eChapters

Introduction 
Augmented energy demand as well as impacts of employment of fossil coals on human health and environment, stresses and imposes the need to lessen the use of conventional fuels or replace them. Equally, global warming has stretched to a distressing level due to many diverse anthropogenic actions leading to increased pollution. The alternate energy sources are desirable that might be the conspicuous and sustainable approach of enabling environmental remediation, besides meeting the global energy call (Grosser et al 2016, Kanchanasutha et al 2016). A potential solution is generation of Biofuels, either conventional biofuels produced from agricultural crops or advanced biofuels produced from wastes, inedible crops or forestry products (Sreedevi Sarsan et al 2021). Advanced biofuels production especially by the utilization of agricultural and animal wastes using new technologies and processes are gaining increased interest among researchers and general public all over the world. Food waste, municipal trash, animal waste and other wastes can all be utilised as feedstocks to create biogas, a clean and sustainable energy source and thus may be the ideal replacement for traditional energy sources. The raw biomass may be converted into fuel products like biogas by briquetting, gasification, and anaerobic digestion, among other processes (Deepanraj et al 2015). In comparison to landfills, incinerations, and composting processes, anaerobic digestion (AD) is suggested as an inexpensive and environmentally benign technique for the generation of renewable energy (Van Forest et al 2012; Nishio et al 2007; Capson-Tojo et al 2016; Xu, F et al 2018; Cha, M et al 2020). The production of energy-rich biogas and alleviation of issues related to the removal of organic waste are all achieved by anaerobic digestion (AD) of organic solids (Sonakya et al 2001). This is since AD is a highly energy-efficient and ecologically benign technique. In an atmosphere devoid of oxygen, the AD system uses anaerobic microbes to transform the naturally occurring substances in the biomass into biogas. The primary byproduct of AD, biogas, has a methane content of roughly 50-70% by volume and 30- 40% atmospheric carbon dioxide (CO2) when feedstocks are digested under anaerobic circumstances, with small levels of hydrogen sulphide (H2S) (0.1-3% v/v), humidity, and other trace pollutants also present (Sahota et al. 2018). The generation of biogas is known to be influenced by factors such as waste types, pretreatment procedures, the microorganisms or enzymes added, operation circumstances and design of the digestors (Mao et al 2015).

Introduction

Spectral signature: A spectral signature is the unique pattern of reflection and emission of a target at different wavelengths. This difference helps to classify remote sensing images based on the spectral signatures of different objects.

Electromagnetic (EM) spectrum: The EM spectrum is the range of all types of EM radiation. Some examples are visible light from a lamp, radio waves from a radio station, microwaves, infrared light, ultraviolet light, X-rays, and gamma rays.

Spectral response patterns: A spectral response pattern is the amount of energy that an object reflects or emits at different wavelengths. It shows how the object interacts with the EM radiation.

Modern plant genomics generates highly complex and high-dimensional datasets that require advanced computational approaches for effective analysis. This chapter explores the application of artificial intelligence to plant genomic data analysis. Machine learning and deep learning techniques are discussed for identifying complex patterns, classifying genomic information, predicting phenotypes and supporting genomic selection. Convolutional neural networks, long short-term memory networks and transformer models are considered for analyzing genomic sequences and relationships. The chapter presents an AI-based framework integrating data processing, model development and prediction while highlighting the challenges associated with genomic data heterogeneity and dimensionality. It emphasizes the potential of AI for improving genomic research and plant improvement strategies

ABSTRACT

Groundwater geochemistry is largely a function of mineral composition of aquifer through which water flows thus, differences in aquifer composition are reflected as differences in groundwater chemistry. To understand water quality and flow pattern multivariate statistical analysis are used due to its relative importance in combining chemical variables and organize voluminous data and group into variables of similar characters. R-mode factor analysis is used in the field of hydrogeochemistry to interpret complex hydrogeological process. A total of 216 samples representing complete lithology like peninsular gneiss, Charnockite and calc gneiss for four different seasons was collected and analyzed for major ions like Ca, Mg, Na, K, HCO₃, Cl, SO₄, NO₃, PO₄, F and H₄SiO₄. Correlation analysis reveals Mg, Cl and Na are spinal and others seasonal. Factor analysis indicates ascendancy of anthropogenic basis followed by lithological weight. Factor score superimpose indicate governing hydrogeochemical regimes spread in western and eastern part. In southern part the same has superior influence than northern part.

