eChapters

Introduction

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

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.

The edible speciality mushrooms are mushrooms that are generally not grown and available commonly in the market. Any mushroom other than popularly grown white button mushroom (Agaricus bisporus) falls under the specialty category, and these mushrooms are generally not available over the counter or in departmental stores in India or other countries in the world. The mushrooms under this group include the different types of commonly grown Oyster mushrooms like grey, black, white, pink, yellow oyster mushrooms, King Oyster mushroom, Shiitake mushroom, Black Ear mushroom, Shimeji mushroom, Enokutake mushroom, Milky mushroom, the Portabella brown, Cremini mushroom , Straw mushrooms and others. This paper will describe in detail biology and cultivation of these mushrooms as grown under controlled environment conditions or under seasonal growing conditions, where ever possible.

13.1 Introduction
Having navigated the foundational statistical methods, advanced analytical techniques, visualization strategies, and the intricate world of Numerical Weather Prediction and Climate Data Analysis, we now arrive at a pivotal point in the chapter “Specialized Applications and Future Trends.” This chapter is dedicated to exploring how the comprehensive understanding and methodologies developed in previous chapters are being applied to address some of the most pressing environmental and societal challenges of our time. Furthermore, we will look ahead to the emerging technologies and interdisciplinary approaches that are shaping the future of atmospheric and climate science.
The atmosphere is not an isolated system; it profoundly influences, and is influenced by, human activities, energy systems, water resources, and urban environments. Therefore, the insights derived from meteorological and climate data are increasingly being tailored and integrated into highly specialized applications that directly impact public health, economic sustainability, and urban planning. This goes beyond traditional weather forecasting to provide actionable intelligence for complex, real-world problems.
In this chapter, we will delve into several key areas where atmospheric science is making critical contributions:
• Air Quality Analysis and Pollutant Measurements: We will explore how meteorological principles are applied to understand the transport, dispersion, and transformation of atmospheric pollutants. This includes the methods for measuring various pollutants and the sophisticated dispersion modeling techniques used to predict air pollution levels and their spread, crucial for public health warnings and environmental regulations.

Abstract

While multiple species interact with each other in variable environments, they employ a variety of interaction strategies to withstand variation in environmental conditions. Phenotypic plasticity is one such mean allowing species to cope with environmental challenges often posed by the variation in consumer pressure, disturbances, and predation risks. In response to these and other environmental cues, expressed phenotypes can alter the sign and strength of interactions among species as these responses are often reflected in increased or decreased species competitive or facilitative abilities. Besides phenotypic plasticity, species interactions also depend on continued variation in abundances or densities of interacting species commonly termed as density-dependent interactions. Such density-dependence follows the law of diminishing returns, which means that while an individual species adds a density-dependent weight to the performance of other species, with increasing accounts, at a certain level of interaction it provides diminishing returns resulting in decreased population growth of the species. Thus, species interactions in variable environments hold two interdependent opposing forces: the growth-escalating and the growth-diminishing forces. The first one is resulted from adaptive phenotypic responses to variable environments and the second one is resulted from dynamic changes of abundances or densities of interacting species. Based on this conceptual framework, we describe community dynamics that are shaped by the balance of these two forces in variable environment. By analyzing these dynamics, we then seek underlying mechanism of Grim’s classic humped-back model of productivity-diversity relationship and also provide an alternative explanation for the intermediate disturbance hypothesis.

Keywords: Competition, Facilitation, Functional niche, Interaction continuum, Phenotypic plasticity.

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Introduction

According to FAO estimates, about 50% total geographical area in India is under various degraded hazards and about 2.1 million ha of land is getting degraded and deforested annually (Sehgal et al. 1998). Out of that underground water in arid and semi-arid areas is saline or sodic in nature. While spices, medicinal and aromatic plants may be a boon for these areas, which not only withstand under adverse conditions but also could be productive and helps in mitigating these problems. However, other crops are remain less or unproductive in these areas.

Spices are low volume and high value crops and have tremendous export potential, which is witnessed by several invasions faced by this Indian peninsula during ancient time. Medicinal and aromatic plants have unequally market potential with the world demand of herbal products growing at the rate of 7 percent per annum (Aishwath and Tarafdar, 2007). Seed spices and aromatic plants are the natural source of flavour, perfumes and fragrance widely exploited by essential oil industries in the world. Owing to considerably diversity of edaphoclimatic conditions ranging from temperate to tropical and arid to sub-humid that prevails in India and an array of medicinal and aromatic flora occurs in wild state. Many exotic essential oil bearing plants, including mints, citronella, geranium, patchouli, lavender and chamomile have been naturalized in India. As the country has emerged as one of the main producers of seed spices and some of the essential oils, theses plants are significant foreign exchange earners. Some of the aromatic plants especially grasses can sustain both biotic as well as abiotc stresses, even in the areas unsuitable for conventional crops (Aishwath et al., 2008). Most of the seed spices crops are seasonal crops and comes up well with limited moisture and nutrients. Therefore, underutilized natural resources can be utilized profitably by crop diversification.

