eChapters

Natural resources are the resources present naturally in the environment. These resources are used by human being for their survival. Minerals, building stones, forests, and various energy resources are known as natural resources. Natural resources have been extracted and utilized since ancient times but as the population increased, the demand of natural resources are increasing day by day.

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

3.1 Overview

3.1.1 Rice straw: What is it?

When harvesting paddy rice, rice straw is produced as a by-product. Approximately 0.7-1.4 kilograms of rice straw can be obtained for each kilogram of milled rice, depending on the variety, moisture content during harvest, and cutting length of the remaining plant stems. The rice straw is separated from the grains after the plants are threshed manually using stationary threshers or with modern combine harvesters, as shown in Figure 3.1.

Rice straw is a type of lignocellulosic biomass comprising cellulose, hemicelluloses, and lignin. Figure 3.2 illustrates that pretreatment can be used to separate these components. Unlike lignin, which is found in the cell wall, cellulose and hemicellulose are organic fibers.

Abstract

Climate change is a matter of great concern considering aquatic resources and the productivity. The likely impact of Climate Change is noted in the domain both in marine as well as estuarine capture fisheries, fresh water capture fisheries and freshwater aquaculture. Climate change is having a linkage with spread of fish diseases as increasing water temperature will shift the balance in favor of either the host or pathogen changing the frequency and distribution of diseases. There are a no of adaptation and mitigation measures in aquaculture in order to increase the productivity and properly maintain the survivability of many aquaculture species.

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.

5.1    Population Growth
    
5.2    Exponential Growth
    
5.3    Population Explosions
    
5.4    Over Population
    
5.5     The Causes of Rapid Population Growth
    
5.6    Ways and Means to Solve Over Population
    
5.7    Environment and Health
    
5.8    Family Welfare Programme
    
5.9    Human Rights
    
5.10    Child Welfare
    
5.11    Women Welfare

9.1 Introduction

Biomass conversion is a transformative process that harnesses the energy and chemical potential of organic materials derived from living organisms. It encompasses various methods of converting biomass, including agricultural residues, forestry by-products, algae, and even waste materials, into valuable products such as biofuels, chemicals, and energy. This process is a cornerstone of sustainable resource utilization and plays a critical role in addressing environmental concerns, reducing reliance on fossil fuels, and promoting a circular economy. Biomass conversion methods range from biochemical processes like fermentation and anaerobic digestion to thermochemical processes such as combustion, pyrolysis, gasification, and liquefaction. Each method employs distinct conditions, catalysts, and technologies to break down complex biomass structures and convert them into desired products.

For nations where agriculture is intrinsically linked to the rhythms of the monsoon, the need for effective climate guidance and resilient farming practices is particularly acute. The preceding chapters have likely underscored the profound impact of climate variability, especially the unpredictable nature of monsoon patterns, on agricultural productivity and the livelihoods of millions. This chapter turns its attention to the essential infrastructure development and enabling policy frameworks required to empower farmers in monsoon-dependent countries to navigate the uncertainties of their climate and build lasting prosperity. Given the unique challenges and opportunities presented by monsoon climates, this chapter will specifically explore the development of infrastructure that supports climate-resilient agriculture in these regions. This includes a focused examination of:
• Specialized Weather Observation and Forecasting Infrastructure for Monsoon Monitoring: Emphasizing the need for advanced systems dedicated to accurately monitoring monsoon onset, intensity, spatial distribution, and predicting extreme events like heavy rainfall and prolonged dry spells, which are critical for timely agricultural decisions.
• Communication and Dissemination Infrastructure Tailored for Monsoon-Related Risks: Focusing on effective and accessible channels to deliver critical monsoon forecasts, advisories on managing water excesses and deficits, and information on appropriate cropping strategies during and between monsoon seasons, reaching even remote agricultural communities.
• Infrastructure for Enhanced Water Management in a Monsoon Context: Addressing the dual challenges of managing excess water during intense monsoon periods (including flood mitigation and drainage systems) and ensuring water availability during dry spells through efficient irrigation, water harvesting, and groundwater management infrastructure.
• Data Management and Analysis Focused on Monsoon-Driven Agricultural Systems: Highlighting the importance of analyzing historical and real-time data related to monsoon patterns and their impact on specific crops and regions to generate localized and timely agricultural guidance.
The chapter will also critically analyze the enabling policies necessary to support climate-resilient agriculture within the specific context of monsoondependent nations. This will involve exploring:

The pursuit of a dependable and abundant harvest stands as the cornerstone of sustainable agriculture. However, this pursuit is increasingly challenged by the growing complexities and uncertainties of our changing climate. Chapter 9, “Harnessing Climate Intelligence for Assured Harvest,” delves into the critical role of climate analysis in navigating these challenges and securing predictable yields. This chapter outlines a strategic approach to achieving “assured harvest” by effectively leveraging “climate intelligence.” This intelligence encompasses a deep understanding of historical climate patterns, real-time weather monitoring, and future climate projections. By strategically analyzing this wealth of information, we can move beyond reactive measures towards proactive and informed decisionmaking across the agricultural lifecycle. Our strategy to harness climate intelligence for assured harvest will focus on several key pillars:
• Deciphering Climate Variability and Change: We will explore the nuances of both short-term weather fluctuations and long-term climate trends, identifying their specific impacts on crop growth, development, and potential losses. This involves understanding critical parameters like temperature, rainfall, humidity, solar radiation, and the frequency of extreme weather events relevant to specific crops and geographical regions.
• Assessing Climate-Related Risks: A crucial element of this strategy involves identifying and quantifying the risks posed by climate variability and change. This includes analyzing the probability and potential severity of events like droughts, floods, heatwaves, cold spells, and shifts in pest and disease patterns.
• Developing Climate-Smart Agricultural Practices: Armed with a robust understanding of climate risks, this chapter will explore a range of climatesmart agricultural practices designed to mitigate these risks and enhance resilience. This encompasses strategies related to crop selection, optimized planting and harvesting schedules, water management, soil health improvement, and integrated pest and disease management.
• Leveraging Technological Advancements: The power of climate intelligence is amplified by advancements in technology. We will examine the role of remote sensing, Geographic Information Systems (GIS), precision agriculture tools, and sophisticated weather forecasting systems

3.1 Chemistry of greenhouse gases (ghgs)

Greenhouse gases (GHGs) are atmospheric-gases that have capabilities to absorb and radiate-back the infrared (IR) radiation in the earth-atmosphere system (Figure 3.1). In this process the atmospheric-gases absorb and radiate- back IR radiation in lower atmosphere effectively emitting and absorbing heat energy heat energy. They maintain the heat budget of earth-atmosphere and keep our plant warm. If we typically see the chemistry of those gases, the key point is that they should have dipole moments. This is the property which makes them capable to absorb and radiate-back the range of IR radiation. In a molecule when valence-electrons are not equally shared in a bond between the two atoms, a slight positive charge is developed above one atom and a negative charge on the other atom. That charge difference of the different atoms in a molecule generates the dipole moment. For instance, in water vapour (H₂O- vapour), an unequal sharing of electrons and or separation of charge causes dipole moment. In H₂O, the centre has a positive charge at the hydrogen atoms and negative charges are located on the oxygen atom. The magnitude of dipole- charges, vibration and stretching of O-H bond generates certain frequency of the oscillating dipole moment that actually regulates the absorption capacity of IR radiation by H₂O vapour.

5.1 Population Growth

In 2000, the world had 6.1 billion human inhabitants. This figure may rise to more than 9 billion in the next 50 years. For the last 50 years, world population multiplied more rapidly than ever before, and more rapidly than it will ever grow in the future. Anthropologists believe the human species dates back at least 3 million years. For most of our history, these distant ancestors lived a precarious existence as hunters and gatherers. This way of life kept their total numbers small, probably less than 10 million. However, as agriculture was introduced, communities evolved that could support more people.

World population expanded to about 300 million by A.D. 1 and continued to grow at a moderate rate. But after the start of the Industrial Revolution in the 18th century, living standards rose and widespread famines and epidemics diminished in some regions. Population growth accelerated. The population climbed to about 760 million in 1750 and reached 1 billion around 1800.

In 1800, the vast majority of the world’s population (86 percent) resided in Asia and Europe, with 65 percent in Asia alone. By 1900, Europe’s share of world population had risen to 25 percent, fueled by the population increase that accompanied the Industrial Revolution. Some of this growth spilled over to the Americas, increasing their share of the world total. The 2000 growth rate of 1.4 percent, when applied to the world’s 6.1 billion population, yields an annual increase of about 85 million people. Because of the large and increasing population size, the number of people added to the global population will remain high for several decades, even as growth rates continue to decline.

Between 2000 and 2030, nearly 100 percent of this annual growth will occur in the less developed countries in Africa, Asia, and Latin America, whose population growth rates are much higher than those in more developed countries.

The growing demand for sustainable materials has encouraged the development of green composites based on bio-based polymers and renewable reinforcements. This chapter explores advanced carbon nanomaterials, including graphene, graphene oxide, carbon nanotubes, carbon nanofibers, and carbon quantum dots, as functional reinforcements for green composites. It discusses their synthesis, functionalization, incorporation into biopolymer and bio-based matrices, and influence on mechanical, electrical, thermal, and barrier properties. Particular attention is given to green synthesis approaches using biomass-derived resources, surface modification, dispersion, environmental considerations, scalability, cost, and life-cycle assessment. The chapter also considers future opportunities for multifunctional and commercially viable sustainable nanocomposites.