Abstract

Water has become a scarce resource all over the World. Water resources of Earth can be classified as surface water and groundwater in which groundwater is the main source for the drinking, domestic and agriculture. The Pudukkottai district is one of the chronic drought prone areas in Tamil Nadu. But, at the same time, the district is pitted with over 5,400 tanks/water bodies widely distributed throughout in general and clustered more in Vellar delta in particular, as the interlobel depressions have provided basic depressions for the surface water to accumulate. However, these surface water bodies are heavily silted. In Pudukkottai district, there are three divergent aquifer systems, hard rock aquifer system in the western, sedimentary aquifer system constituted by the Tertiary Sandstone (Cuddalore Sandstone) in the central and Quaternary aquifer system constituted by fluvial, fluvio-marine and marine systems in the eastern parts of the district.

Introduction to GIS

QGIS (Quantum GIS) is an open-source Geographic Information System (GIS) software has been used to showcase various exercises pertaining to viewing, editing, analyzing, and printing of the geospatial data. QGIS helps to allow the users to easily access and manage geographic data stored in folders on local disks or relational databases available on the user’s network. QGIS supports raster, vector, and mesh layers. QGIS also allows using data from external sources with the help of web services such as Web Map Service (WMS) and Web Feature Service. QGIS is a constantly developing software as anyone can add new features or update existing tools. Newer versions of QGIS are frequently released on the official website. It is a cross-platform software that can be installed in Windows, Linux, and MacOS.

More information, more accurate and timely decision. In present high tech world, the more information you have it is easier to make an accurate decision. The information comes in many different ways, from reports to digital photos. A new way to analyze data is a geographic information system (GIS). GIS allows to bring all types of data together based on the geographic and locational component of the data. GIS is a useful tool for seeking innovative ways to solve the problems.

9.1 What is GIS?

A geographic information system (GIS) is a system designed to capture, store manipulate, analyze, manage and present all types of geographically referenced data. GIS can be defined as a computer-based system for digital entry, storage, transformation, analysis and display of spatial data. A geographic information system integrates hardware, software and geographically referenced data for further analysis. GIS allows users to interpret the georeferenced data in many ways that reveal relationships, patterns and trends.

1.1 INTRODUCTION

Geographic Information System (GIS) is the new emerging field and grows at very rapid pace. Now GIS is a billion dollar industry with applications in varied disciplines. At present, GIS is being used by professionals from various disciplines. In this decade, GIS is being used by experts who are good in GIS technology for various applications. The development trend indicates that GIS will be used by common man in near future and at the same time, advanced GIS analysis will be carried out by GIS experts.

Any technology is developed out of necessity and GIS is of no exception. When a large amount of data were available for water distribution systems, road network, sewer lines, telecom lines, electricity lines, gas pipe lines, state wise population, natural resources etc, it is important to store, maintain and retrieve the data for applications. It is not possible to manually browse through the large amount of paper records. Also even if the data is available in an unorganized way in the computerized environment, it is not useful. Hence if the data are referred to locations in earth surface and the data are stored in an organized way, it is possible to retrieve and analyse the data for applications. Hence the solution is GIS and developed today into reality. Developed countries are using GIS widely in many areas but developing countries are moving towards the development of GIS database for their resources.

Remote sensing, aerial photography, cartography, surveying and other field instruments for attribute data collection contributes to the data acquisition. Cartography, surveying, geography, geodesy contributes for mapping process. Disciplines like computer science and statistics, mathematics involved in processing and analysing data . Computer science and mathematics involved in storing the data structure.

GIS maps are intelligent. It has many advantages compared with paper map in this digital age.

Spatial data means data which are referenced to earth. Maps, satellite imageries, aerial photographs are example of spatial data. Attribute data which are attached to spatial data are called as aspatial data or non-spatial data.

