eChapters

Introduction

Crop discrimination is the process of using remote-sensing technologies to identify and characterize different types of crops based on their electromagnetic behavior at different wavelengths. Each crop has a unique spectral signature that reflects its response to the incident radiation. However, the spectral signature can also vary depending on the season, the angle of the sensor, crop’s features, illumination intensity, weather phenomenon, and topography among other external factors (Parida et al., 2023).

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

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.

Introduction 
The preceding chapter introduced the foundational concept of the systems approach, emphasizing the interconnectedness of components within complex entities and the power of simulation to understand their dynamic behavior. Building upon this understanding, This chapter delves into one of the most vital applications of system simulation in agriculture: crop models. These sophisticated computational tools stand as the cornerstone for quantitatively representing the intricate interplay between the soil, the plant, and the atmosphere, providing a dynamic lens through which to examine agricultural productivity and resource use efficiency. Crop models are essentially mathematical descriptions that mimic the growth, development, and yield of agricultural crops under various environmental conditions and management practices. They encapsulate our scientific understanding of how plants absorb light, water, and nutrients, how they convert these resources into biomass, and how they respond to stresses like drought, heat, or nutrient deficiency. Far from being mere statistical regressions, these are often process-based models, meaning they attempt to simulate the underlying biological, physical, and chemical mechanisms that govern crop performance. By integrating knowledge from disciplines such as plant physiology, soil science, meteorology, and agronomy, crop models provide a holistic and dynamic representation of the entire agricultural system. The evolution of crop modeling has mirrored advancements in computational power and scientific understanding. From early empirical models that relied heavily on statistical relationships between yield and a few environmental factors, the field has progressed to complex mechanistic models that simulate detailed physiological processes on a daily or even hourly basis. This evolution has transformed crop models from purely research-oriented tools into powerful instruments for real-world decision-making. Their ability to conduct virtual experiments—simulating hundreds or thousands of growing seasons

Introduction 
The preceding chapter elucidated the concepts of potential and achievable crop production, highlighting the significant gap that often exists between a crop‘s theoretical maximum yield and what is realized in the field. While genetic potential and optimal climatic conditions define the upper limits, the vast majority of agricultural systems globally operate under less-than-ideal circumstances. Among the most pervasive and impactful constraints on crop productivity are moisture (water) and nutrient limitations. These two factors, either individually or in complex interaction, are frequently the primary drivers of the yield gap, preventing crops from expressing their full genetic potential and significantly impacting farm profitability and regional food security. Water, as the universal solvent and a direct participant in photosynthesis and countless metabolic processes, is indispensable for plant life. However, its availability in agricultural systems is highly variable and often insufficient. From the rainfed fields dependent on increasingly erratic rainfall patterns to irrigated lands facing dwindling water resources, moisture stress – encompassing both drought and, paradoxically, waterlogging – poses an immense challenge. Drought alone accounts for a substantial portion of global crop losses, directly affecting plant physiological functions, growth, and ultimately, yield. Conversely, excessive moisture can lead to anaerobic soil conditions, nutrient leaching, and root damage, equally debilitating for crop performance. Parallel to moisture, the availability and judicious management of essential plant nutrients are foundational to productive agriculture. Plants require a suite of macro- and micronutrients for healthy growth, enzymatic activity, and the synthesis of biomass. Soils, over time and with continuous cropping, often become depleted of these vital elements, necessitating external application through fertilizers or the enhancement of natural nutrient cycling processes. However, nutrient management in the field is rarely perfect. Deficiencies of

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.

Introduction 
Building upon our foundational understanding of how weather intrinsically shapes crop development, from the micro-scale physiological responses to the broader patterns revealed by Indigenous Technical Knowledge, we now transition to a chapter dedicated to a highly practical and actionable synthesis of this critical interrelationship: Crop Weather Calendars. These calendars are far more than mere chronological charts; they represent systematically organized and dynamic compilations of meteorological information meticulously integrated with precise crop-specific data, designed to serve as an indispensable guide for optimizing agricultural practices. In this chapter, we will embark on a comprehensive exploration of Crop Weather Calendars, moving beyond the often informal or culturally embedded ecological calendars previously discussed. We will delve into how these powerful tools systematically integrate historical weather patterns, which involves analyzing longterm meteorological data to establish reliable averages and identify probabilities of specific weather events, such as the typical onset of monsoons, average frost dates, or periods of peak solar radiation. They also incorporate real-time meteorological data, bringing in current weather observations to track actual conditions against historical norms and initial forecasts. Furthermore, Crop Weather Calendars utilize future weather forecasts, drawing on short-, medium-, and long-range meteorological predictions to anticipate upcoming conditions (Fig.12.1). This integrated weather information is then critically aligned with the precise phenological stages of a given crop cultivar, recognizing that a crop’s sensitivity and requirements vary significantly throughout its life cycle. Our exploration will detail the essential components that constitute a robust crop weather calendar, including the identification of crucial growth periods, pinpointing the most sensitive and influential developmental stages of the crop like germination, tillering, flowering, and grain filling, where weather impacts are most pronounced. It also involves the establishment of weather thresholds, which means defining the optimal, critical, and damaging ranges of key weather

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)