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

Chapter 6 delves into the critical role of monsoon rainfall in shaping the economies and lives of numerous countries across the globe. While the concept of a country being only dependent on monsoons is a simplification, this chapter focuses on regions where the seasonal monsoon rains are the paramount source of freshwater for agriculture, industry, and daily life. These monsoon-dependent nations, predominantly located in South Asia, Southeast Asia, Australia, parts of Africa, and even areas of South America, experience a dramatic annual cycle of wet and dry periods dictated by the shifting monsoon winds. The arrival and intensity of the monsoon rains are not merely meteorological events; they are pivotal determinants of agricultural yields, food security, economic stability, and the overall well-being of billions of people. Given this profound dependence, accurate and timely forecasting of monsoon rainfall is of paramount importance. The second part of this chapter explores the current state of the art in monsoon forecasting. We will examine the sophisticated methodologies and technologies employed by meteorological agencies and research institutions worldwide to predict the onset, duration, intensity, and spatial distribution of monsoon rains. This includes an overview of empirical models leveraging historical climate data, the increasing reliance on complex dynamical models that simulate atmospheric and oceanic processes, and the integration of advanced statistical techniques like Bayesian methods and downscaling to enhance forecast accuracy and regional specificity. Understanding the strengths and limitations of these cutting-edge forecasting techniques is crucial for effective water resource management, agricultural planning, disaster preparedness, and ultimately, for building resilience in monsoon-dependent regions.

Experiment

An experiment is a planned inquiry to obtain new facts or confirm or deny the result of previous experiment. Such inquiry can aid in an administrative decision, such as recommending a variety, a feed, a cultural practices, a fertilizer, a pesticide or a fungicide etc.

Type of Experiments

    1.     Preliminary experiments : Here investigator tries out large number of treatments in order to obtain leads for future work. Here replications are not taken for such treatments tried e.g. number of pesticides for control of diseases - take single observation for each treatment and take decision that some of them worth trying for use.

Agriculture has long been intertwined with weather, where accurate forecasts are essential for planning everything from planting schedules to irrigation management, harvesting, and market timing. Over the decades, statistical methods have evolved from simple empirical models to sophisticated tools that integrate vast amounts of historical data, real-time observations, and remote sensing information (Fig. 4.1).
This chapter provides an in-depth exploration of the statistical techniques that underpin modern agricultural forecasting. In doing so, it examines their applications across various agricultural domains—including staple crops, fisheries, livestock, and horticulture/plantation systems—while also addressing the challenges and uncertainties inherent in weather prediction. Furthermore, the chapter discusses how the rise of big data and artificial intelligence (AI) is reshaping traditional approaches, particularly in the context of food production and agricultural resilience.
In the following sections, we explore
• Statistical Techniques: An overview of regression models, time-series analysis, and probabilistic methods used in weather forecasting.
• Applications in Agriculture: Tailoring forecasting models for crops, fisheries, livestock, and horticultural/plantation systems.

8.1 Introduction
Having explored the intricate processes of meteorological data collection, storage, and the crucial steps of quality control and preprocessing, we nowturn our attention to how this vast and meticulously  prepared information is actually analyzed and interpreted. This brings us to the realm of statistical methods, which are indispensable tools for making sense of the inherent variability, uncertainty, and sheer volume of meteorological data. Meteorological phenomena are complex, influenced by numerous interacting factors, and often exhibit chaotic behavior. Statistical methods provide the rigorous framework necessary to quantify these complexities, identify underlying patterns, assess relationships between different atmospheric variables, and ultimately, draw robust conclusions. From summarizing observed climate patterns to evaluating forecast accuracy, and from detecting long-term trends to predicting future weather events, statistics permeate nearly every aspect of modern meteorology.
This chapter will introduce the fundamental statistical concepts and techniques applied in meteorological analysis. We will begin with descriptive statistics to summarize and visualize data, then move to inferential statistics to draw conclusions about atmospheric processes from samples. We will delve into methods for understanding relationships, such as correlation and regression analysis, and explore techniques for analyzing time series data to identify trends and periodicities. Furthermore, we will touch upon spatial statistics for gridded data and the growing role of machine learning approaches. By mastering these statistical tools, meteorologists can transform raw data into meaningful insights, enhance predictive capabilities, and advance our scientific understanding of the Earth’s atmosphere.