If these data are brought into GIS then it is stored in a standard format, it is possible to update, share, retrieve, manipulate and analyse quickly. Much of the time and money is saved in case of reproduction and better decisions could be made

In modem days, many informations receive through electronic media with instant connecting by satellite based information on social, economical, environmental etc. the informations are coded with feasible electronic encrypt and delivered to people. Similarly remote sensing, GIS (Geographical Information System) and GPS (Global Positioning System) also serve world for better understanding the ground reality along with reducing cost of operation, transforming data through electronic webs easily. In earlier phase, ground related information was not easily obtained because all kind of work had been done manually using analog maps of earth; even map draw was done by drafting from totposheet or hand maps, no interface of analog and digital world.

Introduction

A geographical information system (GIS) is a kind of database that has software tools for handling, analyzing, and presenting spatial data and descriptions of phenomena that have a location component. This system can also involve human users and support staff, procedures and workflows, a body of knowledge about relevant concepts and methods, and institutional organizations in a wider sense. GIS technology is a key part of spatial data infrastructure, which the White House describes as “the policies, technologies, standards, human resources, and related activities that are necessary to acquire, process, distribute, use, maintain, and preserve spatial data.”

Key words : NRSA, Natural Resource and Disaster Management, Agricultural Drought, Ecosystems, Biodiversity Information System, Water Resources, Sea Surface Temperature.

Introduction

A few decades ago, paper maps were the principal means to synthesize and represent geographic information. Manipulating this information was limited to a manual, non-interactive process. Since then the rapid development of new technologies to collect and digitize geographic data, together with an increasing demand for both interactive manipulation and analysis of this data, has generated a need for dedicated software’s namely geographic information systems (GISs).

A GIS is more than a cartographic tool to produce maps. It stores geographic data, retrieves and combines this data to create new representations of geographic space, provides tools for spatial analysis, and performs simulations to help expert users to organize their work in many areas like public administration, transportation networks, military applications and environmental information systems. These above mentioned are few among many others where we are concerned with geographical locations, their present status, its potential for developmental activities, patterns of change over the years and the processes on the surface of the earth.

Geographic information technology is used to manipulate objects in geographic space, and to acquire knowledge from spatial facts. Spatial information theory provides a basis for GIS by bringing together fields that deal with spatial reasoning, the representation of space, and human understanding of space.

Key words : Flood Plains Agro-ecosystem, GIS, RS, Spatial Cropping Pattern, Radiometric Normalization, NDVI, Agricultural Systems.

Introduction

Of late, evidence is accumulating to suggest that high productivity and growth rates achieved during the Green Revolution era in the well-endowed regions of the country (i.e North West India) are no longer being sustained to meet the needs of still increasing population in the country. Past growth sources have nearly exhausted and there is also no scope for expansion of crop land. Indeed pressure for competing land uses will likely reduce area under food grain crops. Shrinking natural resource base (per capita availability of land and water devoted to agriculture), declining quality of resources and environmental degradation issues will imply increasing threat to our ability to meet the basic needs of the growing population of the region unless we shift our effort to potential regions like flood plains of the country for improving productivity.

The flood plain riverine lands of India covering approximately 42 m.ha though most endowed with natural resources and has all the potential of becoming rich, is still economically backward. The reason behind this paradox of extent of poverty in the midst of richness of resources is that the natural resources of the region are not being productively harnessed into the process of the economic development and thus are not delivering their potential and commensurate benefits to the people. The Green Revolution technologies evolved during of 60’s and 70’s had little or no impact in this region of the country. Undesirable spatio-temporal distribution of flood in these areas keeps agriculture under uncertainty and risks. These are also the areas where the poorest are concentrated and agriculture largely continues to be more of subsistence. The nature of agricultural problems are highly variable due to great diversity in the production system as also in the socio-economic conditions of the communities requiring location specific approaches to address the problems and enhance the productivity.

Key words : Glaciers, Glacial Lakes, Glacial Lake Outburst Floods (GLOF), Himachal Pradesh, Glacial Inventory, Glacier Recession.