Abstract

A large tsunami triggered due to a massive earthquake of 9.3M in the offshore of Sumatra on 26^th December 2004 created havoc in several countries of the Indian Ocean. The deadly tsunami waves lashed the towns adjoining the coastline of several countries including Indonesia, Thailand, Malaysia, India and Sri Lanka causing more than 1,50,000 deaths. The earthquake occurred along the plate boundary is marked by a subduction zone between the Indian plate and the Burmese micro plate. In Andhra Pradesh, six coastal Districts were affected and amongst them four south coastal districts experienced maximum damage. In the present study, Tuppalapalem (vagarru revenue village) of Vakadu mandal, Nellore District was selected as a case study for discussion in the article. The objective of the study was to make an inventory of the damages to the natural and physical resources, by preparing pre and post-tsunami maps on geomorphology, land use / land cover, tsunami inundation and finally the tsunami risk zonation map on 1:8,000 scale. In this process, water and soil samples of the affected regions were also collected and chemical analysis was carried out. The data on damages were collected using a specially formulated data sheet. High resolution IKONOS data of January 2000 and IRS data of January 2005 were used for the generation of respectively the pre and post-tsunami geomorphology and land use / land cover maps. Ground truth data were also incorporated in these thematic maps.

The study has revealed that the water in all the bore wells became brackish after tsunami. About 80 country boats, 40 fibre boats and nets were lost with an estimated loss of about one crore. The tidal waters damaged the bunds of the aqua-farms and deposited the transported sediments in them. The extensive casuarina plantations, were reduced by the onslaught of tsunami and the casuarina trees became brown due to their contact with tidal waters. From amongst the various geomorphic units mapped in the study area, the beach, spits, shoals, creeks, swales, palaeo creeks were inundated by tsunami where as beach ridges, dune complexes and stabilized sand dunes were not affected and hence inferred as barriers. Geomorphology, land use / land cover and the tsunami inundation maps, tsunami risk zonation map were prepared showing very high, high and moderate zones of tsunami risks. It is recommended to take up casuarina plantations extensively along the coast as a biowall, which will also provide employment to the local people. The shallow aquifers of the coastal environment were contaminated by the tidal waves. Hence, drinking water should be supplied through protected water supply schemes. As a part of the community development, proper awareness must be created through training and appraisal to the fisherman community to face such natural disasters. The coastal regulation zone act, which prohibits any construction activity within 500m of the high tide line, should be strictly enforced. Keeping in view of the experience, a holistic tsunami protection plan is needed to safe guard the Indian coasts.

Abstract

The tsunami has struck many coastal countries of South Asia on 26 December 2004 and caused greater devastation along Andaman - Nicobar and Indian coasts. In India, Tamil Nadu coast is worst affected in Kanyakumari, Nagapattinam and Cuddalore districts. Subsequently, damage assessment, in respect of natural and physical resources, was done using Remote Sensing and GIS in parts of Cuddalore and Pondicherry coasts. The structural damages were studied village wise and categorized as maximum, moderate and least affected areas. As far as the natural resources are concerned, the changes in geomorphology, land use/ land cover, soil and water have been studied. On the basis of the same, the risk maps were also generated in GIS environment.

Keywords: Tsunami, Damage Assessment, Remote Sensing, GIS, Cuddalore, Pondicherry

In 2009, the world celebrated the 200th anniversary of the life and work of Charles Darwin, a transformational scientist who brought about a revolution in our understanding of evolution.  Evolution through natural selection among living organisms leading to the survival of the fittest was a concept which was alien to Christian thought in Darwin’s time, as faith was strong in the divine creation of the living forms of our planet.  Evolution of higher forms of life, including human beings, from fish and other forms of living organisms was an accepted view in ancient Indian thought, as exemplified by the ten forms of manifestation on earth of the Hindu god Vishnu.

“Variety is the spice of life” is a common saying.  Variation is a must for selection to occur.  Today, we are confronted with the prospect of human-induced changes in climate, leading to adverse variations in temperature, precipitation, flood and sea level.  We will have to be prepared for facing the consequences of drought, flood and coastal storms more frequently.

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).

This chapter deals with data importing, exporting and data manipulations in R. R can import almost all kinds of data format. The basic data format of .txt and .csv and data from other softwares like MS office .xls and .xlsx, spss ……, stata ……., etc. however, we will discuss in this chapter to read and write the basic formats and the .xlsx data which we use the most. Further, we will also discuss the basic data manipulations. For a detailed reading on data manipulation, readers can refer to the first book of the series namely “Data Handling and Manipulation in R”.
2.1. Primary Spectral Data Processing
The original spectra consisting of relative reflectance values of 2151 points (at 1 nm interval) between 350 and 2500 nm were averaged at every tenth-nanometer wavelength interval from 360 to 2490 nm by integration technique. Thus, the new spectra contained reflectance values at 360, 370, 380, 2490 nm, are inclusive. For example, the new reflectance value for 360 nm represented an integrated value of the original reflectance values from 356 to 365 nm, inclusive. Similarly, the reflectance at 400 nm was an integrated reflectance values from 396 to 405 nm, while the reflectance at 2490 nm was an integrated value of reflectance from 2486 to 2495 nm. This was done to reduce the volume of data for analysis and to match it more closely to the spectral resolution of the instrument (3 to 10 nm). This procedure resulted in a new dataset, with reflectance values specified for 214 wavelengths for all samples, which were further processed for noise removal.
2.2. Importing Data in R
We can rerad many format of tabular data in R such as excel, text, csv, etc. In thid book we will be using the csv format. Before reading the data, arrange data in excel/CSV sheet so that spectral data in column is followed by chemical data/ lab data. This will ensure that if any soil property or properties added in next column there is no need to change code for selection of spectral data in R environment.

