eChapters

Introduction

The Rispana river is a order of stream forming a tributary of Ganga. Its watershed covers an area of approximately 53.6 Km in the northern part of Dehradun city lies between 78^º30’ to 78^º15⁰’ longitude and 30^º15’ to 30^º 30’ latitude in top sheet No is 1/3. The area is covered in IRS - IC Pan of 13 Jan 1998 and LISS-III of 7 March 1997.

The Doon Valley is enriched with numerous seasonal and perennial streams. Which forms the lifetime or the inhabitants . The Rispana river is the part of the Ganga system of Drainage of the valley. The river originates from the Mussoorie hills, which are full of natural springs. The droplets from the springs collect and form numerous streamlets and these streamlets are joined to be these and they form the famous Shekhar Falls (north of Shahanshai Ashram about 5 Km away). Then the Rispana flows along Rajpur road over the Doon Gravels and then river passes through the tiny hamlets Nalapani, Raipur, Chanderroad, Defence colony, Amwala, Tapowan, Dandadhoran Adohiwala, Dalanwala, Gujram, Ajabpur and finally its contribute the water to the suswa River which intern joins the Ganga.

Introduction

Microbial diversity is one of the main factors characterizing natural ecosystems; soil is considered one of the best storehouses of useful microorganisms in the world. Although the role of most of these microorganisms is still unknown, scientific progress is providing a better comprehension of the specific ecological functions of soil microorganisms (Ahmad, I.et al., 2008). Microorganisms in the rhizosphere soil play a key role in maintaining the soil fertility (Yadav et al. 2015), which is key for successful natural crop production to meet the increasing global organic food demand. Soil acts as a reservoir of air, water, and nutrients that is essential for plant growth. Only a few grams of soil contain hundred million to billion microorganisms. Bacteria are the most abundant microbes in soil followed by fungi; however, the actinomycetes are ranges in between bacteria and fungi. Other microbes that are found in smaller numbers are algae, cyanobacteria, protozoa, and nematodes. Soil gives the necessary support for anchorage to plant roots (Yadav et al. 2012; Bhaduri et al. 2015).

Abstract

This study evaluates the potential of remote sensing techniques for assessing the relationship between land degradation and poverty in Pali development block of Rajasthan. Due to climatic conditions that prevail in this area of the country, less facilities and management practices to conserve land and its fertility becomes an important factor for intensifying poverty. The other countenance of this is that it is poverty which leads to land degradation. This paper assesses the impact of poverty and land degradation on each other. The desertification intensity and BPL density is calculated and relation is seen using Chi square test. The goodness of fit is seen up to 5 per cent level of significance for land degradation and demographic data.  The desertification in agricultural area is also considered. The total degradation in the block calculated is 8.39 per cent. It is found that village with highest BPL density has more degradation intensity particularly in agricultural area, proving the agricultural based economy of the dry lands in Rajasthan. An example study is also done at village level.  Using cadastral map and satellite image classification, fields that require immediate attention have been identified. Demographic and degradation maps are also prepared.

Introduction

In one or another way, agriculture is integral to the physical and economic survival of every human being. The United Nations forecasts that the global population will reach more than 9 billion by 2050. To feed everyone, food production will have to increase 70 per cent. Helping the world’s farmers to achieve this target is challenging in itself, but beyond providing food, agriculture sustains the economies of most countries in significant ways, especially in the developing world (Anonymous, 2018). To meet this challenge in a fast-changing world, new tools, processes and ways of organizing-i.e. innovations-are needed to make agriculture more efficient in economic, social and environmental terms (Hall, Mytelka and Oyeyinka, 2006). Past efforts have tended to focus solely on technology. Previous approaches to technological change in agriculture have been based around supplying technology in a simplistic, top-down manner. These approaches had some success for relatively simple innovations in conducive settings. However, these simplistic approaches are not adequate for the task of meeting the complicated challenges facing agriculture today. Historically, new agricultural technologies have been developed by public research organizations and then transferred to farmers by public extension services (Sulaiman, 2015).

Mushrooms are a nutritious and versatile food commodity that has gained significant importance in India in recent years. With a growing demand for healthy and organic food options, mushroom production has emerged as a lucrative venture for Indian farmers (Fig. 9.1).
• India is home to a diverse range of wild and cultivated mushroom species, with over 200 varieties found across the country. The majority of mushroom production in India is concentrated in the states of Uttar Pradesh, Punjab, Haryana, and Himachal Pradesh.
• Mushroom production in India has witnessed significant growth over the past few decades, driven by advances in technology, improved farming practices, and increasing demand. According to the Indian Council of Agricultural Research, the country produces over 1.5 million metric tons of mushrooms annually, with a value of over `3,000 crores.
 • Mushrooms are an important crop in India due to their high nutritional value, medicinal properties, and economic significance. They are an excellent source of protein, fiber, and various essential vitamins and minerals. Mushrooms are also rich in antioxidants and have been found to have potential health benefits, including reducing inflammation, improving cardiovascular health, and boosting the immune system.