Introduction

Glaciers are large masses of snow, re-crystallized ice and rock debris that accumulate in great quantities and begin to flow outwards and downwards under the pressure of their own weight. Glaciers form when yearly snowfall in a region far exceeds the amount of snow and ice that melts in a given summer. In this way, massive quantities of glacial material accumulate in relatively small periods of geologic time. The glaciers exist in environments of high and low precipitation and in many temperature regimes; they are found on all the continents except Australia and they span the globe from high altitudes in equatorial regions to the polar ice caps. Presently, 10% of the Earth’s land area (5.8 million square miles) is covered with glaciers storing about 75% of the world’s freshwater. However, during the last Ice Age, glaciers covered 32% of the total land area of the planet.

Introduction

Precision Farming (PF) revolves around the idea that any agricultural land contains wide spatial variations in soil types, nutrient availability, and other important factors. If these variations are not taken into account, then there may be a loss in productivity. Precision farming is one of the most scientific and modern approaches to sustainable agriculture that has gained momentum in 21^st century. Consider the situation: ‘A farmer goes to his/her field with his/her Global Positioning System (GPS) guided tractor. The GPS senses the exact location of the tractor within the field. It sends the signal to a computer on the tractor that has a Geographical Information System (GIS), storing the soil nutrient requirements map in it. GIS, in consultation with a Decision Support System, decides what is the exact requirement of the fertilizer for a given location and then commands a variable rate fertilizer applicator, attached with the tractor, to apply the exact dose at that precise location. All this is done within a second, before the tractor moves further.’ This may sound like an excerpt from a scientific fiction, but this is what precision farming means to the large growers of the United States or European countries and day is not far off when it will be feasible for large Indian farmers as well.

PF refers to a developing agricultural management system that promotes variable management practices within a field according to site or soil conditions. Within a short span of time, this concept has been described with various names viz. farming by soil; farming soil, not fields; farming by the foot; spatially prescriptive farming; computer aided farming; farming by computer; farming by satellite; high-tech sustainable agriculture; soil-specific crop management; site-specific farming; and site-specific crop management.

Key words : Wasteland Reclamation, Poverty Indicators, Food Insecurity, Alternate Livelihood Systems, Rural Infrastructure Index.

Introduction

The countries of Asia and the Pacific region accounts for nearly two-thirds of the chronically poor and undernourished in the world. FAO estimates indicate that, by 2010, Asia will still account for about one-half of the world’s malnourished population (FAO 2000). In Asia, poverty has mainly been rural phenomenon and nearly three-fourth of the poor live in rural areas, with large majority of them dependent on natural resources for employment and income. South Asia, which had a poverty incidence of 43 percent (or about 520 million people), contributed about 40 percent of the world’s poor. Development of natural resources thus offers a potentially enormous means of poverty reduction.

In developing countries poverty and environmental deterioration are often viable in proximity to each other, and have led many to infer that a two-way causality exists between human and environmental degradation. Environmental degradation usually occurs when production and consumption activities of growing population irreversibly weaken nature’s recycling capabilities. These economic activities are also attributed to the development of markets, the advent of modern technology, and the spatial integration of inaccessible areas to market systems. In many developing countries, however, environmental degradation such as soil erosion, deforestation and pollution are most visible around poor settlements, leading some policy makers to highlight the direct links between poverty and the environment.

One has to recognize that both poverty and the environment are descriptions of states of human and natural resource attributes and cannot be reduced to simple one-dimensional cause-effect relationships. Apart from conceptual difficulties in modeling linkages, another handicap has been the absence of adequate or reliable data sets on poverty and environment characteristics. The challenge for operational research is exploring how circumstantial evidence and inductive logic can be used to explain the nature of interactions between the two states. Based on nationwide wasteland statistics vis-à-vis state and district wise poverty estimates available with the concerned agencies, this study is one such attempt, and evaluates the incidence of poverty with the backdrop of natural resources degradation and role of institutional interventions with illustrations from India.