Abstract

Carrying out effective and sustainable agriculture has become an important issue in recent years. A key to this is the usage of modern technologies for precision agriculture. Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As more and more agricultural data are virtually being harvested along with the crops and are being collected/ stored in databases, the same can be used in productive decision making if appropriate data mining techniques are developed/applied. As a case study for this, an experiment was conducted with four consecutive agricultural (Kharif and Rabi) seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule and multivariate regression mining techniques/algorithms were propose developed /tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather- environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system.

Keywords : Data Mining, Pest/Disease Management, Precession Protection, Wireless Sensor Networks

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

Introduction 
The preceding chapters have firmly established the conceptual foundations of the systems approach and introduced the diverse world of crop models, highlighting their intricate mechanisms and their transformative potential for agroadvisory. However, even the most elegantly formulated and sophisticated crop model is ultimately a framework; its predictive power and reliability are inherently tethered to the quality and availability of the information it receives. As the adage in data science goes, Garbage in, garbage out– a truth that resonates profoundly within the realm of agricultural system simulation. The chapter shifts focus from the theoretical constructs of models to the vital practical considerations of data requirements. This chapter will delve into the indispensable role that accurate, comprehensive, and well-structured data plays in every stage of crop modeling, from initial parameterization and rigorous calibration to robust validation and reliable application. Without the right data, even a theoretically perfect model cannot accurately mimic the complex dynamics of the soil-plant-atmosphere continuum or provide trustworthy insights for agricultural decision-making. This chapter will systematically unpack the various categories of data essential for crop modeling. It will meticulously detail the specific characteristics and sources of weather data, which serve as the primary atmospheric drivers. It will then explore the crucial properties and measurement techniques for soil data, underpinning the understanding of water and nutrient availability. The discussion will extend to crop genetic and physiological data, which define the inherent capabilities and responses of specific plant varieties. Furthermore, the chapter will cover management data, illustrating how human interventions are integrated into the modeling framework. Beyond merely listing data types, this section will critically examine the challenges associated with data acquisition, including issues of quality, resolution, consistency, and spatial representativeness.

Introduction

The first step of geostatistical analysis in R is the creation of dataset which is acceptable in R environment by using the data collected from field survey or laboratory experiments. Different type of data is collected during experiments e.g. sand content, silt content, organic carbon content, geographical location of data points, soil type, land use type etc. Here, we will discuss how these data are converted into a acceptable dataset format for R environment and how we manage these data. In R this task is performed in two steps. First, a data structure is created to hold the data and second is the entering or exporting the data into data structure.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

4.1 INTRODUCTION

Database management system is a computer program to store, search, manipulate retrieve and give the result. Vast amount of spatial data are available and it is necessary to arrange them, store them for retrieval and analysis. Attribute data adds intelligent to GIS. Without database, a GIS will be just a Cad software. Cad softwares do not have database. GIS softwares have their own databases besides it has the potential to link with external database like Oracle, MS Access, MS Excel, Informix, INGRESS, Sybase and dbase. The data available in paper has to be digitally stored in a proper order. Previously the data were stored in a a flat file format where the data was stored without any structure. But to search and retrieve and analyse the database, database structures were developed. There are of 4 types of database structures namely Hierarchical, Network, Relational database and Object orientation.

Tables are made of rows and columns. Rows represent a map feature. Columns represents characteristic of map feature. Row is called a record or a tupule column is a field or an item. Table 4.1 is a table where rows 1, 2 and 3 represent the locations with the soil types. A column soil type represents various soil types.

Abstract

Population of Rajasthan as per Census 2011 is 68,621,012 persons which is much higher than census 2001 that records 56,507,188. It includes 12 districts of Rajasthan. Western Rajasthan covers about 61% area of the total geographical area and 39.51% population of the total population of the Rajasthan. The growth rate of population is much higher than that of the Rajasthan State. In the census 1981 Western Rajasthan had registered a phenomenal increase of 35.48 per cent over 1971 population. Regional pattern of growth rate in western Rajasthan shows that extreme hot arid region district (Barmer) is on top having 32.55 per cent growth rate whereas Indira Gandhi Canal Region (Ganganagar) district has least population growth of 10.06 per cent only. In 1981, the average urban growth rate had been 54.52 per cent. In 2001, the rural density of population was 83 persons per sq. km.

Abstract

Conversion of an already arid land into a desert through indiscriminate human actions magnified by droughts. Such actions include overgrazing, repeated burning, intensive farming, and stripping of vegetation for firewood. One quarter of the earth’s surface is threatened by desertification - an area of over 3.6 billion hectares. Since 1990, Six million hectares of productive land are lost every year due to land degradation. Desertification threatens the liveihoods of more than one billion people and has already made over 135 million people homeless. Desertification does not occur in linear, easily mappable patterns. Deserts advance erratically, forming patches on their borders. Areas far from natural deserts can degrade quickly to barren soil, rock, or sand through poor land management.