INTRODUCTION

Rubber cultivation in regions extending from 10°N to 10°S latitude is known as the traditional region and the areas other than its natural climatic origin is termed as the non-traditional region. Northeast India comprises majority of the non-traditional areas in India with major climatic constraints like high seasonal rainfall, excessive precipitation and to some extent dry conditions. These areas extending from 22 to 29^°N latitude, holds promise in extending rubber cultivation because of its exceptional climatic make-up and abundant amount of annual precipitation (2000 mm) distributed over a fair period of time.

This perennial crop generally yields latex rubber at the 7th year after planting (with an economic life span of 20 years), although the period (immaturity period) it requires, is higher in the non-traditional region (Rubber Growers Companion, 2006). This immaturity period has also been shown to reduce with water availability in sub-humid climates (Vijayakumar et al. , 1998). Harvest of this crop is by tapping. Tapping is a process of controlled wounding during which thin shavings of bark are removed, thus opening the latex vessels which exude latex. Dry rubber yield is obtained after the latex is coagulated and rolled into sheets (about 500gm) which is then smoked and dried.

The natural habitat of rubber (Heavea brasiliensis) is rain forests of the Amazon basin, situated within 5° North and South at altitudes below 200m. The climate of this region is equatorial monsoon type characterized by mean
monthly temperature by 25 to 28°C, well distributed rainfall and no marked dry weather. Though it is originated in the Amazon basin, it is now predominantly grown in the tropics where an equatorial monsoon type climate prevails. Regions between 8°N and 10°S which includes Indonesian archipelago, Malaysia, southern part of Sri Lank  and some other Islands, are better suited to rubber cultivation. The production of natural rubber in the country was 8.65 lakh MT in 2008–09, registering a 4.74 per cent growth compared to the previous year. India is the fourth largest producer of natural rubber with a share of eight per cent in the World after Thailand, Indonesia and Malaysia and the fourth largest consumer of natural rubber after China, USA and Japan. Among the rubber growing countries in the World, India occupies a pride place with the top most position in productivity (Table 9.1). The productivity of natural rubber (NR) in India in 2009–10 was 1784 kg/ha. In 1950–51, it was 284 kg/ha. The growth achieved in terms of the productivity of natural rubber is 628 per cent. This achievement

3.1 Runoff

Runoff is one of the important parameter of hydrologic cycle. It is defined as that portion of precipitation which is not absorbed by the deep soil strata but finds its way into the stream after meeting the demands of evapotranspiration, interception, infiltration, surface storage, and surface as well as channel detention. It includes surface runoff received into the channels after precipitation occurs, delayed runoff that enters the stream after passing through the land surface, and other delayed runoff from the temporarily detained pits, depressions, swamps or detained as snow fall. It is thus the flow collected from the drainage basin and appearing at the outlet of the basin. The runoff process is demonstrated in Fig. 3.1 as flow chart. Details of the runoff process are discussed as below.

Introduction 
Chapter 14 explores the intricate relationship between rural development and agricultural economics, emphasizing their roles in promoting sustainability and food security. This chapter examines various aspects of agricultural policy, agribusiness, farm management, rural entrepreneurship, and sociology, linking these elements to broader economic and environmental concerns. The chapter starts with an analysis of agricultural policy and development, discussing how policy frameworks shape agricultural practices and rural growth. It highlights the importance of effective policies in fostering agricultural innovation, improving productivity, and supporting rural development. Agribusiness and marketing are crucial components of agricultural economics. This section delves into agribusiness strategies and agricultural marketing practices that influence market access, value addition, and overall economic viability for farmers and agribusinesses. Farm management and financial analysis are essential for optimizing agricultural operations and ensuring financial sustainability. The chapter explores principles of farm management, including planning, decision-making, and risk management, along with techniques for financial analysis to evaluate the profitability and efficiency of farming enterprises. Rural entrepreneurship is highlighted as a key driver of economic development in rural areas. This section examines how entrepreneurial activities contribute to rural growth, job creation, and economic diversification, fostering resilience and innovation in rural communities. Rural sociology provides insights into the social dimensions of rural development. The chapter covers the importance of rural sociology in understanding social structures, behaviors, and dynamics within rural communities. It also discusses new concepts in rural sociology that address contemporary challenges and opportunities. The importance of rural development and agricultural economics in food security is emphasized. The chapter explains how these fields contribute to enhancing food security by improving agricultural practices, supporting rural livelihoods, and addressing food supply challenges.

Abstract

A very strong shallow focus submarine earthquake (of magnitude 9.0) occurred off the west coast of Sumatra Island due to the movement of Indian Plate and Burma Plate on 26th December 2004 at 06:18:53 IST. A vast area around its epicentre was rocked including Andaman and Nicobar Islands and parts of the eastern and western coasts of the Indian mainland. This earthquake had generated a global tsunami, which had also pounced on the Andaman and Nicobar Islands and the coasts of Andhra Pradesh, Tamil Nadu and Kerala and caused severe damage to the coastal population and infrastructure. Considering the devastation of this tsunamigenic earthquake, the Department of Science & Technology, Govt. of India has launched a coordinated programme for imparting S & T inputs with futuristic approach to support rehabilitation programme in the affected areas on a sustainable manner. The main objective of the programme is to generate geospatial databases on damages and risk zonation using pre and post-tsunami remote sensing (RS) data along with collateral attribute data in GIS platform. This paper speaks on the damage assessment (both natural / physical resources and human resources) caused by the earthquake and tsunami covering parts of Andaman Islands along with strategies for risk management and constraints and alternatives for micro level planning.