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

Arid Horticulture denotes cultivation of fruits and vegetables under dry and arid regions. Arid region in India is stretched over 39.54 million ha (31.71 million ha is under hot arid region and 7.38 million ha under cold arid region) comprising about 12.02% of the total area of the country. Most of the precipitation in north-western arid region takes place from July to September. Occasional frost has also been a grave issue for horticultural crops, especially, aonla and moringa (Dhandar and Saroj, 2004). The region is exemplified by low and erratic rainfall with extremes of temperature (1–48°C), high wind velocity and sandy soils (Bhandari et al. 2014). Growing of annual crops restricted only a particular season of the year is the merely source of livelihood for the inhabitant of these region. These crops often encounter abiotic stresses like drought, hot and desiccating winds during their growth period leading to poor productivity. These regions have tremendous potential for cultivation of perennial fruit trees.

Two-dimensional nanomaterials have expanded the possibilities for designing advanced green composites with tailored multifunctional properties. This chapter focuses on MXenes and nanosilicates, including laponite, montmorillonite, and halloysite, as important alternatives and complements to graphene-based nanomaterials. Their integration into green composite matrices can influence mechanical strength, thermal stability, barrier characteristics, surface functionality, and other performance parameters. The chapter examines the development of advanced green composites based on these nanomaterials and considers their potential for sustainable material design. Particular attention is given to the relationship between nanoscale structure, matrix interaction, and composite performance, highlighting opportunities for multifunctional and environmentally responsible material systems.

Introduction 
The fundamental goal of agriculture is to harness the earth‘s resources to produce food, feed, fiber, and fuel to sustain a growing global population. At the heart of this endeavor lies the concept of crop production, a complex interplay of genetic potential, environmental factors, and human management. This chapter, Potential and Achievable Crop Production, delves into the crucial distinction between what a crop can produce under ideal circumstances and what it actually produces in the farmer‘s field. Understanding this dichotomy is paramount for addressing global food security challenges, optimizing resource utilization, and developing sustainable agricultural practices. We will begin by exploring the theoretical maximum yield, often termed "potential production," which represents the inherent capacity of a crop variety to produce biomass and yield when all limiting factors are eliminated. This potential is primarily dictated by a crop‘s genetic makeup and the prevailing climatic conditions, such as solar radiation, temperature, and carbon dioxide concentration. Subsequently, we will shift our focus to achievable production, the real-world yield obtained by farmers. This actual yield invariably falls short of the potential due to a myriad of biotic (pests, diseases, weeds), abiotic (water scarcity, nutrient deficiencies, extreme temperatures, poor soil health), and socio-economic (lack of access to inputs, knowledge, or markets) constraints. The disparity between potential and achievable yield, known as the "yield gap," represents a significant opportunity for enhancing agricultural output without necessarily expanding cultivated land. By meticulously examining the factors that contribute to this gap, we can identify targeted interventions and innovative strategies to bridge it, thereby moving closer to realizing the full productive capacity of our agricultural systems. This chapter will therefore provide a comprehensive framework for understanding the science behind crop yields and the practical approaches required to optimize them for a more food-secure future.

Key facts

    
•    Arsenic (As) is a metalloid, with properties intermediate between those of a metal and a non-metal. Arsenic can exit in the environment and biological systems in various inorganic and organic forms (species). These are arsenite - AsIII, arsenate - AsV, monomethyl arsonic acid (MMAA), dimethyl arsinic acid (DMAA), arsenocholine, arsenobetaine and arsenosugars. 
    
•    Arsenicals have a wide range of toxicity. In general, inorganic arsenicals are more toxic than oragnoarsenicals to biota and trivalent arsenic (arsenite) is more acutely toxic than pentavalent (arsenate) form in the following orders:
    
    Arsenous acid (AsIII) ®  arsenic acid (AsV) ® MMAA ®DMAA ® arsenocholine ® arsenobetaine.
    
•    Under oxidizing and aerobic conditions, the predominant form of arsenic (As) in soil and water is arsenate (AsV), whereas under anaerobic and reducing/or waterlogged conditions, arsenite (AsIII) predominates.
    
•    Inorganic AsIII and AsV are the major As species in groundwater, soils, terrestrial plants, whereas the main species found in marine animals is Arsenobetaine (AsB), which is considered to be non-toxic.
    
