipelines which get distorted, develop cracks and lead to sunken floors etc.

Land is merely an area of the earth’s surface comprising all physical, chemical and biological environments that influence the land use (FAO ,1976). Land refers not only to soil but also to climate, landforms, hydrology, geology, natural vegetation including fauna and flora. The land use simply means management of a land for specific uses to meet the human needs in all rural, urban and industrial context, whereas the land use plan refers to a coherent set of decisions about the ways to achieve the desired level preferably on economic background. Thus, the land use planning is the systematic evaluation and assessment of a land and associated attributes for the purpose of identifying the best land use options beneficial to landowners or users without degrading the land resources or environment. It includes not only the landowner or users, but also the policy or decision makers as well as economic experts for financial measures from planning stage or logistic planning to execution.

Land cover

Relates to the type of feature present on the surface of the earth e.g., Fields, trees, concrete high ways.

Land use

Relates to the human activity associated with a specific piece of land. The same land would have a land cover consisting of roofs, pavements, grass and trees. The land use in an area is really an intervention by the people over the existing natural eco-environment for satisfying their needs.

Land cover is distinct from land use despite the two terms often being used interchangeably. Land use is a description of how people utilize the land and socio-economic activity - urban and agricultural land uses are two of the most commonly recognised high-level classes of use. At any one point or place, there may be multiple and alternate land uses, the specification of which may have a political dimension.

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

The satellite data is available in raster format. Raster data consists of two – dimensional array of pixels or grids which stores the values representing features. All the pixels are of same size and shape with their particular coordinate location. A raster data format is shown below.

Sensors onboard the satellite records the intensity of emitted or reflected electromagnetic radiations in the form of digital number (DN) for each pixel. These digital numbers can be transformed to more meaningful intensity units like radiance or reflectance. This process of conversion of digital numbers to radiance or reflectance is known as radiometric calibration. Sensor specific information is required to conduct radiometric calibration.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

Raster transformations involve mathematical manipulation of raster layers to generate new raster images. These transformed raster images either contain the information of interest or highlight the features which are not discernible in the original image. Transformations are also performed to preserve the essential information in reduced number of dimensions or layers. In this chapter, we are going to discuss few most commonly used transformation tools listed below:

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Supervised classification is the technique to classify raster image in which prior information about the classes in the form of training sites is provided by the user. Training site represents a homogeneous area in a particular class. The spectral response pattern of training sites is used to classify the raster image.The pixels of raster imagewhich have spectral response pattern similar to that of training sites of a particular class are grouped in that class. As there is some variability in the spectral response pattern of a particular class, therefore, multiple training sites per class are taken for supervised classification. Supervised classification of raster images can be performed by various methods. Conventional methods include maximum likelihood classifier, minimum distance to mean classifier, and parallelepiped classifier. Now a days, machine learning algorithms are also gaining importance in this context. These classifying algorithms are first trained or calibrated using the data of training sites.This trained model is then validated on the independent datasets for accuracy assessment. If the accuracy of the developed model is of acceptable level, then that model is used for the classification of raster data.

In this chapter, we are going to discuss the execution of maximum likelihood classifier and few machine learning techniques for supervised classification of raster image in R software. The training sites used in this exercise are in the form of points shapefile. The raster image is classified into six classesi.e. built- up, fallow soil, riverine sand, salt-affected soil, vegetation, and water. For performing this exercise, first of all,set the working directory and import the required packages.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Maps make it easier to interpret and communicate the results of any GIS analysis. R has a large number of packages for making thematic maps. Static mapping is straightforward with plot()methods provided by core spatial packages sf and raster. The use of plot() function have been discussed in the previous chapters. Another package for mapping isspwithspplot() having enough options for producing good quality thematic maps.It is also possible to create advanced maps using ggplot2. This chapter discusses, separately, preparing thematic maps from vectors and rasters with the above mentioned packages. A dedicated map-making package tmaphas been given special focus in this chapter.

1.1 Weather Parameters

The atmosphere is a gaseous component surrounding the earth as an integral part and its interaction with ocean (water bodies), other planets of the solar system and land (Earth) generates one physical product namely weather elements (temperature, pressure, wind, humidity, precipitation, etc.,) and these form the weather and climate of the Earth. The process of generation of weather and climate of the region is purely natural and cannot be modified by the human being. But they can do adaptation to the existing climate and weather for their sustainable life. The weather can be defined as day to day change in the atmosphere in terms of heat and moisture exchange, while the climate is average of long period weather covering larger area or a region.

The particular climate of a region dictates/permits the genesis of a particular bio-organism in that domain and this selected organism’s growth and multiplication are triggered by the prevailing weather of that particular climate. Hence weather parameters of the atmosphere are very important to human being and other organisms of the Earth. The details on weather elements are given in question and answer section of this book (Annexure-I)

The climate, the past weather event that existed over larger region and covering long years of more than 30 years is very important for the emergence and successful sustenance of an organism including human kind. Normally scientists talk about environment and this includes not only the soils but also the climate of the particular region. Based on this, during 1989, the Planning Commission of India divided the entire India in to 15 agro-climatic zones based on physiography and climate. This division is only for planning purposes. Subsequently under NARP, the Indian Council of Agricultural Research (ICAR), New Delhi in consultation with the Planning Commission, requested all State Agricultural Universities (SAU) to divide their State in to different sub climatic zones considering soils, temperature and other weather parameters preciously with in the major agroclimatic zone of India in which the concerned State falls. Accordingly, each SAU has divided their State into different agro-climatic sub-zones and this resulted in 129 agro-climatic sub zones in India and limited to 127 and these all fall within 15 agro climatic zones of India as done by the Planning Commission of India.

The National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Nagpur not satisfied with this division, considering soils, temperature, rainfall, vegetation and all factors responsible for ecosystem (bio-climate, length of growing period, moisture index) divided the whole India in to 20 agro-eco regions by 1992 and revised preciously again by 2015 (Mandal et al., 2016) and the revised approach is being considered as scientific tool and taken for micro-level planning in India now and those are discussed at this chapter.

The subject on micrometeorology deals with small scale meteorology of near ground surface with its related elements of both physical and biological processes. When this micrometeorology is integrated for crop production purposes, then it becomes crop micrometeorology, which is very important in the science of agricultural climatology. This crop micrometeorology gets varied with the architecture of the crops grown, available soil moisture, agronomical practices followed, soil texture, topography of the area, the nature of climate and weather that prevails etc. The soil surface heat, exerts a profound frictional effect on wind movements and hence it is different from the air layers at higher levels. These special features constitute the microclimate, which is modified compared to a flat bare surface by factors like topography, nature of soil surface, presence of vegetation and type of vegetative cover. The presence of crop canopy leads to distinct differences in the thermal and humidity regimes of the soil, the air layers immediately above it and the distribution of radiation and wind profiles inside the canopy. The study on the physical exchanges of the transfer of heat, momentum and moisture in the microclimatic region is called as micrometeorology (Venkataraman and Krishnan, 1992).

Radha Krishna Murthy (2002) reported that the micrometeorology is the micro scale of both the physical and meteorological factors that are taking place within the ecosphere. The ecosphere means the area between just above the crop canopy and just below the root zone of a crop. Further he was on the opinion that the microclimate of valley is important as compared to the entire climate of the mountain for the bio-life of valley. For the crops purpose, the microclimate is the climate of the ground surface to top of the crop canopy or otherwise it can be called as crop’s climate, a modified one from the Stevenson’s Screen climate/weather observation.

4.1. Introduction

Many countries have set up a network of agrometeorological observatories to regularly record important weather parameters such as clouds, rainfall, surface temperature, humidity, wind and wind direction, solar radiation, soil moisture, temperature and humidity profile of the atmosphere, etc. The observation process is performed either manually or through Automatic Weather Stations (AWS).

This recorded information is used for weather forecasting, natural resources management and for tracking agricultural production. However, these networks, which handle data of high spatial climate variability, are typically less robust than they should be. Moreover, the agrometeorological data collected from different ground stations is not transmitted in real time to a central collection point which limits the use of information.

Satellite remote sensing technology is an alternative system that is gaining popularity in the recent times which has allowed us to obtain frequent and precise measurements of vital agrometeorological parameters such as rainfall, solar radiation, temperature, evapotranspiration, albedo etc. Satellite data has many advantages over traditional ground stations, such as high spatial resolution (50 m–5 km), wide spectral bandwidth range (0.4–3.75 um) and high temporal frequency (even half an hour) and the prospect of collecting data from remote areas. In addition to routine repeated observations, satellites often provide synoptic views of the earth’s surface from space on a wide scale and have archive of information to create baseline data.

The preparation, planning and use of agricultural technologies for agriculture, needs application of agricultural meteorology. The role of agrometeorology in the last three decades has increased in terms of increasing food and nutritional security for the burgeoning population and natural resource management particularly in the context of global climate change. Agrometeorology can help to minimize inputs, thus clarifying the contribution of ecosystems and agriculture to the carbon budget in the event of global change. Agrometeorological information needs to be disseminated to users in time and space quite accurately, as agricultural activities are linked to the very local situation where the outputs of modern tools such as local area models, climate models, and dynamic crop simulation models are reduced to finer grid resolution and combined using GIS to extract practical suggestions. This chapter describes the recent applications of remote sensing in Agrometeorology.

5.1 Introduction

A Crop Simulation Model is a mathematical model which describes crop growth and development processes as a function of environment, soil conditions, and crop management. Simulation model is a schematic representation which works on set of underlying assumptions, equations and algorithms, closely replicating the behaviour of the agricultural systems. A crop growth simulation model predicts the harvestable yield. Apart from this final state of crop production, the model also provides a host of quantitative information about major processes involved in the various stages of crop development. The information covers each component of the crop such as leaves, stems, roots and harvestable products as they change over time, including the changes in soil moisture and nutrient status.

An increase in the population, demands an increase in agricultural production with available resources. Efficient management of available resources under varying weather conditions is important for increasing agricultural productivity. Besides that, the emphasis of agricultural production changes from quantity to quality and sustainability (Aggarwal et al., 2000). Solution of these new challenges lies in understanding the interaction of the numerous elements affecting the plant growth and generating a crop growth model. This extremely complicated information needs to be processed and interpreted on a spatial scale. Information technology which has unleashed new frontiers in data analysis and automation is the driving force behind this model generation. These powerful computer programmes simulate the crop growth or yield of crops under various management regimes and provide near real time information to help farmers to make strategic decision in the management of their crops.

Under normal weather situation, agriculture depends on timely occurrence of rainfall for crop management activities irrespective of different farming situations namely, irrigated and unirrigated agriculture. Changes in temperature and rainfall pattern together with frequent occurrence of extreme weather events are major anticipated threats from the climate change that occurs now around the world. This threat in long run, if not addressed properly, would affect food security of a country. Pratap Narain et al., (2000) observed an increase in moderate and severe drought occurrence numbers in the arid zone of India during last decade (1991-2000) as compared to the earlier decades of the last century. Similarly, Pasupalak (2008) reported daily maximum rainfall of 400.3 mm in the last decade against earlier record of 256.4 mm (1969–78) in a location at Odisha State. Further the inter- annual variation within the decade did increase and also the number of rainy days with very heavy rainfall > 125 mm also found increased in the location against the daily mean rainfall of 20 mm/day in India.

A day may be with heavy rainfall or with bright sunshine or windy or in combination of these. When heavy rainfall continues for more than a week with some system like cyclone, floods may be the result. Similarly, if dry spell continues during a cropping season for more than 15 days, drought would initiate in that region. This is again depending upon the nature of the soil water holding capacity and nature of soils, topography and green cover of that region. In between these two extremes, we have normal season, where we get normal seasonal rainfall during a cropping season. Hence like dress code, in agro-climatology, there are three weather codes, namely floods code, drought code and normal code. The jargon weather code was developed by Dr.M. S Swaminathan in India. Across the countries of the World, if these weather codes occur, the impact would be universal. The strength of adaptation capacity to maneuver these extreme events by the farming community rests with their past experience made for similar situations, financial support and also the technology options that are available from the scientific institutions.

7.1 Importance of Weather Forecast in Agriculture

In India, agriculture is in reality to be a “gamble activity” with the monsoon rain, that means the productivity and profit from agriculture depend upon the goodness of the season. If any malevolent weather situation that happens during a crop season, whether it is at the beginning of the crop season or at the mid of the crop season or at the terminal of the crop season or at the whole of the crop season, the productivity of the crops raised would be under stake. In practice the irrigated crop area is lesser than the area under unirrigated crop in India in any region except the regions where irrigation is routed through perennial rivers. Normally the production from unirrigated crop is 30 to 40 per cent of the same crop grown under irrigated condition in an environment in the same season.

In India, in a region or in a taluk, if the irrigated area is lesser than 30 per cent of total cultivable area, there is every opportunity for the occurrence of food shortage. That means assured production depends upon irrigation rather than managing the crops with seasonal rainfall alone. This clearly indicates that the higher probability of occurrence of crop production risks falls under rainfed/dry land situation. The crop production risk is also common to irrigated area like unirrigated crop but the intensity gets varied. In this context one example that happened in India can be quoted. During December- January of 1982, with the occurrence of heavy rain/snow as a result of prevalence of western disturbance weather system, the wheat crop stood for harvest at Punjab got damaged with a loss of 124 crore of rupees. If this was anticipated earlier through weather forecast development, (which is common in all developed countries) and communicated to the farmers in advance, then the yield loss would have been reduced by following some tactical decisions to be taken either by the farmers or by the concerned Government. At that time, India did not have medium range weather forecast system and hence Indian farmers met with greater losses.

8.1 Climate Change

Climate change is a broad term of global negative phenomena as created predominantly by burning of fossil fuels, which add heat-trapping gases to earth’s atmosphere. The over accumulation of greenhouse gases in the atmosphere lead to increased temperature and this is called as global warming. As a result, sea-level rise; ice mass loss in Greenland, Antarctica, Arctic and mountain glaciers worldwide; shifts in flowering/ plant blooming; and extreme weather events occur.

Livestock population meet the protein requirement of humans in addition to meeting from plants. The food chain starts from fodder crops to animals and from animals to humans. In human physiology, digestion of plant protein is much easier than animal protein, but however the digestion depends on climate (temperate climate, sub-tropical climate, tropical climate etc.,) wherein human kind live. Similar to crops, there is strong interaction between livestock and meteorological weather elements (temperature, relative humidity, wind etc.). But detailed study has not been published elaborately in scientific journals in the past. The weather elements have greater influence on livestock productivity in terms of milk, meat, skin, wool etc., pest and disease infestation and their housing needs. In this chapter how weather elements influence shed management, pest and disease problem and animals’ productivity and are discussed. In majority of the Veterinary and Animal Science University in India, they have one division on animal climatology, wherein both research and extension aspects are being attended. However, in depth research is the need of the present decade, since little works have been done in the past on animal climatology.

Irrespective of weather elements, the heat stress due to the combination of temperature and relative humidity plays important role in affecting the productivity of animals. There is a range of thermal conditions within which animals are able to maintain a relatively stable body temperature by means of behavioral and physiological means. Heat stress results from the animal’s inability to dissipate sufficient heat to maintain homeothermy (Sejian et al., 2017). High ambient temperature, relative humidity and radiant energy compromise the ability of the animals to dissipate heat. As a result, there is an increase in body temperature, which in turn initiates compensatory and adaptive mechanisms to re-establish homeothermy and homeostasis. Homeostasis refers to the tendency to maintain a balanced or constant internal state that is optimal for functioning. Animals have a specific balanced or normal body temperature. When there is a problem with the internal functioning of animal body, this temperature may increase, signaling imbalance. As a result, body attempts to solve the problem and restore homeostasis for normal body temperature. These readjustments generally referred as adaptations and this is essential for survival of the animals.

Astro-meteorology is considered as one of the branches of Astronomy along with Astrophysics, Astrobiology and Astrometry. Astronomy is the science that deals with the material universe beyond the earth’s atmosphere. It studies the nature and constitution of Celestial (heavenly) bodies and the laws which govern them including the effect they produce upon one another. It seeks to unravel the probable past history of heavenly bodies and their development. There exists a confusion between the branches of Astronomy viz., Astrophysics and Astro-meteorology with the term Astrology. Astrophysics uses principles and laws of physics to explain how Celestial bodies form and function or in other words it will help to ascertain the nature of the astronomical objects, rather than their positions or motions in space. However, the Astrology and Astro-meteorology are the subjects deal in future prediction and often considered as pseudo-science which uses position and movement of stars and planets for that purpose. Astrology links these to predict the human behaviour, while that of Astro-meteorology links the same to predict the future weather.

From ancient days, these predictions are based on one’s wisdom and knowledge in astronomy and often these astrologers are astronomers who keenly watch the stars and planets movement and guide their rulers and farmers with their guesstimates. Till date it is being lore, mainly passed to followers of this group and the methodology is not documented clearly and properly. There are many incidences of falsified predictions of astrology as reported by many reviewers elsewhere and we are not discussing the same here, owing to our interest in weather prediction i.e., astro-meteorology only. However, we have to recognize the works of the ancient astrologers who helped in developing the astronomical science.

Basically, there are two approaches in astrology viz., geocentric and heliocentric as used by the practitioner of astrology worldwide. As the name implies the Geocentric astrology is based on the calculation of planetary position as seen by the observer from the earth and thus earth is centre, while Heliocentric astrology bases its interpretation upon position within solar system with reference to the sun as centre. Though in true sense, the sun is the centre, the effect of motion as manifest on the earth is the basis of most astrological interpretation. Hence, vast majority of astrologers employs the geocentric calculations of the planetary positions for their predictions.

Meteorology is derived from the Greek word meteoro which means above the earth’s surface; i.e., the atmosphere. The state of the atmosphere experienced at a given time, in any location on the surface of the earth is known as weather. Thus, meteorology is the scientific study of the atmosphere or the weather. It is a branch of physics dealing with atmosphere and it is often quoted as the ‘physics of the lower atmosphere’. The main focus of meteorology is to understand the weather processes and forecast the weather. Solar radiation, the air mass and moisture are the three key ingredients that make up weather. The exchange of heat, moisture and momentum between the earth’s surface and the atmosphere determines the characteristic of weather. The observable weather events are known as meteorological phenomena and are defined by a set of variables called weather elements. Atmospheric temperature, atmospheric pressure, humidity, precipitation, cloudiness, wind and visibility, etc. are examples of such weather elements. The weather elements are not separate entities. They are closely related with each other and are highly variable in nature. Meteorology explains and analyses the changes of these weather elements.

The major subdivisions of meteorology are dynamic meteorology (or atmospheric dynamics) and physical meteorology (or atmospheric physics). The dynamic meteorology is concerned with the analysis and interpretation of the three-dimensional, time-varying, macro scale motions and energy transformations in the atmosphere. The science of dynamic meteorology is meant to develop models based on the hydrodynamic and thermodynamic equations. The physical meteorology is concerned with the processes that alter the physical properties and the chemical composition of air parcels as they move through the atmosphere.

Atmosphere can be defined as the gaseous envelope surrounding the earth. It is retained by earth’s gravity. The atmosphere has a mass of about 5.15 × 10¹⁸ kg, three quarters of which is within about 11 km of the surface. The imaginary ‘Karman line’ lies at an altitude of 100 km above the earth’s sea level, and commonly represents the boundary between the earth’s atmosphere and outer space. The study of earth’s atmosphere and its processes is called atmospheric science or ‘aerology’.

Atmospheric pressure is the force per unit area exerted on a surface by the weight of air above that surface in the atmosphere of earth. The gas molecules of the atmosphere are pulled towards the earth by the force of gravity and hence exert a force on earth surface. Atmospheric pressure can be calculated as the summed weight of all gas molecules between a horizontal plane and top of the atmosphere and divided by the area of the plane. Low pressure areas have less atmospheric mass above their location, whereas high pressure areas have more atmospheric mass above their location.