Keywords: Tsunamigenic earthquake, Damage Assessment, Remote Sensing and GIS, Andaman - Nicobar.

Introduction

The coastal tract of peninsular India extends from Rann of Kutch in Gujarat to Malabar Coast in Kerala on the western coast and from Coromandal coast in Tamil Nadu up to Sunderban delta in West Bengal on the eastern coast. The eastern coast is generally characterized by a wider coast line when compared to its western counterpart. Coastal ecosystem in India occupies an area of about10.78 million hectares (1,07,833 km²) extending in nine states. It also occupies considerable area underLakshadweep and Andaman and Nicobar group of Islands. The extent and distribution of coastalarea have been shown in Table 1.

Saline Water Agriculture has been drawing interest as one of the solutions for limitation of world fresh water supply, farmland availability, salinity ingression and the pressing future need of food supply. The cultivation of salt-tolerant crops on saline soil and with saline waters has significant social and economic potential.

It is often argued that fresh water agriculture near rivers and ponds was invented because of the need to sustain people and communities. Saline water agriculture is going to be needed for sustaining our future generations and civilization.

11.1 Introduction

Salinity is the saltiness or dissolved salt content of a body of water. The technical term for saltiness in the ocean is halinity, from the fact that halides, chloride specifically are the most abundant anions in the mix of dissolved elements. In oceanography, it has been traditional to express halinity not as percent, but as parts per thousand (ppt ), which is approximately grams of salt per liter of solution. In other disciplines chemical analyses of solutions, and thus salinity is frequently reported in mg/L or ppm (parts per million). Prior to 1978, salinity or halinity was expressed as % usually based on the electrical conductivity ratio of the sample to “Copenhagen water”, an artificial sea water manufactured to serve as a world “standard”(Lewis, E.L 1980). In 1978, oceanographers redefined salinity in Practical Salinity Units (psu): the conductivity ratio of a sea water sample to a standard KCl solution (UNESCO, 1981a) (UNESCO, 1981b). Ratios have no units, so it is not the case that 35 psu exactly equals 35 grams of salt per litre of solution (UNESCO, 1985).

These seemingly esoteric approaches to measuring and reporting salt concentrations may appear to obscure their practical use; but it must be remembered that salinity is the sum weight of many different elements within a given volume of water. It has always been the case that to get a precise salinity as a concentration and convert this to an amount of substance required knowing much more about the sample and the measurement than just the weight of the solids upon evaporation. For example, volume is influenced by water temperature; and the composition of the salts is not a constant. Saline waters from inland seas can have a composition that differs from that of the ocean. For the latter reason, these waters are termed saline as differentiated from ocean waters, where the term haline applies.

The total amount of salts dissolved in water is known as salinity. Salinity of water is denoted as ppt, g/L, psu. Salinity of different water has been classified as Freshwater 0-0.5 ppt, Brackish water 0.5-30ppt and Marine water > 300 ppt.
Reagents
1. Silver nitrate (AgNO3) (0.1N): Dissolve 4.25 g silver nitrate in distilled water and make the total volume 250 ml by shaking.
2. Potassium chromate (K2CrO4) indicator (5%): Dissolve 2.5 g potassium chromate in distilled water and make the total volume 50 ml by shaking.
Procedure
1. Salinity in estuarine and coastal water can be estimated by this method.
2. Take 10 ml of water sample in a 50 ml conical flask.
3. Add 3-4 drops of potassium nitrate indicator solution to it.
4. Titrate against 0.1N Silver nitrate till the yellow colour changes to brick red.

    1.    Salt affected soils;
    2.    Classification of salt affected soils; 
    3.    Formation of salt affected soils; 

Introduction

The need for water for human consumption and the industry keeps spiraling up in the cities in the wake of rural diaspora and urban sprawl. Groundwater is exploited at an unsustainable rate leading to depletion of aquifers. Worse still is the intrusion of salt water from adjoining coast into the aquifers leading to total wastage of remaining water.

Next to the mineral frame work the salts in the soil deserve consideration. In fact these salts constitute the main food material of plants besides what the root hairs can get out of the mineral framework in the form of basic materials already dealt with. We are, however, not concerned with the plant food materials, but salts that are in excess and which alter the properties of the soil to such an extent as to influence its behaviour profoundly.