8.1 Introduction

The knowledge of statistics and probability is very helpful for correct prediction of hydrological data and correct prediction of hydrological data is useful in giving good performance in all water resources projects. Since hydrological data are highly stochastic varying both in space and time, it is essential to predict them as correct as possible. The hydrologic evens do not depend on physical and chemical laws but depend entirely on nature. They cannot be predicted exactly by using any relationship but can be predicted at different probability levels by fitting suitable probability distribution functions. Application of statistics and probability in the field of hydrology can help to predict the hydrologic events in a better way. In hydrology, objectives of statistics are as follows (Suresh, 2008):

Rainfall, runoff, evaporation, and other hydrological variables exhibit considerable natural uncertainty and therefore cannot be treated as entirely deterministic phenomena. Statistical and probability methods make it possible to organize observations, describe their variability, identify relationships, estimate extreme events, and predict values associated with selected probabilities. Hydrological data may be analysed daily, weekly, monthly, seasonally, or annually and expressed through averages, maxima, minima, dispersion, skewness, and frequency. This chapter introduces statistical terminology, correlation, regression, probability distributions, plotting positions, goodness-of-fit tests, model-evaluation tools, reliability, standard error, and risk analysis. These techniques are essential for flood-frequency analysis, planning, and hydraulic design.

Abstract

Alleviating plant salt stress and remediating saline soils are of great economic interest. Beneficial microbes such as arbuscular mycorrhizal (AM) fungi, plant growth promoting rhizobacteria (PGPR), etc. are associated with many plant species including trees. These beneficial microbes can cope up with salinity and help the plants to survive in such soils. Diversity status of beneficial microbes from the samples collected from different salt affected areas was investigated by many researchers. They screened and identified the efficient PGPR isolates isolated from saline areas for plant growth hormone (IAA) production and phosphate solubilization. The significance and highlights of the recent research as well as the past understanding of beneficial microbes and their role in reclamation of saline areas is discussed in this paper.

Abstract

The arid Kachchh district, located at the western most part of the Gujarat State in India, is known for its large extent of area under wetland habitat, which is represented by numerous seasonal water bodies and coastal habitats. Vast stretch of grasslands also available in the northern border of the district, the Banni (23⁰ 19’ to 23⁰ 52’ N; 68⁰ 56’ to 70⁰ 32’ E), which encompasses an area of 2,617 km². There are many depressions in Banni, locally known as Dhandh (shallow lakes). During monsoon, rainwater gets collected in these depressions and form shallow lakes. About 34 smaller and bigger size Dhandhs are present in the Banni. Banni communities are highly dependent on the Dhandhs for various purposes. Due to the erratic rainfall in arid Kachchh, Maldharis is highly unstable for livestock in search of fodder and water during drought years. During the normal rainfall years, livestock from neighboring talukas of Kachhh and even from other districts depends on this grassland and Dhandhs. During last decade, the Banni grasslands have seen serious environmental changes due to the invasion of an exotic plant Prosopis juliflora and increasing the salinity are the major issues of Dhandh degradation. Thus, the Banni is gradually losing its capacity to sustain human and livestock population.

Main objectives of the present paper is to examine the local perceptions of wetland and grassland change, socio-economic status, and factors that cause degradation of the wetland and vegetation resources in the Banni grassland.

Data mining is a powerful technology which helps in extracting future trends and behaviour from large databases. Thereby it helps decision makers to make knowledge-driven decisions. One common form of real-world data is time series. The suitable models are required to analyze time series data. This chapter provides a short introduction to stochastic system, uncertainty, basic statistics time series analysis and some stochastic models.

Introduction

Livestock plays an important role in the sustainable livelihood of poor people of rain-fed agro-ecosystem, because of inherent risk involved in the crop farming due to uncertainty of rainfall and occurrence of recurrent droughts. They provide income and increased economic stability, and often the most important “cash crops” in small-scale mixed farming systems. Livestock sector in India is highly livelihood intensive and most rural households own livestock of one species or the other and earn supplementary incomes from them. Livestock holding is less iniquitous than land holding and income from livestock is more equitably distributed. The sector contributes 25.6 per cent of the value of output at current prices of total value of output in agriculture, fishing & forestry sector and in GDP, it was 4.11% in 2012-13 (19^th livestock census, 2012) therefore, development of the livestock sector is the critical pathway to rural prosperity

As per the 19^th livestock census, the total livestock population consisting of Cattle, Buffalo, Sheep, Goat, Pig, Horses & Ponies, Mules, Donkeys, Camels, Mithun and Yak in the country is 512.05 million numbers in 2012. The total Bovine population (Cattle, Buffalo, Mithun and Yak) is 299.9 million numbers in 2012 which shows a decline of 1.57% over previous census. In percentage basis distribution of livestock were: 37.28% were cattle, 21.23% buffaloes, 12.71% sheep, 26.40% goats and 2.01% pigs. The corresponding figures as per the 18th Livestock Census were 37.58%, 19.89%, 13.50%, 26.53% and 2.10%. The number of milch animals (in-milk and dry) in cows and buffaloes has increased from 111.09 million to 118.59 million, an increase of 6.75%. The number of animals in milk in cows and buffaloes has increased from 77.04 million to 80.52 million showing a growth of 4.51%. The Female Cattle (Cows) Population has increased by 6.52% over the previous census (2007) and the total number of female cattle in 2012 is 122.9 million numbers. The Female Buffalo population has increased by 7.99% over the previous census and the total number of female buffalo is 92.5 million numbers in 2012. The buffalo population has increased from 105.3 million to 108.7 million showing a growth of 3.19%. The total sheep in the country is 65.06 million numbers in 2012, declined by about 9.07% over census 2007. The Goat population has declined by 3.82% over the previous census and the total Goat in the country is 135.17 million numbers in 2012. The total poultry population in the country has increased by 12.39% over the previous census and the total poultry in the country is 729.2 million numbers in 2012. The total number of animals in milk in the country is 116.77 Million numbers.