Concept and Definition

Desertification is a major problem in the drylands of India, affecting the way of life for its inhabitants. The problem is more severe in the arid lands in the northwestern part of the country. There have been a few numbers of definitions of the word “Desertification”. However for simplicity of its origin and meaning we would refer to two of the frequently used ones; (1) proposed in 1991 by UNEP: “Land degradation in arid, semi-arid and dry sub-humid areas resulting mainly from adverse human impact and (2) prepared for the Earth Summit at Rio, held in June 1992, for inclusion in Agenda 21, as “Land degradation in arid, semi-arid and dry sub-humid areas resulting from climatic variations and human activities. (Anonymous 1992).

Comparing two of these initial definitions, it would show that first one indicated desertification as a state of natural resources like soil and vegetation, mainly resulting from human activity while the second one included climate as a major cause. However, it reduced the emphasis of human activities as being the primary responsible factor and tried to link the possible impact of climate change on desertification. But the simplicity of the 1992 definition of desertification helped researchers to bring in sharp focus on the special environmental and socio-economic problems of the drylands.

So over the years, the key words that explains the term “desertification” has been changing or are being amended for specific purposes. This would at least mean that the problem of desertification is still debated world-wide, and its impact is not limited to some nations/regions as was presumed and discussed in first of UNCCD platforms. Therefore, as a precaution and combating this process, few catchy words like non-sustainable management, productivity, over-exploitation of natural resources, dryland livelihood, droughts are found to surround the word desertification. Earlier, some, people equated desertification with advancing boundaries of the existing desert and in India, examples of expansion / contraction of geographical boundary of Thar desert was a public debate among various researchers and planners. In the same time, some of the definitions tried to broaden its geographical scope so that greater populations suffering from adverse effects of land degradation would benefit out of various remedial measures.

Remote sensing has proved its credibility beyond doubt in natural resources survey and environmental monitoring owing to its synoptivity, multispectral photographic capability and repetitive coverage. While its synoptivity gives unbiased information on the regional panorama of the earth surface, its multispectral possibility aids precise interpretation of various objects, as different terrestrial objects exhibit various spectral responses in different bands of the electromagnetic spectrum. Amongst various terrestrial objects, water has a characteristic spectral response of its own, i.e. it permits the blue / red part of EMR (electromagnetic radiation, 0.4-0.8 µm) to penetrate inside. On the contrary, it totally absorbs the infrared part of EMR (0.8-1.5 µm). As such blue/red part of EMR penetrates the water bodies, the impurities and suspended sediments are seen very clearly in various shades of gray as and when the water bodies are shallow. Many workers, therefore, have used blue / red band of the satellite data in such water-quality studies^l-3. Hardy⁴ has observed that the infrared light in the spectral regions of 0.6-1.4 µm has the potential to map the turbidity in the ocean water, whereas Kritakos et al ⁵ have used the rationed data of visible and near infrared bands (0.4-1.1 µm) and obtained better results for water quality monitoring. Keeping such facts in mind, the blue/red band (band 2) of the IRS 1A satellite data was used to configurate the bathymetry along Madras coast in Bay of Bengal (Figure 1 a).

In the present study, the IRS lA band 2 data (0.52-0.59 µm) of March 1991 was used and subjected to edge enhancement studies for the coastal zone of Madras (Figure 1 a). Such edge enhancement normally sharpens the edges of two contrasting objects or sharpens the edges of objects of contrasting relief. In such edge enhanced data, few

Introduction

Globally, the SOC pool of 1,550 Pg (1 Pg = 1015 g) to 1-m depth is about three times that of biotic and twice that of atmospheric pools (Post et al. 1982, Watson et al. 2000). In arid and semi-arid ecosystems, SOC contents show high degree of spatial variability due to patchiness of vegetation (Wiesmeier et al. 2009). The arid ecosystem might play a key role in mitigation of climate change effects through sequestering carbon in soils (United Nations 2011). Therefore, a suitable land use system having high C sequestration potential for arid ecosystem needs to be identified and promoted. However, the prevailing conditions of arid ecosystems normally does not support dense vegetation. Therefore, low carbon stock is expected in both above-ground vegetation and below-ground soil. Fruit trees are an integral part of arid farming system and played a significant role to improve the nutritional security and employment opportunity of farmers but also have potential role to increase carbon storage both in biomass and soil. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Therefore, reliable estimates of SOC pools are essential to establish the soil carbon sequestration programmes at different landscape scales.

Introduction

A complex spatial soil properties pattern is directly controlled by soil internal factors and anthropogenic impacts.Hartemink et al. (2001) elaborate a strong decline in mineralogy, morphology and genesis of soil research than strong increase in pedometrics applications during 1967 to 2001. Hartemink and McBratney (2008) identified this change as “soil science renaissance”. Basically, the soil science deeply rooted in agriculture, geology, and chemistry but the paradigm has been shifted from classification and inventorization to quantification of soil patterns in spatial as well as temporal patterns and their impact on the ecosystem health and hydrological cycle. The environmental keen approach explains that soil is in the center of the ecosystem and highly interacting with the biotic and abiotic factors of the ecosystem and a complex pattern and process has been co-evolved with the time.

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.