Appendix - 1:
Physical Properties of Seed – bed / Puddled Soil

Tillage is the process of getting the soil ready for planting and maintaining it loose and weed-free while the crop is growing.  The main goal and core function of tillage are broken down into three phases:

1. Creating a suitable seedbed,
2.  Eliminating weed competition, and
3.  Enhancing the physical quality of the soil.

Soil testing is a useful tool that can help to ensure the efficient use of applied plant nutrients. Soil tests provide a means for assessing the fertility status of a soil, but soil tests do not provide a direct measure of the actual quantity of plant available nutrients in the soil. Instead, soil tests measure the quantity of a nutrient element that is extractable from a soil by a particular chemical extracting solution. The measured quantity of extractable nutrient in a soil is then used to predict the crop yield response to application of the nutrient as fertilizer, manure, or other amendment. As soil test levels increase for a particular nutrient, the expected crop yield response to additions of that nutrient decreases. A soil-testing programme has four phases as follows:

1.Collection of soil samples,

2.Chemical analysis of soil samples,

3.Calibration and interpretation of the results of chemical analysis, and

4.Nutrient/Fertilizer Recommendation.

Abstract
 
Mention about the Great Himalayan river of Vedic Period (10000-8000 BC) is found in ancient Indian literature. The river called as ‘Saraswati’ in India and ‘Hakra’ in Pakistan, was flowing with full majesty through Thar Desert around 6000 BC and dried up during 3000 BC due to climatic and tectonic changes in Himalayan region. The relict of this lost river is still found as palaeochannels in this region. Large numbers of archaeological sites of Harappan civilization occur along the dry river bed. 

Abstract

This paper sets out to describe key results and experiences obtained during the assessment of the crop response to water which varies strongly from crop to crop depending on its specific genetic characteristics and adaptation to ecological conditions. The specific crop water information for the different crops is compiled in a crop-based data base. This includes crop water parameters, yield response to water, as well as selection of economy crops. Existing natural water resources can sustain the crop production with high yield as well as economic value with minimal environment impact. Most of the crop varieties get all of its required water from rainfall- water but due to climate change there is always a demand of irrigation water from the ground water resources. Thus, there is a need to evaluate the crop water information in relation to ground water extraction as a source of irrigation.

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the buildup of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems. Introduction to image processing 
Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. It is a type of signal dispensation in which input is image, like video frame or photograph and output may be image or characteristics associated with that image. Usually image processing system includes treating images as two dimensional signals while applying already set signal processing methods to them.
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Now-a-days, image processing is among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too.

5.1 Introduction
The Earth’s atmosphere is a vast, dynamic, and interconnected system, constantly in motion and influencing every aspect of our lives. Accurately observing and understanding this complex system is fundamental to predicting weather, monitoring climate change, and responding to environmental challenges. While ground-based platforms provide invaluable high-resolutiondata at specific locations, theitr  spatial coverage is inherently limited. This is where satellite remote sensing emerges as a revolutionary tool in meteorology. By observing the Earth and its atmosphere from space, satellites offer an unparalleled vantage point, providing global and continuous coverage that is impossible to achieve with ground-based instruments alone. They can capture data from remote oceans, deserts, and polar regions, offering a truly holistic view of atmospheric processes.
Satellite remote sensing enables meteorologists to measure a wide array of atmospheric parameters, including temperature and humidity profiles, cloud properties (type, height, movement), wind fields, precipitation rates, and the distribution of atmospheric gases and aerosols. This wealth of data is crucial for initializing and validating numerical weather prediction models, tracking severe storms, monitoring long-term climate trends, and conducting fundamental atmospheric research. This chapter will delve into the fascinating world of satellite remote sensing in meteorology, exploring the principles behind how these orbiting observatories collect data, the various types of meteorological satellites and their specialized instruments, and the diverse applications of the information they provide for understanding and forecasting
our planet’s weather and climate.

Data collection is a tedious and time consuming work, particularly from remote places. So many times the collected data is noisy. But data is the base for future planning and management of water resources. Planning based on noisy data could lead to failure. The modern data collection and subsequent control system i.e. telemetry and SCADA, are introduced in this chapter.

Introduction

Rice is known as the grain of life, and is synonymous with food for Asians. In addition to being a staple food and an integral part of social rites, rituals, and festivals in all Asian countries, it has a medicinal value too, which was clearly recognized by the medicine systems of the region. Ancient Ayurvedic treatises laud the Raktashali red rice as a nutritive food and medicine. The medicinal value of other rices such as Sashtika, Sali, and parched rice have been documented in the Charaka Samhita (c. 700 BC) and the Susruta Samhita (c. 400 BC), in the treatment of various ailments such as diarrhea, vomiting, fever, hemorrhage, chest pain, wounds, and burns.

According to late Dr. Richaria, the well known rice scientist, 4, 00,000 varieties of rice existed in India during the Vedic period. According to his estimates, even today 2, 00,000 varieties of rice exist in India - a truly phenomenal number. Farmers in every part of country have a deep knowledge of their own rice varieties, This has enabled them to harvest a crop even under the most severe stress situations. Traditional paddy varieties are location specific and has its own advantages viz. saline tolerant, drought tolerant, pest tolerant, used for specific purpose such as roofing material for houses etc.

It has been repeatedly proving that Science& Technology that is close and in harmony with nature is proving winner in the long run. If the past 60 years led to the Green Revolution in agriculture which often became Greed Revolution, the next 60 year should be the emergence of love for nature, diversity and Sustainable Green Revolution. The two goals – Sustainability and Productivity are inter-related.