Introduction

Most of the technological advances in the recent past in Indian Horticulture have been done for favourable agro-climatic regions. However, the vast and diverse land-scape of hot desert, arid, semi-arid and tribal area is not yet receiving logical thought for resource based horticultural development (Samadia and Haldhar, 2017). Although, the semi-arid regions of the country have now became potential hub for aonla, bael, ber, pomegranate, jamun, guava, fig and custard apple. There has been a steady increase in area and production of these fruits particularly under aonla, ber, custard apple and pomegranate in the country as a result of identification and development of suitable varieties and production technologies. The average productivity of these fruits in India is, however, low as compared to many developed countries. The main reasons for low productivity are old and senile orchards, low yielding varieties, poor orchard management and inadequate technological up-gradation and adoption by the growers. Presently, the continuing decline in the availability of cultivable land, rising energy and land costs together with the increased demand of fruit and fruit products, have given thrust to the concept of high density planting (HDP) in fruit crops. High density planting gives earlier production and return per unit area, shortens juvenility, eco-friendly, provides efficient land use and better use of resources like light, water and fertilizers, efficient pesticides application, besides, in this system the harvesting becomes easy(Mishra and Goswami, 2016). Among the factors such as cultivar, rootstock, quality of planting material and cultivation practices contribute to high yield of fruit trees, however, the number of trees^-1 is the most important factor which brings about radical increase in fruit production. Therefore, high density planting has great potential for increasing productivity in fruit crops (Mishra et al., 2003). The success of this technology in most of the fruit crops is dependent on the use of methods to control shoot growth and maximize light interception as the trees begin to bear fruit. The main aim of HDP is to achieve the twin requisites of productivity by maintaining a balance between vegetative and reproductive growth without impairing the plant health and fruit quality. The underlying principle of HDP is to make the best use of vertical and horizontal space per unit time and to harness maximum possible return per unit of inputs and natural resources.

Introduction

Energy conservation is the practice of using less energy to lower costs and reduce environmental impact.

A majority percentage of energy used in buildings is still from non - renewable and fossil fuel resources. Lighting and Appliances are affecting energy demand in domestic sector.

Reasons for increased energy demand

• Increase in electrical appliance ownership

• Increasing income level and reliable access

• Rise of Lighting and appliance loads

13.1 Introduction

Greenhouse gas (GHG) emissions have become an increasingly important topic worldwide due to their effects on global warming and climate change. The effects of GHG emissions on the ecological and socioeconomic vulnerability have already been noticed and will continue to grow regionally and globally in the years to come (IPCC 2007). Carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons, perflourocarbons, and sulfur hexafluoride are the important GHGs that are monitored by the United Nations Framework Convention on Climate Change (UNFCCC 2008). Global GHG emissions due to human activities (anthropogenic) have grown since the beginning of the industrial revolution with an increase of 70% between 1970 and 2004 (IPCC, 2007).

6.1 Introduction

Stratigraphy is the branch of geology which deals with the study of rock layers (strata) and layering (stratification) in their chronological order. It is primarily used in the study of sedimentary and layered volcanic rocks in order to interpret geological history and age. The term historical geology is also used as a synonym of stratigraphy but in a wider sense, it deals with the whole sequence of events that make up the history of the earth from its origin and age relations of rock strata, evolution through its life form, and structures. The scope of stratigraphy is very wide; it involves description and classification of layered sedimentary rocks and also of igneous & metamorphic rocks. The study involves correlation between different areas like lithological characters, fossil content, geophysical and geochemical properties including absolute radiometric dating.

6.2 Principles of Stratigraphy

Stratigraphy is based on the following few principles:

6.2.1 Law of superposition

In a sequence of horizontal sedimentary rock layers, the younger layers are on the top and the older layers are at the bottom. To put it another way, in a normal sequence, each bed is younger than the bed beneath it. Geologists today use it as the basis for a bed-by-bed analysis of geological history in a given area. This is important to stratigraphic dating, which assumes that an object cannot be older than the materials of which it is composed. This law known as "Law of Superposition" was first proposed by the scientist Nicolas Steno in the 17th century.

5.1 Introduction

A steam can be defined as a flow channel in to which the surface runoff flowing over the land surfaces and base flow/ground flow flowing below the ground surfaces meet. It receives the total flow from a specified basin. Streamflow representing the runoff phase of the hydrologic cycle is the most important basic data for hydrologic studies. Interestingly, stream flow is the only part of the hydrologic cycle that can be measured accurately. Stream flow is a historical data and like rainfall and runoff, it is a stochastic hydrologic variable. It is also called as discharge or rate of flow. Discharge is defined as the volume of water flowing through a cross section in a unit time.