•    The LD50 values of different arsenicals show that arsenite (AsIII) is most toxic (LD50 range 15-42 mg/kg) and arsenobetaine is least toxic (LD50 > 10,000 mg/kg).

Introduction

Cooking accounts for a major share of energy consumption in developing countries. Fifty per cent of the total energy consumed in India is for cooking (Fritz 1981). Most of the cooking energy requirement is met by non-commercial fuels such as firewood (75%), agricultural waste and cow dung cake (25%) in rural areas. The fuel wood requirement is 0.4 tons per person per year in India. In rural areas firewood crisis is far graver than that caused by a rise in oil prices. Marginal villagers have to forage 8 to 10 hours a day in search of firewood as compared to 1 to 2 hours ten years ago. One third of India’s fertilizer consumption can be met if cow dung is not burnt for cooking and instead is used as manure. The cutting of firewood causes deforestation that leads to desertification. Fortunately, India is blessed with abundant solar radiation (IMD, 1985). The arid parts of India receive maximum radiation i.e. 7600-8000 MJ m^-2 per annum, followed by semi-arid parts, 7200-7600 MJm^-2, per annum and least on hilly areas where solar radiation is still appreciable i.e. 6000 MJm^-2 per annum. Therefore, solar cookers seem to be a good substitute for cooking with firewood.

Principle of cooking

The different methods of cooking of food are boiling, frying, roasting, and baking. For boiling of rice, lentils etc. the temperature of food being cooked is about 100° C while for other methods, high temperatures are required. Heat is supplied at the bottom of the vessel for frying and boiling purposes in conventional cooking. Roasting and baking is generally performed on open fire or in ovens, wherein food is surrounded by hot surfaces and heat is transferred to the food by radiation and convection.

Water is the lifeblood of agriculture, fundamentally dictating the success or failure of crop production worldwide. While sufficient and timely rainfall, or adequate irrigation, ensures healthy plant growth and bountiful harvests, an imbalance in the water supply can quickly escalate into a crisis. Among the various forms of drought—meteorological, hydrological, and socio-economic - agricultural drought stands as the most direct and immediate threat to food security and farm livelihoods. It represents a critical shortfall in the moisture necessary to sustain crop growth, leading to reduced yields, compromised crop quality, and often, complete crop failure (Fig. 9.1). effective mitigation and adaptation strategies. This chapter will delve into the precise definition of agricultural drought, differentiate it from other drought types, and critically examine its various forms based on the timing of water deficit during a crop’s life cycle. By dissecting the characteristics of early season, mid-season, terminal, and apparent permanent droughts, we aim to provide a comprehensive understanding of how water scarcity at different stages impacts crop physiology, productivity, and the broader agricultural landscape.

Introduction

Climate plays a crucial role in shaping the agricultural landscape of a region, and the changing climatic conditions pose a significant threat to global food security. In developing countries in Asia and Africa, the primary challenges to realizing optimal crop yields are declining soil fertility, particularly soil organic carbon and drought. The unchecked emission of greenhouse gases is expected to lead to a considerable rise in the average global temperature by 6°C in the next century, primarily driven by activities such as fossil burning and urbanization. Over the past centuries, there has been a noticeable increase in the concentration of CO2 and CH4 , with up to a 35% expected rise in nitrous oxide due to the improper use of nitrogenous fertilizers. Climate models predict a substantial impact on agricultural productivity in the 21st century, with rising temperatures and more frequent extreme weather events (NASA, 2022). To maintain agricultural productivity, it is imperative to understand the effects of increasing temperatures, shifting precipitation patterns, and rising CO2 levels on soil health. It is noteworthy that climate is one of the key factors influencing soil formation, alongside four other factors. Temperature and precipitation directly affect soil formation by providing the necessary conditions for weathering and biomass production. The sum of active temperature and precipitation evaporation ratio are critical parameters that determine energy consumption for soil formation, water balances in soil, organic-mineral interactions, and the transformation processes within the soil. However, human activities such as population growth, deforestation, disruption of marine ecosystems, and the greenhouse effect contribute to climate change, leading to denudation, depletion of soil organic matter, destruction of soil structure, erosion, and desertification, ultimately causing a decline in soil health and environmental degradation (IMD, 2021). Despite being a gradual process, climate change, characterized by small, long-term changes in temperature and precipitation, significantly influences various soil processes, particularly those related to soil fertility. The anticipated effects of climate change on soils primarily involve alterations in soil moisture conditions, increased soil temperature, and elevated CO2 levels. Understanding the consequences of climate change on soil health is crucial, as it has far-reaching impacts on agriculture, biodiversity, carbon cycling, and ecosystem resilience in the face of an evolving climate (Nunez et al., 2019).