It is also said that the main difference between the past and today is that our problems are truly global. We are also aware that globally and nationally the rich-poor divide is growing and though world produces enough food, nearly 800 million people remain food insecure. Among 8 millennium goals, eradication of poverty and hunger and environmental sustainability are perhaps the most prominent and complimentary.

This chapter introduces the scientific, biological, chemical, and economic importance of medicinal and aromatic plants associated with forest and allied systems. It examines their classification and diversity, chemical constituents, harvesting practices, processing, value addition, and extraction techniques. The chapter highlights the potential of these plants as valuable biological resources and explores approaches for their productive utilization. Sustainable harvesting, appropriate processing, and value addition are important for improving their economic potential while supporting conservation and responsible management of plant resources.

1. The unconsolidated mineral or organic material on the immediate surface of the Earth that serves as a natural medium for the growth of land plants. - Soil Science Society of America

3.1 What is environment ?

The word environment is derived from a French word “environne” means to encircle or surround. Environment is defined as the circumstances or conditions that surround an organism or group of organisms. It is also defined as the complex of social or cultural conditions that affect an individual or community. Since humans inhabit the natural world and they build social, cultural and technological world, all constitute important parts of our environment.

Introduction

Attaining food and nutritional security for a growing population and reducing poverty while sustaining agricultural production under the current scenario of depleting natural resources, increasing cost of inputs and impending negative impacts of climatic change are the major challenges the country is currently facing (FAO, 2009). In addition to these challenges, decline in soil organic matter, consequent deterioration of soil health, large scale erosion of fertile top-soil are the other bottle-necks for increasing agricultural productivity from a finite land resource base. These constraints are evolving mainly due to: (1) intensive tillage induced soil organic matter decline, soil structural degradation, water and wind erosion, reduced water infiltration, surface sealing and crusting, soil compaction, (2) insufficient return of organic material to soil owing to prevalent crop residue removal and burning (Prasad et al., 1999) and (3) mono-cropping. Conservation agricultural (CA) is a panacea to address these challenges the farming community of this country is presently facing. The CA system incorporates a holistic approach to agriculture that results in improved soil health and water quality, lower water and nutrient consumption, reduced soil loss, strategic land use and healthy biodiversity. It involves minimum soil disturbance, permanent soil cover through crop residues or cover crops, and crop rotations for achieving higher productivity (Hobbs et al., 2008).

INTRODUCTION

The state of Manipur is located at the easternmost border of India and is characterized by flat valleys comprising of four valley districts (Bishnupur, Imphal East, Imphal West and Thoubal) surrounded by five mild to steep slope hill districts (Chandel, Churachanpur, Senapati, Tamenglong, and Ukhrul). The state is geographically bordered by Nagaland in the north, Burma in the east, Mizoram in the south and Assam in the west. The total geographical area of the state is 22, 327sq. km divided into 20, 089sq. km in the hills and 2, 238sq. km. in the valley. More than 98% of the population in the hill is in rural areas whereas around 60% of the population in the valley is in rural and around 40% are in the urban areas.

The AAS Unit, Imphal is located at 24045' N latitude, 93054' E longitude at an altitude of 774m above msl. The agro-climatic zone of the state covers sub tropical plain zone, sub tropical hill zone, temperate hill zone and sub temperate zone.

Introduction

Conservation of biodiversity and mitigation of global warming are two major environmental challenges today. In the context of climate change and the global carbon cycle, the relationship between plant biodiversity and soil organic carbon (SOC) sequestration has become a subject of considerable scientific interest. The Earth’s terrestrial vegetation plays a pivotal role in the global carbon cycle. Not only are tremendous amounts of carbon stored in the terrestrial vegetation, but large amounts are also actively exchanged between vegetation and the atmosphere. Anthropogenic perturbations exacerbate the emission of CO₂ from soil caused by decomposition of soil organic matter (SOM) or soil respiration (Schlesinger, 2000). The emissions are accentuated by agricultural activities including tropical deforestation and biomass burning, plowing, drainage of wetlands and low-input farming or shifting cultivation. In addition to its impact on decomposition of SOM, macroclimate has a large impact on a fraction of the SOC pool which is active. Conversion of natural to agricultural ecosystems increases maximum soil temperature and decreases soil moisture storage in the root zone, especially in drained agricultural soils (Lal, 1996). Thus, land use history has a strong impact on the SOC pool (Pulleman et al., 2000). The dynamic relationship between plant biodiversity and SOC depicts that any land use practices that increase vegetative cover, or reduce its removal, could have an influence on the global carbon budget by increasing the terrestrial carbon sink.

Seepage is the movement of water through the soil body and foundation of a hydraulic structure. In an earthen dam, uncontrolled seepage may produce excessive water loss, high pore-water pressure, slope instability, internal erosion, piping, or complete structural failure. Seepage analysis therefore determines the quantity and path of water movement and helps engineers design filters, drains, impervious cores, cut-offs, and other protective components. This chapter explains the objectives and importance of seepage analysis, Darcy’s law, permeability, seepage velocity, phreatic lines, flow nets, equipotential lines, and flow channels. It also examines seepage forces, piping conditions, exit gradients, and methods for improving dam safety.