Abstract

In order to estimate the irrigation need of the crop, stress degree day index computers from canopy and air temperature could be used. During rabi manon, two varieties (Seeta, Brassica Juncea) and (Benoy, Brassica Camperteris) were grown. The canopy temperature of the crop was measured on 45th, 60th  and 75th  days after sowing and stress degree day (SDDI) was also measured. About 67, 70 and 51 per cent activation between SDDI and dry matter production could be explained at 45, 60 and 75 DAS of the crop growth period. Reflectance by mustard canopy was 940 measured. Late sown crop reflected less radiation with the advancement of growth. Extinction coefficient was also measured and the increase in the value of extinction coefficient upon irrigation showed canopy development and better utilization of increasing radiation by the crop. 

Introduction

According to the Forest Survey of India (FSI) “all lands, more than one hectare in area, with a tree canopy density of more than 10% is defined as Forest” (FSI 2009). The status of forests and forest management systems contribute to the vulnerability of forests to climate change. Indian forests are extremely diverse and heterogeneous. Forests and Climate Change are intimately intertwined. Forests have regulated the climate, rain, groundwater, soil of the earth over millennia. Their transpiration act as a regulator of the balance of oxygen and carbon-di-oxide: the world’s forests and forest soils currently store more than one trillion tons of carbon – twice the amount found floating free in the atmosphere. The forest and soils are home of biological diversity.

27. STRIKE AND DIP

Outcrop : A part of the rock formation which crops out or exposed on the surface of the earth is called an outcrop.

The total area over which a particular rock unit occurs at the surface whether visibly exposed or not. The term outcrop is especially used for delination of such an area on a geological map.

Attitude : To study these outcrops their attitude help in better understanding the position of the bed on the ground surface or in field is attitude of a bed can be described with the help of two basic structural elements i.e. strike and dip.

7.1 Introduction

The earth's crust is made up of igneous, sedimentary and metamorphic rocks. Each variety of rocks exhibit some columnar joints in Igneous rock, characteristic structures like bedding in sedimentary rocks and fabrics like schistosity and gneissosity in metamorphic rocks.

As a result of tectonic activities in nature, rocks may suffer deformation, rupture, dislocation of beds (Fault) or other planar structures.. The whole study of such deformation is known as structural Geology. It has been observed that sedimentary rocks are most suitable for studying structures, because these rocks are deposited in quiet environment, and initially the beds arranged in horizontal position. As a result of stress due to kinematic movements/tectonism, the horizontal beds tend to deviate from their horizontal disposition to dipping position or get folded or faulted. Thus the rock develops various kinds of structures. There are following structures present in sedimentary rocks:

6.1. Data Interpretation and Visualization of West Bengal

Any kind of production management system or decision-making process is being organised in social ecology. No variable cannot behave discrete or free without having any kind of influence as well as impact from the vary operating ecosystem. That is how it is imperative to know and visualize the basic features in terms of its distributive characters. The distribution of features in variables through this networking and interactions ultimately contribute to a holistic visualization of social ecologization under study.

Table 6.1.1 represents the distribution of variables in terms of range i.e. maximum and minimum values, mean, standard deviation and coefficient of variation for Terai Zone, Old Alluvial Zone and New Alluvial Zone of West Bengal.

The average age (x1) of the respondent stands at 49.02. It denotes that so far as responsibility of agriculture and age base livelihood runs at the population so far as the respondent under study is peaks are at the hands of farmers approaching or passing through old ages. On the contrary the absence of young population or withdrawal of young age farmers from agriculture have got a serious implecation near and far. The cv value runs at 25.78 per cent to indicate that the distribution of variable is highly consistant.

The average of functional education (x2) of the respondent stands at 8.20.The variable education charecterization the social ecology speaks that the most of the respondents are having mid level school education.So, this has got spil over effect on the function and operation of other variables i.e. precisely the perception variables.The cv value stands at 43.69 per cent that’s good enough to infer that the distribution of the variable is highly consistent.

Introduction

Environmental problems have come to the forefront as one of the most important issues in transport policy. For a number of pollutants the road transport sector is one of the most significant contributors to environmental pollution. Vehicular emissions have been identified as one of the major contributors in deteriorating air quality in the urban centers. The problem has further been compounded by the concentration of large number of vehicles and comparatively high motor vehicle/population ratios in these cities. Substantial SPM, RSPM CO₂ and No_x are emitted. The problem of air pollution has assumed serious proportion in some of the major metropolitan cities of India. Therefore, CPCB has 296 monitoring stations all over India covering major cities.

The air pollution situation in most of the cities is still far from satisfactory. In India the major air pollution reason is automotive smoke and smoke from industries. New channels are also publishing pollution levels in selected metropolitan cities. The most polluted city in Asia is Pune. The SPM and RSPM levels are displayed at various squares to create awareness among the masses.