Introduction 
As we have journeyed through the preceding chapters, meticulously examining the foundational understanding of crop-weather interactions, the sophisticated development of crop simulation models and decision support systems, the invaluable contributions of indigenous technical knowledge, and the systematic formulation of precise and customized agroadvisories, we now arrive at the decisive stage of this entire endeavor of dissemination of Agromet Advisories. The profound scientific insights and meticulously crafted recommendations, no matter how accurate or relevant, remain inert unless they effectively reach the hands, ears, or screens of the farmers who need them most, in a format they can readily understand and apply. This chapter, therefore, is dedicated to unraveling the complex, yet essential, process of ensuring that crucial agrometeorological information successfully traverses the last mile to translate into tangible impact on agricultural practices and outcomes across diverse landscapes. The challenge of effective dissemination extends far beyond a simple broadcast; it necessitates the establishment of a robust, dynamic, and multichannel communication ecosystem. This ecosystem must be meticulously designed to account for the rich tapestry of socio-economic, technological, and literacy disparities prevalent within farming communities. Historically, traditional methods such as the communal gathering around a radio for a farmer‘s bulletin or face-to-face discussions during village meetings formed the bedrock of knowledge transfer, leveraging established social networks and widely accessible, albeit low-tech, communication tools. However, the relentless march of the digital revolution has fundamentally reshaped this landscape, unveiling an unprecedented potential for expansive reach, enhanced personalization, and rapid delivery of information, thereby opening a myriad of new avenues for disseminating critical advisories with remarkable speed and specificity.

The goal of food for all can be achieved only through sustained efforts in producing, saving and sharing food grains.

The Supreme Court of India has rendered great service by arousing public, professional and political concern about the co-existence of rotting grain mountains and mounting hungry mouths. In several African countries hunger is increasing because food is either not available in the market, or is too expensive for the poor. Food inflation is showing no signs of abating. In our country, chronic hunger is largely poverty induced. The progress made in achieving the UN Millennium Development Goal No. 1, namely, reducing hunger and poverty by half by the year 2015, has been far from satisfactory and the available data indicate that we may have years and years to go before we achieve this target. Globally, the number of persons going to bed hungry has increased from 800 million in the year 2000 to over one billion. The position is likely to get worse in the near term, since the prices of wheat, rice and maize are going up in the global market. Adverse growing conditions in Russia, Canada and Australia are partly responsible for the recent escalation in grain prices. Nearer home, Pakistan is recovering from serious floods. According to a recent UN report, 3.2 million ha of standing crops and 200,000 heads of livestock have been lost. 

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:

Geomorphometric analysis is the measurement of the threedimensional geometry of landforms and has traditionally been applied to watersheds, drainages, hill slopes, beaches, and other groupings of terrain features. In particular basin morphometric parameters have received a lot of attention from hydrologists and geomorphologists since watersheds (catchments) have been used for analysis of various physical ecosystem processes, including soil erosion, deposition, runoff, stream discharge, sediment yield, sedimentation of streams, irradiation by sunlight, evaporation, evapotranspiration, and nutrient distribution. The study of morphometric parameters, which include variables such as average basin slope, the basin elongation ratio, compactness ratio, basin relief and stream density. Some parameters, such as slope length or stream sinuosity, can be used to represent both basin and hill slope, or basin and stream channel properties.

The main task before geomorphologists is to use an ideal unit of the earth surface for the study of its landforms. The search for an ideal areal unit, within which the collection, processing, organization and interpretation of the data of the geometry of landform, particularly of erosional origin can be made has been the main aim of the geomorphologists right from Fennemen (1914), Horton (1932 and 1945), Strahler (1956 and 1957), Miller (1953), Chorley (1957), Schumm (1956), Shreve (1966), Singh (1981), Sarkar (1995), Schumm and Spitz (1996), Sharma and Amin (1996), Iqbaluddin et al (1997) and Raj et al (1999).

1. Introduction
Drought’ which apparently means lack of water, is one of most vulnerable natural hazards. It alone causes a huge crop loss annually. But history says it not only affects crop but destroys human lives.in the year 1972 almost 600000 people died due to starvation caused by huge drought in Northern India. In simple language drought means shortage of water mainly caused by abnormal climatic condition and damaging to the environment. Ideally there is no such proper definition of drought because it should be region specific and circumstances specific. A per McMahon and Diaz Arenas (1982) define drought as “a period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a serious hydrological imbalance, carrying connotations of a moisture deficiency with respect to man’s usage of water”. Apart from these several general definitions have been provided by many authors. Beran and Rodier (1985) gave a definition based on hydrological aspect of drought that, ‘The chief characteristic of a drought is a decrease of water availability in a particular period and over a particular area’. India meteorological department (IMD) has defined drought as a situation in a subdivision in a year when the actual rainfall is less than 75% of the normal annual rainfall. Basically, to define drought the impact on environment and society and their complex interaction must be considered. Drought is defined as a state of total water capacity being within the range of 12-20% for a period of 16 days and can be distinguished from water deficit, which is the state of water capacity falling below 30%. Drought affects crop development, production, productivity and quality and on other hand soil physical, chemical and biological properties.
2. Classification of Drought
As per the hydrological point of view drought can be classified into different Categories: i) On the basis of source from which water is available: -a. Meteorological Drought b. Hydrological drought c. Agricultural Drought. ii) On the basis of time of occurrence: - a. permanent drought area b. Seasonal Drought Area contingent drought. iii) On the basis of medium: - a. soil drought atmospheric drought.