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

The research locale is a crucial aspect of any research study because it can have a significant impact on the data collected, the results obtained, and the conclusions drawn from the research. The selection of the research locale should be based on the research questions, objectives, and methodology of the study. The characteristics of the research locale such as its physical, social, cultural, and economic attributes, and the population being studied, can affect the research outcomes.

To accurately understand and interpret the data, used in social science research, it is necessary to have a thorough understanding of the attitude, behaviour and characteristics of the individuals who live in the area being studied. This section of the research critically examines the socio-demographic background of local people in a rural setting. The research setting refers to the context in which the research is conducted, and it plays a crucial role in shaping the interplay of different factors and components that influence the study’s outcomes. The research setting is essential because it provides a contextual framework that helps to inform the researcher’s interpretation of the data. By understanding the characteristics and context of the research area, the researcher can more accurately conceptualize and interpret the data, leading to a more nuanced understanding of the implications and behavioural complexities of the individual and the larger community.

1. Introduction

Arid lands, the “dry domains”, cover ~ 32 % of the continental surface of the world. This geographical region is defined by low precipitation and usually highly variable rainfall patterns (Heathcote, 1983; Groombridge, 1992; Watson et al., 1995). These lands are extremely diverse with respect to landforms, soils, fauna, flora, water balance, human activities and the binding element of all arid lands.

Abstract
 
Within the time scale for soil formation, humans appeared to the planet within the last 1 million years. They shifted their lifestyle from hunter-gatherer to nomadic animal husbandry and finally to cultivation of domesticated crops only within the last 10,000 yrs. Earlier, these crops were cultured in a semi-nomadic style as land was plentiful, so when soil lost its productivity, people abandoned it in favor of new-land. Gradually, as land became scarcer and population exploded, agriculture evolved to continuous cropping of the same parcel of land. This discovered the major concern for soil health. As organic matter is key to soil health, humus is the last stage of organic matter decomposition. Humus is a complex aggregate of dark brown colored amorphous substances, which have originated during the decomposition of plant and animal residues by microorganisms, under aerobic and anaerobic conditions. Humus and living microorganisms are connected by numerous ways which must be appreciated in order to understand the origin and nature of humus. Our attempt is to narrate the role of organic matter and vital role of microbes in humification process.

Abstract

Sabatayn basin is one of the two mature basins producing oil and gas in Yemen. Due to the recent decline in oil production, the exploration activities have been extensively concentrated mainly on these two basins (Sabatayn and Sayun-Masilah). So, this present study has been conducted to evaluate the exploration potential of the remaining trap areas within these mature basins. Fractured basement of reservoir becomes very attractive exploration target in Yemen recently, after the recent discoveries of basement fractured oil fields. As the evaluation of basement traps needs time and fund, to reduce the same, this research study uses a new technique which was devised with the help of Geoinformatics by analyzing the surface and subsurface lineaments effectively. In this study, a satellite image Enhanced Thematic Mapper (ETM+7) based analysis was conducted for extracting surface lineaments, and for the subsurface lineaments, the basement lineaments were extracted from seismic, magnetic and gravity data. An orientation analysis of these surface and subsurface linear features was performed to detect the basic structural grains of the study area. The correlation between these structural grains and subsurface oil and gas traps was performed to understand the connectivity to the reservoirs. This paper discusses in detail about the same and the importance of using surface and subsurface lineament analysis for delineating hydrocarbon reservoirs in the study area.

Modeling is heart of hydroinformatics. Simulation modeling became integrated part of modern science to justify feasibility of selected option among various competitive alternatives. Simulation modeling is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time. This chapter describes basic concepts, model simulation, model variables, modeling process, model performance etc. in brief.

Introduction

Crop models can be broadly classified into two categories: dynamic or process-based crop simulation models (CSMs), and reduced form statistical models. Since simulation models are based on various plant-related processes, they develop or alter in tandem with plant growth (Chetty 2009); as a result, they are inherently nonlinear (Olmstead 2009). To run, calibrate, and validate, they require a significant amount of date input. However, statistical models are helpful in situations where there is a lack of data because they describe the relationships between a subset of variables while keeping the others constant. (Chetty 2009).

ABSTRACT

The disposal of industrial effluents in rivers causes groundwater contamination. It is necessary to assess how these contaminants are likely to migrate in the groundwater system. In the present study, an attempt is made to study the migration of contaminants in the groundwater of Palar River basin in Tamil Nadu. Simulation of the contaminants was carried out by using numerical methods. The finite difference computer code MODFLOW (Modular 3-d finite difference flow) and MT3D (Mass transport 3-d) with Groundwater Modelling System (GMS) as pre and post processor was used to simulate the groundwater flow and transport in this study. The simulation was done in transient state for a time period of one year. It predicted the migration of plume of chlorides in groundwater. The changes in the distance and concentration rate of chlorides are identified for a particular period. Modelling provides a useful basis for contaminant transport studies, as well as in the evaluation of the hydrogeologic parameters relevant in the context of the transport problem.