Abstract

The Pudukkottai district located in the southeastern tip of Tamil Nadu, India provides a complex geological setting with three litho-tectonic blocks membered by the hard Crystalline rocks of Archaeozoic-Proterozoic Era in the west,Mio-Pliocene Sandstone/Cuddalore Sandstone in the centre and Quaternary sediments in the east. While the western and central blocks are separated by a well-defined NNE-SSW trending fault, the central Mio-Pliocene Sandstone block is cut into two parts by a N-S fault with western static block and the eastern uplifted counterpart. Besides the above, the various drainage anomalies viz; Annular, Deflected and Eyed drainages and compressed meanders interpreted using the raw and digitally enhanced satellite multispectral data show that the N-S oriented faults with oscillations from NNW-SSE to NNE-SSW, NE-SW and E-W orientations are tectonically active in the area. The integration of these active faults with known geothermal springs and the maxima axes drawn from the contours deducted from the temperature of groundwater measured during the predawn hours from the wells revealed that the N-S and NE-SW faults are the probable locales for geothermal resources in the study area. Thus the study demonstrates a newer methodology for locating the geothermal prospects.

Abstract

The Tamil Nadu is one of the leading states in groundwater development. Since the surface water resources have been fully exploited for irrigation, the groundwater is massively mined for agriculture, domestic and industrial purposes. The paper provides a synthesis of whole groundwater related issues including basin wise groundwater conditions, the aquifer types, geogenic water quality and quality deterioration due to anthropogenic activities, need for recharge, the utility of Remote Sensing & GIS etc. The paper has come out with set of recommendations as the concluding remarks.

Introduction

Geomorphology is the study of earth’s landscapes over time and space, and involves an analysis of both its surface features as well as the processes responsible. Historically, the subject evolved through monumental studies on supposed evolution of the landscape, based on intuitive knowledge of the researchers on landscape response to endogenic and exogenic forces, as well as visions of possible changes over a time scale of centuries to millennia or more. Too much emphasis on the study of possible landscape evolution in an era of little technological back-up facilities meant that the discourses were largely philosophical in nature, with fewer measurable parameters to substantiate the concepts or the reasoning, although reflecting deep understanding of the researchers involved. As older concepts began to be confronted with newer ones, and applied aspects started receiving greater attention, a need was felt for quantification of the features and processes. This was more so for an understanding of the fluvial landscape, although aeolian and other process-based studies also gradually incorporated quantitative analysis as a means of understanding the landscape. Despite the awareness, lack of adequate equipment hindered the progress of quantification in the pre-World War II period.

Abstract

Natural resources and agricultural production processes vary spatially. Capturing the spatial variations in data and knowledge of natural resources and production processes has been a major challenge for efficient and sustainable management of agricultural production systems. Geographical Information Systems (GIS) provide an effective platform for storing and mapping spatial data and modeling the relevant processes. Huge volumes of geospatial data on specific themes such as soils, water, climate, and other natural resources, are being collected by public and private agencies. However, currently it is difficult for most users to access the data and integrate them into a GIS platform to derive useful information and knowledge for specific applications. There is a need to create geospatial data and knowledge systems that can provide efficient, on-demand and remote access of spatial data to users to enable development of a variety of GIS applications for agricultural and natural resources management. The design of a prototype geospatial library to provide web-based access to geospatial data, services, and learning and knowledge resources that can support agricultural and natural resources management is presented. The design is scalable and sufficiently general to allow a range of agricultural and natural resources management applications across different spatial scales.

Keywords: Agriculture, GIS, Geospatial library, Knowledge Management, Natural Resources

Abstract

Any naturally occurring phenomena varies both in space and time. This variability is a result of various natural process and hence deterministic. Geostatistics is a tool to help us to characterize spatial variability and uncertainty resulting from imperfect characterization of the variability. Geostatistics involves the theory of regionalized variables, which dates back to the early fifties and includes concepts of random function and stationarity. Geostatistical mapping can be defined as analytical production of maps by using field observations, explanatory information, and a computer program that calculates values at locations of interest. There area number of spatial prediction models depending on the amount of statisticsinvolved in the analysis. Kriging is a generic name adopted by the geostatisticians for a family of generalized least- squares regression algorithms and various kriging algorithms viz., punctual kriging, block kriging, co-kriging and universal kriging are explained in this paper. Applications of geostatistics can be found in many varied disciplines ranging from the geology to soil science, hydrology, meteorology, environmental sciences, agriculture and even structural engineering and some of its applications are summarized in this paper.

Keywords :Geostatistics, spatial variability, kriging, model