eChapters

Abstract

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

Introduction

Data Mining is a complex subject that describes a type of database application used to search large amounts of data for patterns using specific tools. Advanced statistical methods and tools such as classification, association rule mining and cluster analysis search large volumes of data for previously unknown relationships within the data. Data Mining can be used to decipher trends in a wide variety of fields such as computer science, the marketing and advertising of goods, services or products, artificial intelligence research, biological sciences and high-level government intelligence. Data mining is a logical process that is used to search through large amounts of information in order to find important data.

ABSTRACT

The Remote Sensing data volume is vast, so high processing capacity is required. It is impossible to update spatial data without the support of high performance of RS image management, retrieval, and change detection and pattern discovery. After introducing some background information including significance of SDI updating, feasibility and advantages of satellite RS imagery used to update spatial data, the framework of updating spatial and attribute information based on RS image is proposed. In the process, the integrated processing of raster data and vector data is very important, so some functions of RS image processing and GIS software should be fused. In order to extract the anticipated images from vast image database, effective retrieval technique is vital. RS image data mining and knowledge discovery is the synergy of Spatial Data Mining (SDM) and Image Data Mining (IDM). Oriented to the demands of SDI updating to intelligent information processing, some primary issues on RSDM are analysed. It is pointed out that updating SDI by satellite RS imagery will be potential advantageous in the future. RS Data Mining and Knowledge Discovery are very important and will play important roles in the future.

Keywords: Spatial Data Infrastructure (SDI), Remote Sensing Data Mining, Spatial Data Mining, Image Data Mining.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Introduction

The value of data privacy in the linked world of today cannot be emphasised. As digital technologies proliferate and personal data is collected, stored, and analysed on a large scale, people and organisations are more susceptible to data exploitation and privacy violations. In the modern era, privacy of data are vital because of following reasons:

1. Safety Regarding Personal Information: Fundamental People’s freedom to manage their personal data protected by data privacy. In a time when everyone has a digital footprint, maintaining one’s liberty and dignity requires protecting private information such as financial records, health records, and internet activity

2. Trust Management is Critical for Organisations and Companies: Maintaining data privacy improves reputation and brand loyalty by building trust with stakeholders, clients, and consumers. On the other hand, privacy violations and data breaches can seriously harm an organization’s reputation by undermining people’s confidence in its ability to protect sensitive data.

3. Compliance with Laws and Regulations: Strict data privacy laws have been passed by governments all over the world to uphold people’s rights and make businesses responsible for handling personal information. Adherence to regulatory mandates similar to the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US not only necessary from a legal standpoint but also morally required for moral business practices.

17.1 Introduction

In an era where digital technologies permeate every facet of our lives, the discourse surrounding data privacy and security has never been more pertinent. However, within this discourse lies a critical blind spot the intersectionality of gender and technology. As we navigate the complexities of safeguarding personal data and ensuring online security, it is important to recognise and address the differential impacts experienced by individuals based on their gender identities. The combination of gender dynamics with data privacy and security unveils a multifaceted landscape characterised by unique challenges and opportunities. This intersectionality necessitates an examination that goes beyond conventional approaches, requiring a critical lens that acknowledges the diverse experiences and power dynamics at play.

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

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.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

Introduction

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

Introduction

Hydrological data visualization plays a crucial role in analyzing and understanding the behavior and characteristics of water resources. By visualizing hydrological data, we can gain insight into the distribution, variability, and trends of different hydrological variables such as river flow, precipitation, groundwater levels, and water quality parameters. This information can help decision-making in areas such as water resources management, flood forecasting, and climate adaptation.

R Language is a popular and versatile tool for hydrological data visualization, offering several powerful packages for data analysis and visualization. Each of these packages has its own strengths and limitations, which can be leveraged depending on the specific needs of a given analysis. For example, ggplot2 is an extensively used package for creating publication-quality static plots that are customizable and visually appealing. This package is based on the grammar of graphics, which provides a new plotting system that combines the benefits of both base and lattice graphics. ggplot2 can be used to create a wide range of plot types, including time-series plots, spatial maps, and scatterplots. It provides several options for customizing plot elements such as axis labels, legends, and plot themes. On the other hand plotly offers interactive and dynamic plots that can be easily shared and embedded in web applications. With plotly, we can create plots that allow for zooming, panning, and hovering over data points to display additional information. This package is mainly useful for exploring relationships between hydrological variables. In addition to these packages, hydroTSM provides functions for reading, manipulating, and plotting time-series data. hydroTSM can help us create time-series plots of variables such as river f low, precipitation, and temperature, with options for adding trend lines error bars, and annotations.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