There are reports in financial newspapers that the ongoing (2010) drought affecting nearly 200 districts in the country may not have much effect on GDP, since the farmers in the drought-affected areas contribute hardly 3 per cent to GDP.  It is sad that such a measure of the impact of drought on the lives and livelihoods of millions of rural families is even considered. It is this mindset, typical of the growing insensitivity to human suffering in our country, that is responsible for India being the home for the largest number of undernourished and malnourished children, women and men in the world.  No wonder we are finding it difficult to achieve the first among the UN Millennium Development Goals, namely, reducing hunger and poverty by half by 2015.  Unless we realise that agriculture in India is not just a food- producing machine, but is the backbone of the livelihood of over 60 per cent of our population, rural deprivation and suffering will not only continue to persist, but will get worse, leading to severe social unrest.

Abstract

The groundwater targeting in hard rock aquifer systems is complex, because of the heterogeneity of the aquifer systems. The ground water mobility and stability in such aquifer systems varies because of the lithology, folded structures, fracture density, geomorphology, slope etc., These occur in various permutations and combinations and control the overall water holding and conducting capacity in different parts of the aquifer systems. Hence, one needs to analyse all the data so as to understand and bring out precise spatial models on the groundwater probability in hard rock aquifer systems. In the present study, the authors have carried out GIS based appraisals to understand the relation between the lithology and groundwater movement in hard rock aquifer systems of parts of Central Tamil Nadu covering 1300 sq.km. In the said study, GIS images were generated on the maxima zones of various aquifer parameters such as water level, width of aquifer depletion, transmissivity, permeability, specific capacity, storage co-efficient, optimum yield and recovery rate. These were overlaid with the lithology GIS image. Such an analysis has shown that, in general, the fissile hornblende biotite gneiss appears to have some control over the groundwater systems.

Introduction 
Building on our extensive exploration of how individual weather parameters intricately influence every facet of crop life and the diverse methodologies employed to evaluate these responses, we now embark on a journey into the mathematical heart of crop science. This chapter, Elementary Crop Growth Models,” will introduce you to the foundational concepts and simpler mathematical frameworks used to quantify, simulate, and ultimately predict how crops grow under varying environmental conditions. In the upcoming sections, we’ll delve into the fundamental building blocks of crop modeling. You’ll discover how these elementary models simplify the complex biological processes occurring within a plant to create manageable, yet insightful, representations. Unlike highly detailed mechanistic models, which require vast amounts of data and computational power, elementary models focus on capturing the most essential relationships between environmental drivers (like solar radiation, temperature, and water) and key aspects of crop growth, such as biomass accumulation or phenological progression. These simpler models serve as a crucial stepping stone. They allow us to grasp core principles of crop-weather interaction in a quantifiable way, providing practical tools for initial assessments, quick predictions, and conceptual understanding, without getting lost in the overwhelming complexity of every single physiological pathway. By understanding these foundational models, you’ll gain a clearer perspective on how agricultural scientists begin to predict the future performance of our food crops, even as the climate around them continues to evolve

Remote sensing can be a useful tool in engineering geological investigations, particularly in the identification and characterization of geological features and hazards.

Some of the ways that remote sensing can be used in engineering geological investigations include:

Geological mapping: Remote sensing data can be used to create detailed maps of the geological features in an area, including rock formations, faults, and folds. This information can be used to identify potential hazards, such as landslides and rockfalls, and to plan engineering projects accordingly.

Landslide detection and monitoring: Remote sensing data, such as satellite imagery, can be used to detect landslides and monitor their movement over time. This information can help engineers to design and implement appropriate stabilization measures.

Ground deformation monitoring: Remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can be used to detect ground deformation caused by geological processes such as subsidence, uplift, and faulting. This information can be used to identify potential hazards and plan engineering projects accordingly.

5.1 INTRODUCTION

A GIS database without single error is reliable for decision making. There are various possible sources of errors in GIS. If an error is not removed during scanning, the final output will also have that error. Besides whatever the work has been carried on wrongly scanned map it will add to errors because the source map itself's erroneous. This is like garbage in garbage out proverb. If wrong data is taken for analysis it will only result in wrong output. Hence it is important to remove error at every stage of GIS work. It is important to locate the sources of errors so that due care may be taken while handling a project.

5.2 ORIGINAL SOURCES OF ERRORS

All the equipments which give attributed data and spatial data will give errors. Before going to field or before collecting data all the related instruments should be calibrated properly. Also the instrument should not cross the permissible error. Besides these while using data from diversified sources errors pertaining to those instruments are mixed to give a different result. When very good accuracy is desired data should be collected from high end equipments. Even if one adds the data from a comparbly less accurate instrument to the data of many accurate instrument is then the result obtained finally will not reliable enough. Global Positioning System (GPS) is available with mm accuracy to meters accuracy. Also surveying with suitable map projection and datum is important; otherwise the result will be wrong. Survey instruments posses some inaccuracy and when a person is using it he should have the accuracy over the instrumental like rain gauge, water level recorders which give attributed data which should be checked for instrumental inaccuracies and method of data collection before using it. Likewise satellite and aerial sensors miss some data and some error are inherent. Such errors should be removed before moving ahead. Errors are resulted due to map reduction, enlargement, data editing and computation method adopted. At the same mapping instruments also possess inaccuracies. When after certain interval a person is going for survey, the registered object may have been changed. Many new features might have come up. Hence the established control points are changed and if the wrongly placed control points are taken for survey then the result itself will be wrong. If proper resolution is not available in the instruments, then the instruments may not capture the minute details which are required.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.