Introduction

Site-specific nutrient management (SSNM) is the dynamic, field-specific management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling throughsoil–plantsystems(Sarkar etal.,2017).Thisnewmethodofnutrient recommendationsisprimarilybasedontheinitialnutrientstatus(INS)from thesoil andthecrop’sneedfornutrients toachieve thedesiredyield.

The ancient partnership between agriculture and weather forecasting, initially rooted in observational knowledge and rudimentary tools, has been fundamental to sustaining human populations for millennia. Traditional agricultural meteorology has diligently documented climate patterns, analyzed weather phenomena, and established empirical relationships between meteorological variables and crop yields. This foundational knowledge remains invaluable. However, the stark realities of the 21st century, dominated by the accelerating and often unpredictable impacts of climate change, necessitate a radical departure from conventional thinking. The comfortable reliance on historical data and linear projections is increasingly inadequate in a world characterized by unprecedented c limate variability, intensifying extreme weather events (as you‘ve extensively researched), and the urgent need for enhanced food security for a growing global population.
• The limitations of traditional agricultural meteorology become increasingly apparent when confronted with the complexities of climate change. For instance, relying solely on historical rainfall patterns for irrigation planning becomes precarious when those patterns are drastically altered by shifting climate regimes. Traditional pest and disease forecasting models, often based on established temperature and humidity thresholds, may fail to accurately predict outbreaks as these thresholds themselves are disrupted by climate anomalies. The need for real-time, localized, and predictive insights into the dynamic interplay between the atmosphere and agricultural systems has never been more critical.
• This is where the transformative power of emerging technologies steps in. The next generation of agricultural meteorologists stands at a pivotal juncture, tasked with leveraging advancements in diverse technological domains to revolutionize the field. Consider the sheer volume of data now available from a constellation of sources – weather satellites providing high-resolution global coverage, dense networks of ground-based sensors capturing hyperlocal environmental conditions, agricultural drones offering detailed field-level monitoring, and IoT devices collecting real-time soil and plant health parameters. This deluge of information dwarfs anything

Abstract

The present study deals with the slope instability analysis of a vulnerable stretch of 16 km between Pratapgarh road intersection and Mahabaleshwar town, which is a part of the state highway-72, Satara district, Maharashtra, India. The highway remains closed during the monsoon seasons, due to the recurrent instances of cut slope failures. So the present research work was conducted with an aim to study the reasons, extent and mechanism of the cut slope failures along the road sections as well as optimization of tools for the assessment of road cut stability of the area. The 16 km stretch was divided into seven vulnerable sections (M1; near Mahabaleshwar - M7; near Poladpur) based on the geological, geometrical and structural attributes. The slope forming mass primarily consists of laterite, weathered basalt and fresh jointed basalt, which are interbedded with red bole at some sections. Limit equilibrium, finite difference and finite element simulation were performed for seven cut slope sections, using similar geometries and material strength attributes for all the analyzing techniques. The assessment was carried out for both dry and saturated conditions to emulate the monsoons. The factor of safety yielded through the limit equilibrium method was the most conservative of all the tools; while the finite element method gave the least conservative figures in terms of factors of safety, being close to the field observations, the safety factor results of finite difference methods were between the limit equilibrium and finite element methods. The cut slope sections composed of weathered basalts and laterites (M1, M2, M3, M6) were found most vulnerable to failure during rains, while the sections composed of fresh jointed basalts (M4 & M5), though overall stable, are susceptible to planar and wedge failures.

Abstract

The Himalayas of Himachal Pradesh is prone to landslides due to inherent geodynamic conditions, rainfall and anthropogeny. Besides multi-variate studies on the landslides of Himachal Pradesh, instrument based early warning system was attempted by taking Jhakri landslides as the test site. Various instruments viz; piezometer, vertical inclinometer, Rain gauge, etc. were installed and the paper discusses about the methodology adopted in the installation of the instruments and the observations made from the instruments. While Piezometer did not reveal much information, the Tiltmeter showed minor movements.

Introduction
1.1 Definition of Smart Energy Management (SEM)
Smart Energy Management is a smart practice that utilizes evolved and improved technologies for the analysis and enhancement of the energy systems in real-time. The main aim is to make energy production, distribution, and consumption more profi cient, affordable, and sustainable. SEM also focusses on minimizing the waste generated and environmental effects (Kataki et al., 2017). By the combination of advanced technologies such as Internet of Things (IoT), Artifi cial Intelligence (AI), Machine Learning (ML), optimization and transformation of bioenergy systems can be effectively achieved. This technology can also be used to monitor and control the bioenergy systems actively so as to complement the prerequisite of the implementation and operations in bioenergy plants successfully (Syed, 2024). The era of increased need for the renewable sources and alarming issues of climate alteration calls for the innovative energy management (Jones, 2017). It emphasizes the demand for SEM in bioenergy systems ensuring the production of sustainable, eco-compatible and structures bioenergy system. This chapter foreground the top causes for which SEM to be more vital for the future bioenergy prospects.SEM enables real-time monitoring and optimization which allows bioenergy plants and their production of energy according to the demand, weather conditions and availability of the feedstock (Swami et al., 2023). SEM also generates the way to regulate bioenergy systems to facilitate the cost, performance, and energy conversion efficacy by utilising sensors, statistics and cloud computing (Fig 1). The integration of SEM with bioenergy is specifically relevant because of the variable and complicated nature of biomass feedstocks which include forest and agricultural residues, and food waste (Rodrigues et al., 2022, Duca and Tascano, 2022). The variations such as, moisture, quality and energy density show the effect on the efficiency in the production of bioenergy. Smart technologies are used to monitor as well as optimize the energy production along with the real-time fluctuations in response to the demand, feedstock availability and its supply (Hao et al., 2022)
1.2 AI (Artificial Intelligence) ML (Machine Learning) and IoT
There are two major emerging technologies named AI and ML which have transformed SEM for bioenergy systems. These technologies ease predictive analytics, decision making and enhanced simple processes. By analysing the massive datasets gathered from bioenergy operations, AI systems are able to forecast future energy output, system efficacy and operational damage (Saju et al., 2025). Utilizing real-time data feed such as pH range, composition of feedstock, temperature etc, AI systems incorporated with bioenergy plants can forecast biogas generation in and anaerobic digestion system (Barasa, 2021). Algorithms of ML in SEM helps in analysing and enhancing decision making process by both live and historical data. It predicts the forecast pattern in energy output and modify the operational parameters. Consequently, through a continuous learning process over time, the forecast prediction and bioenergy system are becoming more and more efficient (Khan et al., 2022). Bioenergy systems such as feedstock transport, conversion processes, and energy distribution can have real-time data which are collected by IoT technologies, constituting sensors, actuators and other connected devices (Ahuja and Khosla, 2019; Wang et al., 2022). Intelligent energy management systems utilizing IoT and AI technologies can greatly improve the efficiency of energy distribution and use, contributing to more sustainable urban environments. According to Liew et al., 2021, sensors are the source of valuable insights for the operation of bioenergy because it helps the operators in make decisions about system performance and efficiency and gain a better knowledge of the system functions. Sensors are installed in bioenergy facilities to provide continuous monitoring of information such as temperature, moisture content, pressure, and gas composition using IoT. Figure 2 depicts the significance of AI, IoT and ML in renewable energy.

Introduction 
The importance of climate change, sustainable development, environmental concerns, and energy needs are gaining attention more than ever. The conventional energy procedures, such as solely dependent on fossil fuels are ended with greenhouse emissions, contributing to pollution and deficiency in natural resources. To overcome these straits, bioenergy has become the sustainable and promising alternative, also offering the declining nature of dependency on non-renewable resources and parallelly providing cleaner energy options (Khalid, 2024). Mere the production of bioenergy is insufficient to address the huge energy needs of the current situation. If sustainability is to be ensured means, then effective energy management is to be ensured. The newly developed smart energy management systems improve energy production, distribution, and consumption by utilizing cutting-edge technology like artificial intelligence (AI), data analytics, and Internet of Things (IoT) devices. Smart energy management is a notion that limelight on lifting the reliability and efficiency of energy systems. With the usage of current technologies, smart energy systems can audit real-time energy use, and energy demands and manipulate the power distribution accordingly. This required to improvising the potential of renewable energy (Hossain et al., 2024). Energy recovery is defined as the reusing of energy, that has the possibility to get waste in the following forms, heat, waste materials, gas and so on. In the bioenergy industry organic waste is converted into a suitable form of energy, which gives hybrid benefits like waste reduction and energy generation. Furthermore, a highly resilient and efficient energy system that meets sustainable development goals can be created by combining these recovery systems with intelligent energy management tools (Mariana et al., 2021). The investigation of how energy recovery technologies and intelligent energy management can improve the efficiency and sustainability of bioenergy systems is covered in this chapter. The potential of smart energy solutions to meet the increasing need for sustainable and renewable energy sources will be highlighted in this research through a thorough analysis of case studies, emerging trends, and existing technologies. A roadmap for the future of bioenergy will be provided, together with an identification of the main obstacles and possibilities that players in this quickly changing industry must contend with (Naeem, et al., 2024).

The fate of smoke from a wild land fire depends in large part on the airflow carrying it away from the fire which can involve a complex interaction of eddies that may occur near the ground or expand beyond the atmospheric mixed layer (Fig.11.1). The buoyancy of smoke and ambient atmospheric conditions determines how high and how quickly the smoke rises, and thus where it travels.
The smoke plume distribution is affected by number of factors as indicated below:
• Elevation: The smoke plumes at higher elevations disperse the smoke particles more vertically while plumes from lover elevations disperse more horizontally.
• Temperature: The large difference between the smoke and surrounding air affects the Plume dynamics. The buoyancy force increases the vertical velocity of the particles, while heat diffusion increases surrounding air temperature.
• Moisture: The presence of moisture can modify the distribution of smoke and chemistry of smoke particles.
• Topography: Topography can influence emissions that remain within the mixing zone.
• Atmospheric stability: Atmospheric stability can affect the distribution of smoke.