Financial data visualization is the process of turning financial information into visual formats like charts, graphs, and dashboards. It helps finance teams interpret large datasets quickly, spot patterns or anomalies, and make betterinformed business decisions. Data visualization in finance is a craft of transforming complex financial numbers into accessible visual forms. Visual formats such as charts, graphs, dashboards, maps, and interactive displays serve as lenses through which data becomes visible and understandable. Finance practitioners navigate risk profiles, asset performance, expense trends, cash flows, and portfolio behavior more effectively when raw inputs become visual stories. Creation of visual representations hinges on careful selection of chart types, scaling, color, and annotation. Communicating precise meanings through visuals demands an understanding of data relationships, temporal changes, distributions, and comparisons.

Introduction

Individual bits or “bytes” of “raw” biological data (e.g. the number of individual plants of a given species at a given location) do not by themselves inform the human mind. However, drawing various data together within an appropriate context yields information that may be useful (e.g. the distribution and abundance of the plant species at various points in space and time). In turn, this information helps foster the quality of knowing (e.g. whether the plant species is increasing or decreasing in distribution and abundance over space and time). Knowledge and experience blend to become wisdom—the power of applying these attributes critically or practically to make decisions. This quotation is from the Atlantic Canada Conservation Data Centre, a non-profit organization established in 1999.Thus, we are led to think about Data, Information, Knowledge, and Wisdom as we explore the capabilities and limitations of IT systems.

Introduction

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

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

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

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

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

Introduction

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

Data - A Definition and Certain Points to Ponder

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

Introduction

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

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

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

4.1 INTRODUCTION

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

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

Evolution of Computer technology and advent of database concept have provided enormous opportunities to the  scientist, researchers and managers of diverse field  to create, manipulate integrate, link and analyse their data in order to generate information and knowledge. The research projects involving exploration, survey and collection of data, generally create database to maintain and analyze the data.  Almost in every sphere of life and industry this database concept/technology is used in one form or other.  Scientists and researchers of life sciences realized the importance of the database and used this technology to develop databases of different types/groups of living organisms. The database technology has eased the difficulty in designing, developing and maintaining the complex information systems.

Databases

A database is a collection of information that is organized so that it can easily be accessed, managed, and updated. Databases can be classified according to types of content: bibliographic, full-text, numeric and images and by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. The term database may be narrowed to specify particular aspects of organized collection of data and may refer to the logical database, to the physical database as data content in computer data storage or to many other database sub-definitions.

Abstract

Biological databases that are composed of a large collection of digital nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer, which is made available to the research community worldwide using web servers. The vast amount of information generated in molecular biology is analyzed, curated, tabulated, and arranged in databases. There are different types of biological databases, based on their contents. Besides, the database management system, a program to manage the databases, can be of two types: distributed DBMS and relational DBMS. This chapter briefly narrates the importance and purpose of biological databases.

Date palm (Phoenix dactylifera L.) known as Khajoor, Kharek is an important fruit tree of semi-arid and arid regions of the world. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system.  It is also one of the most suitable fruit plant, which can be easily grown under saline irrigation conditions. It is well known fact for date palm that its feet in the water and its head in sun. It requires dry hot climate for growth and development of fruits. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, income generation and foreign exchange earnings. It can be grown at such areas where adequate irrigation facilities are available besides other climatic requirements (Chandra et al., 1992; Sharma and Singh, 2013).

Introduction

Date palm (Phoenix dactylifera L.; Family- Arecaceae) also known as Khajoor, Kharek is an ancient important fruit tree of semi-arid and arid regions of the world. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system. It is also suitable fruit trees for growing under saline irrigation conditions. It is well known fact for date palm that its feet in the water and its head in sun. It requires dry hot climate for growth and development of fruits. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, higher income generation and foreign exchange earnings. It can be grown at such places where adequate irrigation facility is available besides other dry hot climate conditions (Chandra et al, 1992; Singh and Dhandar, 2007). Better returns from date plantation can be achieved through good management practices besides adequate marketing of produce. The quality produce and its marketing will help to increase the income from date palm cultivation.