Smaller cities like Nanded, Parbhani and Yavatmal are not covered under National Ambient Air Quality Monitoring (NAAQM) scheme of Central Pollution Control Board (CPCB). The National Ambient Air Quality Standards for suspended particulate matters (SPM) are based on gravimetric measurement. Standards for Respirable suspended particulate matters (RSPM) are also based on gravimetric measurement. The development in terms of number of vehicles and building construction has also reached at the doors of these cities and towns. Nanded city having population of 5.5 lakhs has total number of Auto rickshaws more than 5,000. In the present study, Nanded city has been selected to check particulate matters and gaseous pollutants. Since, the city is one of the important Pilgrimage and commercial centre.

Real time weather data at different time intervals is very important for the crop weather studies, but through the agro-meteorological observatories such real time weather data are generally not collected. The acquisition of agro-meteorological data through conventional observatories requires services of trained personnel to collect, compile and creation of weather data base. With the advances in technology, weather observations are automatically recorded, stored and can be transmitted to the remote place with the help of Automatic Weather Station (AWS).

Abstract

Groundwater is one of the major sources of exploitation in arid and semi -arid regions of Rajasthan. To protect groundwater, its data on spatial distribution are important along with quantity. Analysis of quality of groundwater is also very important, because the physical and chemical characteristics of groundwater determine its suitability for agricultural, industrial and domestic usages. The importance ground water increases many fold for arid tracts like Western Rajasthan. Here quality of water is also matter of serious concern. Geostatistics methods are most advanced techniques for interpolation of Groundwater quality. The present study appraises the groundwater quality of western Rajasthan for agricultural purpose with GIS technology. Kriging Raster Interpolation technique of spatial analyst module in Arc GIS software has been used to generate the spatial distribution map of ground water for agricultural purpose. In western Rajasthan only 28% (58109 km2) of ground water falls under the category of good and is suitable for agricultural use. 

ABSTRACT

Trace Elements were analysed from water samples collected from bore wells from block 2, 5, 14, 26 and 29 at Neyveli Township. Lead Concentration has crossed the limit of ISI standard prescribed for drinking which is 0.100 ppm. Lead concentration in the order of 0.082 ppm at block 5 and 0.11 ppm in block 29 were observed. Cadmium concentration has also crossed the limit of ISI (0.010 ppm) for drinking purpose, which is 0.02 ppm in block 26. Chromium concentration has also crossed the permissible limit of ISI standards (0.05 ppm) for drinking, which is 0.098 ppm in Block 14.

The above elements when compared with ISI standards for drinking water have shown a slight increase in concentration of lead, cadmium & chromium. Concentration of lead in Neyveli basin may be attributed to basement geochemical sources influenced by physical and chemical weathering. High concentration of chromium and cadmium in the aquifer may be due to anthropogenic activity probably from effluents of closed briqueting & carbonization plant, which is due south of block 14.

Abstract

Conventional wisdom holds that variability in rainfall represents a significant variability of agriculture production. But urbanization of the cities on one hand and climate change on theother hand areaffecting the agriculture. Surprisingly, there have been very few analyses exploring the link between rainfall variability and urbanization on agriculture production. Therefore an attempt is made in this paper to know the affect of rainfall variability and urbanisation on the cropping area. For this purposeNDVI analysis is carried out in the catchments of Osmansagar and Himayathsagar Reservoirs by using the medium to high spatial resolution multi spectral data provided by remote sensing satellites, such as Landsat TM, IRS-1C/1D LISS-III and IRS-P6 LISS-III has been used for the years1989, 1994, 1998, 2002 and 2008 for Kharif season, 1975, 1990, 1994, 1998, 2003 and 2008 for rabi season which is collected from NRSC (National Remote Sensing Centre). From this analysis it is found that the increment is more in rabi season compared to kharif season due to the increment of non-monsoon rainfall intensities and ground water withdrawal structures. The increased utilization of the groundwater is causing the reduction of inflows to the reservoirs in the recent years because whatever the rainfall that occurs in the catchment, first utilised for meeting the depleted soil moisture and ground water consequently reducing the surface runoff.

Keywords : Climate change, Urbanisation, Inflows, Cropping area, NDVI

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

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

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

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

Introduction
Sugarcane (Saccharum officinarum Linn.) is a remarkable tall, perennial grass characterized by its thick, jointed stalks and long linear leaves. This remarkable plant, known for its ability to efficiently photosynthesize in warm climates, is a C4 plant. The propagation of sugarcane primarily occurs through stem cuttings, as it seldom produces flowers and seeds. It finds roots in regions with tropical and subtropical climates featuring warm temperatures, abundant sunshine, and well-drained fertile soil (Shukla et al., 2017). Cultivated for its sweet juice, rich in sucrose, sugarcane serves as the primary raw material for the production of sugar, ethanol, and various other valuable products. Its lifecycle comprises several growth stages, including germination, tillering, grand growth, and maturation, culminating in the harvesting of its valuable juice. Continuous advancements in botany and genetics have contributed to the improvement of sugarcane cultivation, enhancing yield and resistance to pests and diseases. The origins and domestication of sugarcane can be traced back to Southeast Asia, particularly in regions encompassing Papua New Guinea, Indonesia, and their vicinity. The domestication of sugarcane is believed to have commenced thousands of years ago when early human populations in these regions recognized the sweetness of the cane and began cultivating it for its edible properties. Over time, through selective breeding and propagation of plants with desirable traits, these early cultivators played a pivotal role in shaping the sugarcane we know today. This indigenous Southeast Asian plant spread to other parts of the world through trade and exploration. Historical records indicate that sugarcane found its way to India around 500 BC and reached China in the 6th century AD. Subsequently, it traversed the Middle East, the Mediterranean region, and eventually reached the Americas during the early voyages of exploration and colonization (Grivet et al., 2004). Christopher Columbus played a significant role in introducing sugarcane to the Caribbean islands during his second voyage in 1493, and from there, it made its way to other parts of the New World, including Brazil and various South American countries. The cultivation of sugarcane emerged as an essential industry in these regions, profoundly influencing their economic and social landscapes. The domestication and widespread cultivation of sugarcane have left an indelible mark on the global economy, trade, and human history. Today, sugarcane remains one of the most significant crops globally, with major producing countries, including Brazil, India, China, Thailand, and others. It stands not only as a pivotal

Sulfate can have harmful effects on aquatic life in freshwaters, sulfate cause osmotic stress or specific ion toxicity in aquatic organisms, especially in soft waters where Ca2+ and Mg2+ concentrations are low. Egg fertilization and early embryonic development were the most sensitive developmental stages of whitefish to sulfate (Karjalainen et al., 2021).
Sulphate (SO4 2-) may be estimated by either turbidimetric or gravimetric methods (APHA, 1995)
1. Turbidimetric method is more applicable in the range of 1- 40 mg Sulphate (SO4 2-)/ L
2. Gravimetric method is suitable if the concentration of Sulphate (SO4 2-) is above 10 mg/ L
Turbidimetric Method (Range 1 - 40 mg/L of sulphate) Principle
Sulphate ion (SO4 2-) precipitated with barium chloride (BaCl2) in an acetic acid medium in order to form crystals of barium sulphate (BaSO4).
BaCl2 + SO4 2-+ BaSO4 (precipitate)
The formation of these crystals is increased in the presence of an acetic acid solution containing sodium acetate, potassium nitrate, acetic acid and magnesium chloride. The suspended barium sulphate (BaSO4) is measured by spectrophotometer and concentration of sulphate (SO4 2-) is determined by comparison of the reading with a standard curve. Minimum detectable concentration is approximately 1 mg (SO4 2-) / L.

Sulphur (S) occurs in soil in both organic and inorganic forms. Inorganic S fraction may occur as sulphate (SO₄^2-) and compounds of lower oxidation state such as sulphide (S^2-), polysulphide (S_n^2-), thiosulphate (S₂O₃^2-) and elemental sulphur (S⁰). In well drained soils inorganic S usually present as sulphate. Under anaerobic condition (reduced or waterlogged soil) inorganic S mainly occur as sulphide and under extreme condition as elemental S form. Estimation of S carried out in two steps:

7.1 Summary

Never ever in the annals of human civilization, the importance of ecological resilience has become so important and imperative as we are perceiving now. Both the need and greed of food economics have driven the resource bases of our ecosystem, to a dilapidated and fragile status. By 2050 the world’s population will touch close to 9.5 billion and we have to scale up our food production 60% more then what it is right now. In India by 2040, we have to cross 450 million tons of our targeted food production to keep rhythm with population growth. By 2035 we are going to cross the population of China. Understanding the cascade of risks for climate change, as well as the vulnerabilities to these risks, is key to frame ways to adapt. Reducing vulnerabilities is key to reducing the net impacts on food security and nutrition and also to reducing long-term effects. Increasing resilience of food security in the face of climate change calls for multiple interventions, from social protection to agricultural practices and risk management..

When population pressure is soaring up, the need to produce more food from less and less available land and water, the issues of ecological resilience are coming up more swashbuckling and humongous. The aftermath of green revolution has contributed to achievement in food security on one hand, the other hand water has been contaminated and depleted, soil has been eroded with a sharp decline of organic carbon. We are losing 15 tons of topsoil per hectare per year and inviting a disruptive agro-ecosystem, the syndrome of which are quite vivid and prominent.

7.1. Summary
The planet rise at this moment cannot endure and conjure up any further stress and distress created by un-mindful modernization of agriculture. This situation has gone further complex with the unabated rate of urbanization. The natural resources which looked plenty some 50 years back, now is coming up something scant and degraded. We have to protect this planet; hence, we have to protect our agriculture by placing ecological resilience with its perfect balance and dynamics. In doing so, we need to manage and recycle energy, water, and biodiversity in such a way that the operating agro-ecosystem can perform for another centuries. This is a herculean task, it will only be possible when farmers and other citizens are participating elegantly and equally into the process, because farmers are not producing food for their own, rather they are feeding the entire civilization. Every year 15-20 tonnes of top soils are being eroded, the highest anytime; the sea level is rising at the rate 5-10 mm again it has become highest over the last decades and the prediction for rate of changes in rainfall and temperature contributing to global warming has been accelerate like anything. In addition to this, we need to feed 950 crores of population by 2050, while lands are getting fragmented, resources are fragile, and water is getting increasingly contaminated. Thus, CA is the only option to create a perfect balance between productivity with increase of ecological resilience, even within the cropping system by introducing crop diversification with leguminous crops and this is specially so when the biodiversity is also getting eroded. Water harvesting is the need of the hour as we can make our farm more energy parsimonious, we have direct whatever crop residues are produced in the soil echelon and making this concept a revolution into a reality we have to go in a massive way with bring awareness, creating policies, and mobilizing community to make our agriculture ever- green and ever- resilient.
On this context the present research has been conducted with the following objectives:

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

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

Abstract

The state of Tamil Nadu has a low easterly gradient slope and there are number of rivers, big and small originate from the west flow towards east south easterly and confluence the Bay of Bengal. These were grouped into seventeen major and minor river basins, the Cauvery being the largest basin and the river is also a near perennial river. These river basins receive rainfall both from the southwest and northeast monsoons. The state has over 40,000 surface water bodies and number of major dams and reservoirs. Even then, since the rainfall of the north east monsoon, the major source of water source to these basins, occurs in short span of few days with high rainfall, neither adequate storage is there to store them nor the recharge possibility is available, thus all leading to flooding and wastage. The 40,000 and odd water bodies are heavily silted and polluted too. The paper provides an over view of all these, basin wise, and in addition, strongly advocates intra state river linkage and improvised irrigation management as the best option to save the state from its water deficiency.

3.1 Introduction

There is now latest national thinking going on to conserve the known sources of energy such as firewood, cow dung, agricultural waste, kerosene, oil etc. i.e. by limiting their conventional uses and to propagate the use of non conventional energy especially in the rural area. The large scale indiscriminate use of non commercial fuels fully in the rural areas are not only providing to be inefficient but also inadequate to cope with the growing rural energy demand caused by growing population and growing consciousness among rural people for a better way of life. Planned input of energy are, therefore urgently require to protect the environment and to cope with raising rural energy demand. In order to assess the widening gap between the supply and demand of commercial energy and indiscriminate use of non commercial fuels in rural areas, there is a need for various types of statistical or quantified information to be collected and analyzed in an objective manner and presented suitability so as to serve as a sound basis for taking policy decision to make an optimum utilization of commercial energy in a balanced manner. This will require conducting of survey in areas to be covered.

The statistical data may be collected by complete enumeration or by a sample survey. The earlier method is very cumbersome, time consuming, cost ineffective and requires an intensive development of manpower, if domains of study is quite large. Complete enumeration and sample surveys presuppose the existence of a certain minimum of facilities such as funds, professional personnel for planning the survey methodology and supervision of field operations, sufficiently qualified enumerators, sampling frames, machine tabulation equipment, transport and communication facilities etc. These facilities or combinations there of do not always exist to the extent needed for a complete enumeration survey. As such, sample survey is preferred to complete enumeration for ethic type of studies giving due importance to sampling error.

In the integrated energy planning, there is an objective to find out the present consumption pattern of energy for different end uses, shortages being experienced and people’s reaction to using alternate sources of energy is also enumerated.

The survey methods determine – incident, distribution and the interrelation among sociological variable and psychological variable. The sociological variable s consists of age, education, income and attitude, behavior, motivation, belief and knowledge about the problem.

Abstract

This paper is the synthesis of researches of quaternary on the Chad lake basin, our field investigations, our previous palaeogeographic research underlined by the sédimentological analyses, ¹⁴C radiocarbon and thermoluninescence dating. The objective is to show that after the effects of geological fluctuation, paleoclimatic oscillations, the Chad lake basin experiences since historical times an increasingly drying up due to the combination of geographic factors such as its close position in the Sahara desert, centre of the climate dynamics and human activities. This situation frequently generates recurrent live conditions to which man has to face.

Introduction

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

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

African agriculture is in crisis, and Africa’s farmland is losing its fertility at an alarming rate. Farmers grow the same crops in the same fields year. They don’t fertilize, and they don’t terrace their fields. When the soil wears out, the clear a new piece of land (wwww.npr.org). N. Kozana Diamini Luma, the Chairperson of the African Union Commission has emphasized that ‘the year 2014 – the year of Agriculture in Africa’ will be used as an opportunity to accelerate the drive for food security. The major thrust for that has to be on ensuring that the farmers make their profession on economic activity that generate wellbeing for all in a sustainable manner (1, 2).