An ancient Chinese proverb says

If you are thinking one year ahead, plant rice
If you are thinking ten years ahead, plant trees
If you are thinking hundred years ahead, educate the people

Subramanya Bharati, a great Tamil poet, wrote decades ago that nutrition and education were the two legs of a human being — nutrition for the body and education for the mind. We must, therefore, foster a movement for integrating academic excellence and social relevance in the curricula of our educational institutions. Our universities should promote the growth of the science of ecotechnology, which is the product of the integration of traditional knowledge with frontier science. This will need a mind-set change in relation to the knowledge and wisdom of our tribal and rural families.

In the 1990s, I developed the Iwokrama Rain Forest Programme, which represents the world’s largest adventure in the sustainable management of rainforests. The local Amerindian population welcomed me to Iwokrama with a song, which translates thus:

Abstract

The paper presents an approach of selecting suitable areas for artificial groundwater recharge structures through remote sensing and integrated terrain analysis by statistical method. The study area “Ayyar basin” is an aquifer system of gneisses and charnockite litho-units in Tiruchirappalli district, Tamil Nadu. The area has witnessed a steady decline in groundwater table. Hence, the scope for artificial groundwater recharge was studied in this area with the help of Tamil Nadu State Council for Science and Technology (TNSCS&T).

Introduction 
The preceding chapters have laid a robust foundation, introducing the concept of agricultural systems, delving into the architecture and types of crop models, and meticulously detailing the essential data requirements for their operation. We have established that crop models are powerful tools for simulating the complex interactions within the soil-plant-atmosphere continuum, driven by a rich tapestry of environmental and management inputs. Among these inputs, weather stands out as arguably the most dynamic and influential factor, wielding profound control over every aspect of crop life, from the precise timing of germination to the final quantity and quality of the harvest. Chapter 5 shifts our focus to the direct and indirect consequences of this critical environmental driver: the evaluation of crop responses to weather. This chapter is dedicated to understanding, quantifying, and interpreting how variations in meteorological conditions—such as temperature, solar radiation, precipitation, humidity, and wind—directly influence the physiological processes, developmental progression, biomass accumulation, and ultimate yield of cultivated crops. It‘s about moving from simply providing weather data to actually assessing its impact on the plant‘s performance. The ability to accurately evaluate these responses is paramount for several interconnected reasons: Firstly, from a fundamental scientific perspective, it deepens our understanding of plant ecophysiology. By analyzing how crops react to specific weather patterns, we gain invaluable insights into the underlying biological mechanisms that govern growth and development under diverse environmental pressures. This knowledge is crucial for advancing agricultural science itself. Secondly, for agricultural management and decision-making, this evaluation is directly actionable. Understanding optimal weather conditions for specific growth stages, or predicting the impact of adverse events, enables farmers

The geologists of the Indian subcontinent have all along been of the opinion, that, the southern part of the Indian Peninsula is a stable shield area. But, the recently acquired IRS-1A image of June, 1988 vividly displays spectacular geomorphic anomalies suggesting that this part of the Indian subcontinent is prone to earth movements. In order to unearth more evidences on such earth movements / neotectonism in Tamil Nadu, the IRS-1A image of Coromandel coast of Tamil Nadu was analysed ( Fig. 1).

OBSERVATIONS

• The IRS-1A image conspicuously displays three contrasting morphotectonic domains in the study area marked by the western undissected Tertiary upland (Vallam upland), central highly dissected Tertiary upland (Pattukottai -Mannargudi upland) and the eastern Cauvery delta (Fig 1 and 2). These three domains are separated by two conspicuous N-S trending lineaments (E and F, Fig.1 and 2) namely the Tanjavur - Avadaiyarkovil lineament in the west and Kumbakonam -Muttupet lineament in the east.
• In addition, two more E-W trending lineaments (G and H, Fig.1 and 2) are also observed trifurcating the undissected Vallam upland into three blocks.
• Along these lineaments, especially the Tanjavur -Avadaiyarkovil and Kumbakonam -Muttupet lineaments, the rivers show conspicuous drainage anomalies in the form of drainage deflection, drainage compression etc., wherever they transect the lineaments.

Abstract

It has been established beyond doubt that the drainage architecture and the fluvial histories of river systems give excellent indications not only on the surficial lithology but also on the ongoing morphotectonic processes of the planet earth. Among the various drainage patterns, the ‘eyed drainage’ pattern is considered to be one of the most significant anomalies and such ‘mega eyed’ drainages interpreted from IRS-IA imagery in some of the major river systems in Tamil Nadu, Indian Peninsula, are found to signify ongoing tectonic movements in an otherwise seismically inert shield area.

Factorial experiments provide an efficient method for studying two or more factors simultaneously at different levels. This chapter introduces the concept and notation of factorial arrangements and explains treatment combinations in experiments such as 2 × 2 and higher-order factorials. It distinguishes among simple effects, main effects, and interaction effects, emphasizing the importance of interactions in interpreting biological responses. Factorial experiments allow researchers to examine the effects of individual factors as well as their combined influence. The chapter also explains that factorial treatments may be arranged using different basic experimental designs, such as Completely Randomized, Randomized Block, or Latin Square designs, according to experimental objectives and conditions.