Introduction

Date palm (Phoenix dactylifera L.) is a monocotyledonous and dieocious species belonging to the family Palmae. The Latin name of the date palm, Phoenix dactylifera, is derived from the Phoenician name “Phoenix,” which means date palm, and dactylifera is derived from the Greek word “daktulos,” which means a finger. In India, generally, two species of Phoenix are found, of which, Phoenix sylvestris Roxb. called “desi khajoor,” is found throughout the country but t produces inferior quality fruits. The fruit contains large stones with less pulp content.

Date palm is considered one of the world’s oldest cultivated fruit trees. Archeological evidence suggests that the date palm was domesticated some 6000 years ago in the Mesopotamian region. It is widely cultivated in arid regions of the Middle East and North Africa. It is also one of the most appropriate fruit trees, and it can be easily grown under saline soil and water conditions. It grows well in poor, arid soils due to its hardy plant characteristics and deep root system. Date palm is believed to be indigenous to countries around the Persian Gulf. In the Indus Valley, date palm is believed to have been introduced by the soldiers of Alexander the Great in the fourth century B.C. and during the Muslim invasions at the beginning of the eight century AD. Date palm was commercially cultivated in Iraq, Saudi Arabia, Algeria, Iran, Egypt, Morocco, Tunisia, Pakistan, and Sudan, among many other Near East and North African countries. In India, dates are grown in the states of Gujarat, Rajasthan, Punjab, Haryana, and Tamil Nadu (Table 1).

Phoenix dactylifera commonly known as the true date palm, is a palm in the genus Phoenix, cultivated for its edible sweet fruits. The fruit’s English name as well as the Latin species name dactylifera, both come from the Greek word for “finger,” Daktulos, because of the fruit’s elongated shape. The date palm is believed to have originated in the lands around the Persian Gulf. Date palms are said to thrive with their “feet in water and heads in the sun” because they need plenty of ground water but high heat and arid weather to produce fruit. Therefore, date palms grow best in hot and arid climates.

Genus : Phoenix 
Species : dactylifera L.
Family : Palmae (Sub family-Phoenicoideae)
Chromosome No. : 2n=36

Date (Phoenix dactylifera) Finger bearing is derived from Greek word Phoenix. This ancient plant has been named phoenix after the mythological bird which was native of Egypt and also very likely after ‘Phoenicia’, an old country on the coastal regions of Syria where palms were found growing in plenty. The date palms are usually planted as ornamental for their luxurious crown which is spreading at the top. The trunk and crown is covered with stiff pinnate leaves, suckers or offshoots at the base, markedly transforms the site or scene and provide an exotic charm.

Date palm (Phoenix dactylifera L.) is an important fruit tree of semi-arid and arid regions. It grows well under poor desertic soils due to its hardy plant characteristics and deep root system. It is also one of the most suitable fruit trees, which can be easily grown under saline conditions. In arid region, crop production is risky preposition, where date palm cultivation contributes in achieving food security, high nutritive value food, crop diversification, desertification control, income generation and foreign exchange earnings. It can be grown at such places where adequate irrigation facilities are available besides other climatic requirements.

The latin name of the date palm, Phoenix dactylifera is derived from a Phoenician name “Phoenix”, which means date palm and dactylifera derived from a Greek word “daktulos” means a finger. In India, generally two species of Phoenix are found. Out of which, Phoenix sylvestris Roxb. called “desi khajoor” is found throughout the country but it produces inferior quality fruits. The fruit contains large stone with less pulp content. From its crown parts, a liquid is extracted known as neera or taddi, which is used as fresh drinks and also for preparation of jaggary. The tree of wild date/ desi khajoor (Phoenix sylvestris) is found in Bundelkhand region of the country and it is suitable tree component for agro forestry system (Tewari et al., 2001). Phoenix dactylifera is commercially grown for edible fruits and value added products.

• The date is a good source of crystalline sugar.
• It is a dioecious and monocotyledonous fruit crop.
• Dates can be grown in sandy loamy soils.
• Date palm needs 3300 units of heat. (A unit of heat means a degree above a daily mean of 18°C between flowering, fruit development and ripening period.

Cultivars

• Halawi of makes tolerant of rain. It is 28.5 to 42.2 per cent Dakastage Dang Zoo, Hayni Khadrawy, Shamran (Sayen) are tolerant to high humidity. Medjoal, Bastree, Zahidi – is rain and high humidity tolerant.

Date-palm (Phoenix dactylifera L.). belongs to family Palmae and is the most common tree of desert and dry areas. Date palm is one of the oldest trees known to man kind. Date-palm is believed to have originated in the countries around Persian Gulf. It is extensively grown in Iraq, Iran, UAE, Saudi Arabia, Algeria, Egypt, Libya, Yemen, Mauritiana, Morrocco, Tunisia, Baharin, Sudan, USA, Spain, Australia, Pakistan, South Africa, Somalia, Oman and some parts of the South America. Date is a staple food of the people of the Western Iraq and North Africa. In India, it has been believed to be introduced by soldiers of Alexander the Great in 4^th century B.C. In India it is commercially cultivated in Rajasthan, Punjab, Hariyana and Gujarat.

Cultivars: Deglet Noor, Halawy, Khadhrawi, Hayani, Majhul, Zahidi, Khalaeh.

Uses

Date palms produce many products that are useful to human beings. The primary product is the date fruit, which can be eaten fresh, dried, or in various processed forms. In North Africa and the Middle East, some dates are harvested and consumed during the Khalal stage, when the fruit are still very astringent with a high tannin content (Dowson and Aten, 1962 ; Glasner et al., 2002; Kader,1992). Dates can be used in cereal, pudding, bread, pressed cakes, cookies, candy bars, ice cream, and date shakes (a California specialty). Date fruit also can be made into juice, vinegar, wine, beer, sugar, syrup, honey, chutney, pickle, paste, dip, and food flavoring (Barreveld, 1993; Glasner et al., 2002).

It is said that there are as many uses of dates as there are days in a year. The trunk and wood of date palms can be used as timber, wood, or fuel. Fiber from the trunk and leaves can be made into bags, baskets, camel saddles, cords, crates, fans, food covers, furniture, mats, paper, ropes, trays, and twine. Dried bundles of leaves (‘‘Barusti’’) can be made into shades, roofs, separating walls, and enclosures. Ribs of the leaves can be used to build boats (small fishing boats called ‘‘Shasha’’) or fishing traps. The base of the leaves and fruit stalks can be used for fuel. The pith of the palms can be made into date palm flour. Terminal buds (palm heart) can be eaten as a salad or as a cooked vegetable. Date seeds can be used as feed for livestock or strung as beads for decoration. Oil from date seeds can be manufactured into soap (Chao and Krueger, 2007). Dates are considered a balanced and healthy diet and have several medicinal uses. Leaves are source of fibre used for making ropes baskets and cordage. Walking sticks are made from leafstalk.

Introduction
Davana (Artemisia pallens Wall.) is one of the important aromatic herb found abundantly in humid habitats in the plains all over India. It is popularly known as sage brush, worm wood and mug word. The davana (Artemisia pallens) is a small to medium height; annual and herbaceous aromatic plant. This herb is erect, and grow up to 60 cm height. Its leaves are highly divided and bears small size yellow coloured flower. Its leaves and stem found protected with greyish-white tomentum. The leaves are lobed, alternate, and petiolate.
Origin & Distribution
It is native to southern India, where it is traditionally grown in Karnataka, Maharashtra, Kerala, T.N., and A.P, growing widely in tropical to subtropical
plains as Rabi season crop. It also found growing in wild form in the Himalayan region in the Simla, Kashmir valley, and Nainital hills. It is widely used by Ayurvedic healers to balance the three energies. It is an erect growing
plant up to 60 cm height. It does not grow wild, but grown as a garden plant in South India. It is a crop of irrigated conditions; and do well when planted in November-December, yielding significantly higher oil yield in comparison to crop planting in July-August. The crop should be harvested 2-3 inches above ground level during bright sunny days at 50 percent flowering stage to have high oil content and oil yield.

Semilooper: Thysanoplusia(Plusia) orichalcea : Noctuidae

Bluish green or grey coloured eggs are laid singly on lower surface of leaves.The larvae are semi loopers and feed voraciously on leaves and cause defoliation of the plants.

6.1. Origin and distribution

• Grows wild in temperate Himalayas.

• Common in Kashmir Valley, Shimla and Nainital Hills.

• Commercially cultivated in Karnataka, Maharashtra, Kerala, Tamil Nadu and Andhra Pradesh

6.2. Economic importance

• Sweet worm wood or annual worm wood

• Chemical constituents - Artimisinin

• Commonly used in garlands, bouquets and religious offerings in most part of the country.

• Leaves and flowers contain essential oil valued for its exquisite and delicate aroma and is used in high grade perfumes and cosmetics.

Deadweight cost/loss : The extent to which the value and impact of a tax, tax relief or subsidy is reduced because of its side-effects. For instance, increasing the amount of tax levied on workers’ pay will lead some workers to stop working or work less, so reducing the amount of extra tax to be collected. However, creating a tax relief or subsidy to encourage people to buy life insurance would have a deadweight cost because people who would have bought insurance anyway would benefit.

Death-to-case ratio : The number of deaths attributed to a particular disease during a specified time period divided by the number of new cases of that disease identified during the same time period.

Debt forgiveness : Cancelling or rescheduling a borrower’s debts to lessen the pain of the debt burden. Debt forgiveness is increasingly viewed as the best way to relieve the financial problems facing poorer countries. Some of these countries have to pay so much in interest each year to foreign lenders that they have little money left to spend on the long-term solutions to their poverty, such as educating their workers and building a modern infrastructure. In 1998 the world Bank calculated that around 40 of the world’s poorest countries had an “unsustainably high” debt burden : the present value of their total debts was more than 220% of their exports. Debt forgiveness has potential drawbacks. For instance, there is a risk of moral hazard. If countries that borrow too much are let off their financial obligations, poor countries may feel they have nothing to lose by borrowing as much as they can. This is why policymakers often argue that debt forgiveness should come with a conditionality clause, for instance, a requirement that countries have a track record of implementing economic reforms designed to prevent a repeat of the errors that first created the need for debt forgiveness. This is the approach taken by the World Bank’s HIPC (highly indebted poor country) initiative, launched in 1996 and expanded in 1999. However, by 2003, only eight of the 38 poor countries eligible under the programme had made enough progress in reform to have some debt forgiven.

Introduction

Work-dwelling or workplace well-being describes all aspects of the working lifespan of workers from the point of view of quality, safety and physical environment. It pronounces the workers’ feelings about their work, their working environment, the climate at work and work organization. According to WHO (2010), a healthy workplace environment not only merely finds the absence of illness but also a state of comprehensive physical, mental and social comfort. Work and health hence have a reciprocal relationship that concerns occupational health wellbeing and confers decent work environments i.e. (i) payment of reasonable income; (ii) safety-security in employment and working environment; (iii) guarantee of equal opportunities and treatment for all; (iv) enclosure of social protection for the workers and their families; (v) offering prospects for personal development and inspiring social integration and (vi) allowance of workers to express their apprehensions and to organize (ILO, 1999). In the entrepreneurial sphere, globalization along with fast industrial progress is supposed to be accountable for occupational health hazards and related workplace ill-being, work insecurity, the lack of protection for workers’ health and well-being in the form of work-related accidents, illness and stress, lack of workers’ compensation and paid sick leave, lack of maternity protection, excessive hours of work, etc.(https://www.ilo.org/). This was evidenced in the recent report of ILO (2023) where it was found that 2.3 million people around the world surrender to work-related accidents or diseases every year which links to over 6000 deaths every solitary day (www.ilo.org). Subsequently, there are about 340 million occupational accidents where 160 million are fatalities of work-related illnesses annually in the World.

Introduction

Decentralization means the action or fact of reducing or undoing “centralization” or “concentration.” The dictionary meaning of decentralization is “The process of transferring the functions from the Central Government to the local units.” Decentralization is devolving power of decision–making with the lower level of organization. In a decentralization, delegation and devolution of power are given to the lower level. Devolution implies a situation in which lower ranking units acquire greater autonomy in respect of certain defined functions, including decision-making authority. Devolution of functions, powers, and responsibilities are normally granted through legislation.

Decentralized Planning in India

With the introduction of the Community Development Programme (CDP) and National Extension Service (NES) in 1952, increasing agricultural production had been on India’s agenda in different plan periods. The success of CDP and NES was reviewed by the Balawant Rai Committee in 1957 and the committee realized that community development can be achieved only when the community understands its problems, owns its responsibilities, exercises the necessary powers through its elected representatives, and maintains a constant and intelligent vigilance on local administration. The recommendation of the committee led to the preparation of plans at the level of state, district, block, towns, and Panchayats in the second plan period from 1957 to 1962. The Fourth Evaluation Report (Programme Evaluation Organization, Planning Commission) in 1959 recommended that organized and institutional participation by the public in development programs is bound to give better, more consistent, and more lasting results. In 1977, Ashok Meheta Committee suggested changing the concept of Panchayati Raj to a political institution. In 1981, the eminent economist Prof. M.L. Dantewala observed that the merit of decentralized planning is derived from its ability to discover dormant resources and skills and its endeavor to activate them. However, there was slow progress in decentralized planning up to the eighth plan (1992-97).

Storage of paddy seed lots after harvesting is one of the crucial aspects of processing shopfloor. Paddy due to increased mechanised harvesting may loose vital seed quality parameters. Since seed growers cannot buy a combined harvester themselves-they resort to hiring of this machine. Sometimes in the hurry of completing harvesting they harvest the crop at un-safe moisture content. This un-safe moisture content may go up to 50-60% also. Harvesting of seed crop with high moisture has adverse effect on storability. Ensuring safe seed moisture is very critical especially during rabi-summer seed production cycle. Most of the growers depend on sun drying for reducing the seed moisture. But paddy should not be dried >3% in one cycle. Follow tempering and convection cooling after every cycle and repeat the cycle till seed attains safe moisture content (14%).

Introduction

Financial markets play a critical role in the health and stability of a nation's economy. They act as a complex network where individuals, businesses and governments can exchange financial instruments, facilitating the flow of capital and fostering economic growth. According to the World Bank, the global stock market capitalization reached a staggering $93 trillion USD in 2022, highlighting the vast pool of resources circulating within these systems. At the heart of financial markets lies the ability to connect those who possess capital with those who require it (Karpova et al?, 2020). Companies seeking to expand their operations can raise funds by issuing stocks or bonds, essentially selling a piece of ownership or a promise to repay a loan with interest. Investors, on the other hand, can utilize these markets to put their money to work, seeking returns on their investments through dividends, interest payments, or capital appreciation. This efficient allocation of resources fuels economic growth by enabling businesses to invest, innovate and create jobs.

Introduction

Women make essential contributions to the agricultural and rural economies in all developing countries. Their roles vary considerably between and within regions and are changing rapidly in many parts of the world, where economic and social forces are transforming the agricultural sector. Agriculture needs manpower, if the manpower split into gender wise, amazing fact is that the women contribution is greater or equal to men (Chauhan, 2011).That is reason that right from the day the country become independent, it has been trying hard for rural development.

According to the Penguin Atlas of Women in the World, women make up 40 per cent of the agricultural labour force in most parts of the world, while in developing countries they make up 67 per cent of the agricultural workforce (Singh, 2008). Therefore, strengthening and expanding women’s roles and opportunities in agriculture promotes sustainable economic development. Gender inequalities are a measurable and significant limit on agricultural productivity and efficiency, undermining rural economic development. India has paved the way to development by increasing its trade and growth in Agriculture sector. To review the agriculture scenario of our country, it depicted that there is need to pay sincere attention towards the human resource development (men and women together) because modernizing agriculture needs skill and efficient work forced. Farm business has become a family enterprise in India, where both men and women take part evenly. It is well known fact that the success of rural development process largely depends on the participation of people at large, irrespective of sex. The problems of involving women’s participation in the development process are now catching the attention of planners and policy maker because of increasing imbalances generated out of development process. Now days, rural women play a vital role in domestic and socio-economic life of the society and therefore, national development is not possible without developing this important and substantial section of our society (Okafor, 2008).

The content presented in this chapter is exploratory and reflects emerging trends in integrating weather intelligence with decision support systems. The term “weather intelligence” is introduced as a framework, with ongoing research expected to further refine and expand upon these ideas as technologies evolve. In an era where climate variability and extreme weather events increasingly threaten global food systems, the integration of advanced decision support systems (DSS) with what we are now calling weather intelligence has emerged as a transformative paradigm. This chapter explores how food production— encompassing agriculture, fisheries, and animal husbandry—can harness integrated weather intelligence to inform real-time crisis management, strategic planning, and risk mitigation. The discussion is exploratory in many respects, as the concept of weather intelligence represents an innovative framework that challenges traditional forecasting methods and decision support tools (Fig. 9.1). Drawing on multidisciplinary research and practical case studies, we examine the building blocks of decision support systems tailored to food production. We then discuss the integration of weather intelligence into crisis response and strategic planning, highlighting both established risk mitigation approaches and emerging tools designed to anticipate and respond to extreme weather events. By contextualizing these developments within the broader scope of sustainable food production, this chapter offers an in-depth analysis and a forward-looking vision for decision support and response management in a rapidly changing climate.

7.1  Write True/False

Q.1    All the problem of statistical inference may be considered as the problem of “Decision making in the face of uncertainty.”
Q.2    The problem of point estimation is the problem if deciding which value in the “parameter space” Wq is the true value of the parameter.

Given this immense significance, the ability to accurately predict the behavior of the monsoon has always been a paramount concern. The science of monsoon forecasting has evolved significantly over time, transitioning from rudimentary observational methods to sophisticated numerical weather prediction models powered by advanced computing capabilities. This chapter delves into the intricate world of monsoon forecasting, exploring its historical roots, the scientific principles that underpin it, the diverse methodologies employed, and the continuous efforts being made to enhance its accuracy and utility. We will embark on a detailed journey through the complexities of the Indian monsoon system, examining the atmospheric and oceanic factors that drive its variability. From understanding the large-scale circulation patterns to deciphering the influence of regional phenomena like the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), we will unravel the key drivers that forecasters consider. The chapter will then explore the spectrum of forecasting approaches, ranging from statistical models based on historical data to dynamic models that simulate the intricate physical processes of the atmosphere and ocean. We will also discuss the role of satellite technology, ground-based observations, and high-performance computing in generating increasingly detailed and reliable forecasts. Furthermore, this chapter will critically assess the current state of monsoon forecasting, acknowledging both its advancements and its inherent limitations. We will address the challenges in achieving the desired levels of accuracy, particularly at regional and local scales, which are most relevant to farmers. Understanding these limitations is crucial for setting realistic expectations and guiding future research directions. Finally, and perhaps most importantly, we will examine the crucial link between monsoon forecasting and the needs of the agricultural sector. We will revisit the expectations of farmers regarding forecast information – their need for accuracy, timeliness, localization, specificity, and actionable advice. By understanding the gap between current forecasting capabilities and these requirements, we can better appreciate the ongoing efforts to bridge this divide through improved forecasting techniques and effective dissemination strategies. This chapter aims to provide a

Soil Organic Matter

Soil organic matter plays an important role in the maintenance and improvement of soil properties. It is a dynamic material and is one of the major sources of nutrient elements for plants. Soil organic matter is derived to a large extent from residues and remains of the plants together with the small quantities of animal remains excreta, and microbial tissues. Soil organic matter is composed of three major components i.e. plant residues, animal remains and dead remains of microorganisms. Soil organic matter comprises residues of plants and animals and these compounds occur in soil in close combination with inorganic substances. Animals and plant residues are made up of complex carbohydrates, simple sugars, starch, cellulose, hemicellulose, pectins, gums, mucilage, proteins fats, oils, waxes, resins, alcohols, aldehydes, ketones, organic acids, lignin, phenols, tannins, hydrocarbons, alkaloids, pigments, etc.

The organic matter subjected to microbial decay in the soil comes from several sources like plant remains, forest litter, incorporation of plant tissues, animal tissues, and excretory products. The chemistry of organic matter is very complex, and investigations of the transformations and the responsible organisms have therefore been extremely interesting. The organic constituents of the plants are commonly divided into six categories.

Soil organic matter is among the most complex materials existing in nature. In addition to the organic constituents present in undecayed plant and animal tissues, soil organic matter contains living and dead microbial cells, microbially synthesized compounds and an endless array of derivatives of these materials produced as the result of microbial activity. Soil organic matter probably contains most of the naturally occurring organic compounds. Some components of soil organic matter are no doubt distinctive to the soil environment, particularly those involving inorganic-organic complexes.

6.1. Composition of Soil Organic Matter

The composition of soil organic fraction is as follows:

1. Components of organic residues undergoing decomposition

7.1 General Description

Shifting cultivation is a type of land use that dates back to the dawn of agriculture. It's done in places where forest resources are still plentiful. It usually refers to the gradual transformation of primary forests into a mosaic of secondary forests at varying stages of growth, rather than the complete abolition of forests. However, primary forest removal occurs gradually in order to preserve the same fallow time despite population development. As no primary forests remain (or as the remaining primary forests are entirely protected by local customs or conservation legislation), secondary forests fade away, and new techniques arise or are adopted, the fallow period shortens. Natural capital is gradually replaced by man-made productive capital, or the system approaches a state of crisis, depending on whether the limitations to these changes can be levered. In theory, the new land uses allow for more forest protection because they require less territory to provide the same output. In practice, they are triggered or triggered by a series of more radical changes, such as infrastructure development, migrations, financial investments, and new consumer habits, all of which frequently end in large-scale deforestation.

Deep learning has significantly advanced automated plant phenotyping and disease detection by enabling computers to identify complex patterns in large volumes of plant imagery and sensor data. This chapter examines the fundamentals and applications of deep learning architectures in plant science. It discusses convolutional neural networks for image-based plant analysis and advanced architectures including recurrent neural networks, transformers and hybrid models. Data acquisition through imaging and sensor technologies is also considered. The chapter highlights how deep learning can support automated identification of plant characteristics, disease symptoms and crop health conditions. Challenges related to datasets, environmental variability, computational requirements and model generalization are examined alongside emerging research opportunities.

Deep learning enables automated analysis of agricultural images for crop classification, disease detection, weed identification, yield prediction and soil monitoring. This chapter examines Convolutional Neural Networks, Recurrent Neural Networks and Vision Transformers applied to imagery collected by satellites, drones and ground-based sensors. These models automatically identify patterns and features that conventional methods may overlook. Integration with precision agriculture can improve decision-making, increase crop productivity and reduce resource waste. Challenges include shortages of annotated datasets, limited interpretability and high computational requirements. Transfer learning, data augmentation, edge computing and real-time processing are discussed as approaches for enabling practical deployment in remote and resource-constrained agricultural environments.

Traditional livestock diagnosis is often reactive and may identify disease only after clinical symptoms become visible. This chapter introduces deep-learning methods for automated health monitoring, early disease detection and predictive analytics. Models analyse images, videos, body temperature, heart rate, activity patterns and historical health information. Convolutional neural networks extract visual features and classify diseases, while LSTM networks identify patterns in time-dependent physiological and behavioural data. Multimodal fusion improves the reliability of predictions by integrating different data sources. IoT-connected systems can generate real-time warnings and support timely intervention. The chapter compares deep learning with conventional approaches and evaluates accuracy, recall, false-negative rates and practical deployment.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

Abstract

Now-a-days non bovine milk become an area of interest for their potential to provide nutrients along with beneficial health effects. Deer belongs to cervidae family, is one of the source of non bovine milk. Two thousand years ago, the people of Soviet Union along the borders of Russia, Mongolia, and China used to consume deer milk and milk product. Deer milk is a plentiful source of milk solids. The proteins of deer milk is more digestible than bovine milk owing to their unique profile and characteristics. Deer milk being rich in highly digestible protein offers beneficial health effect through bioactive peptide production. The distinctive physico-chemical properties being quite similar to sheep milk shows the milk product manufacturers a scope to use deer milk for production of various dairy products. Although there is immense opportunity to use deer milk for development of diversified functional products, further research work is needed to be carry out to find out the type of biopeptides produced during protein hydrolysis, role of fatty acids, effect of different unit processing on its constituents etc. This chapter delineates the nutritional profile and physico-chemical properties of deer milk along with the significance of it in terms of its therapeutic roles and potential to be used for product development.