Abstract

The Adam bridge, the curvilinear chains of islands linking India (Thanushkodi) with Sri Lanka (Thalaimannar), has been a matter of scientific and mythological debate. While the great epic Ramayana says that Lord Rama built this Adam bridge around 1.5 million years ago to bring back his wife Seetha from Srilanka who was a captive by king Ravana of Srilanka, some reports gave another view that it is coral islands of 1.7 million years of age. Under this backdrop, the author refutes the claim made by NASA that the Adam bridge was located firstly by one of its satellites and cited evidences that the Indian scientists located and mapped it using the IRS satellite data much earlier. Further, on the basis of bundles of beach ridges dated in Tiruthuraipoondi-Vedaranniyam area and as well as some of these continue up to Uchipulli area in Ramanathpuram district, the author assigned an age of 3500 YBP ( Years Before Present) to Uchipulli beach ridges seen in the main land to the west of Rameshwaram and Adam’s bridge and inferred that the Adam’s bridge must be equal to or younger than 3500 YBP. The easterly nosing of Indian mainland in Ramanathapuram – Rameshwaram – Thanushkodi and its further extension in the form of Adam bridge, the author demonstrated that the because of anticlockwisely rotating littoral currents to the north and clockwisely moving littoral currents to the south of Adam bridge, the chains sandy islands namely Adam’s bridge was formed by the segregation of sands in between currents shadow zone after 3500 YBP.

Studies and Findings

There was some coverage in the newspapers some time ago about the discovery of a land bridge (Adam’s Bridge) between Dhanushkodi (India) and Talaimannar (Sri Lanka) by NASA through one of its satellite mission. It was also written in some articles that this land bridge was constructed by Lord Rama 1.7 million years ago to reach Sri Lanka with the aim to bring back his wife Sita from the clutches of Ravana. However, this linkage was refuted by NASA. It accepted that the picture was captured by one of its satellites, but it was unaware of the linkage of the land bridge with the great Indian epic Ramayana. Some reports gave another view and mentioned that the bridge was nothing, but 1.7 million years old coral reefs. Many historians have also come out against linking the Adam’s Bridge with Ramayana.

Three years after the establishment of the Central Institute of Fisheries Education (CIFE), a global conference was held at Rio de Janeiro, Brazil, for promoting environmentally sustainable development in all spheres of human activity. At this UN Conference on Environment and Development, generally referred to as the Earth Summit, an Agenda 21 was adopted to provide guidelines for mainstreaming ecology in all major research and development programmes. In Agenda 21, pride of place has been given to the management of aquatic resources from both the quantitative and qualitative aspects. 2012 marks the twentieth anniversary of the Rio conference.  It will be useful to prepare a balance sheet indicating where we have succeeded and where we have failed in implementing Agenda 21 in the area of inland and marine fisheries, as well as integrated coastal zone management.

Abstract

The anomalies observed in the reverine systems have been demonstrated to be the potential tool in deciphering the tectonically active zones. Such various drainage anomalies viz: radial drainages, anomalous compressed meanders, eyed drainage and the youthful floodplains observed in Annamali – Palani - Varushanad hill ranges of the Western Ghats of Tamil Nadu show that the N-S and NE-SW trending faults are tectonically active in the recent years.

Introduction
 
Arid western part of Rajasthan which contains the Indian segment of the Thar Desert, accounts for ~62% of the country’s hot arid area and is a region of low rainfall. Under such physico-climatic situation, fluvial processes function much less frequently and more episodically than the aeolian processes, but their impacts on landforms and society are large, as noticed on several occasions in arid regions. There is a distinct east-west gradient of rainfall and temperature in the region. Along the eastern margin bordering Aravallies, the mean annual rainfall is 500 mm while in the westernmost part at Jaisalmer, it is less than 150 mm. The rainfall is largely monsoon driven which brings 80 - 90 per cent of total rainfall received during the year. There is large inter-annual rainfall variability of rainfall which leads to climatic extremes of drought and floods of different magnitudes. For example on an average, the Thar Desert in India experiences flood-causing rainfall in 2-3 years in a decade, but a maximum of 5 flood causing rainfall years in a decade was recorded during the 1950s, 1970s and the 1990s (Rao et al. 2006).

Bhutan has developed the concept of Gross National Happiness (GNH) as a substitute for Gross Domestic Product (GDP). In this theory, GNH comprises spiritual and cultural values, including love of sport, music, dance and spiritual activities. This is a good departure from measuring happiness purely from the view point of money. I would like to elaborate on four pillars that are necessary to uphold sustainable human happiness.

Ecology

There is an increasing awareness of the need for adopting sustainable lifestyles which will not make a heavy demand on our life-support systems of land, water, biodiversity, forests, oceans and climate. In his book titled World on the Edge.1 Lester Brown has described how we still have time to prevent environmental and economic collapse. Recent calculations on the ecological footprint each one of us is leaving behind shows that our per capita consumption of natural resources is exceeding the biocapacity of our earth. Soon we will need two planets to meet the growing demand for land, water, forests and biodiversity. In this context, we should keep in view what Mahatma Gandhi said long ago: