eChapters

Temperature stands as a fundamental environmental factor profoundly influencing the life cycle of crops, from the initial spark of germination to the final stages of maturation and yield formation. The thermal regime, encompassing the daily and seasonal patterns of temperature, dictates the rate of biochemical reactions within the plant, thereby governing the pace and efficiency of physiological processes crucial for growth and development.
This chapter, „Thermal Regime: Crop Growth and Development,“ will delve into the intricate relationship between temperature and the various phenological stages of crop plants. It will be explored how different cardinal temperatures – the minimum, optimum, and maximum thresholds – dictate the boundaries within which growth can occur and the rates at which developmental milestones are achieved. Understanding the impact of temperature on processes like photosynthesis, respiration, nutrient uptake, and ultimately, yield, is paramount for optimizing crop production in diverse environments.
Furthermore, we will examine the concepts of heat units or growing degree days, which provide a quantitative framework for predicting crop
development based on temperature accumulation. By appreciating how the thermal environment interacts with genetic potential and other environmental factors, we can gain valuable insights into crop suitability for specific regions, the timing of critical growth stages, and strategies for mitigating the adverse effects of temperature extremes.
This chapter aims to provide a comprehensive understanding of the thermal regime and its critical role in shaping the growth and development of our cultivated plants, laying the foundation for informed agricultural practices and improved crop management.

In this chapter we will talk about the tools which are required to make project in artificial intelligence. All this bellow discussed tools are uses python programming language for making any project in any respective field. We have discussed about how anyone can install and use the tools to built a real time project in artificial Intelligence.

Using Python IDE

To download and install Python visit the official website of Python https://www.python.org/downloads/and choose your version. We have chosen Python version as per requirement all the codes in this book were on python version 3.6 as you can see in the Fig. 7.1.

Fundamentally toposheet represents a kind of map in which topographic features of particular area is given details on the map features in two dimensional land surfaces. It shows both horizontal and vertical portion of the terrain with a combination of contour lines, contours, symbols, labels and related representation together in one piece of paper. Toposheet portray the shapes, location of mountains, many natural and manmade features, and the toposheet combinedly uses for extracting accurate information about particular area containing land surface features.

Traditional knowledge is a type of Intellectual property. It is also known as indigenous knowledge generated from the local communities and tradition of regional technology areas. This knowledge is orally passed over generation from person to person. According to world intellectual property organization, Traditional knowledge is an indigenous, cultural and intellectual property. The biodiversity of a country, the uses and applications of biological resources and prevalent practices are examples of traditional knowledge associated with life science. TK has the tremendous potential of being transformed into commercial opportunity by the development of products and processes. Hence, a share of benefits must accrue to creators and holders of Traditional Knowledge.

Transformation of data is useful when the assumptions required for analysis of variance are not adequately satisfied. Forestry experiments may produce observations showing skewness, unequal variances, non-normality, or departures from additivity. This chapter explains the need for transforming observations to an appropriate scale before statistical analysis. It discusses the characteristics of an ideal transformation, including stabilisation of variance, improvement of normality, efficient estimation of means, and achievement of linear and additive effects. Various transformation techniques used in forestry experimental analysis are introduced with suitable examples. The chapter emphasizes that appropriate transformation can improve the validity, sensitivity, and interpretability of statistical tests.

Introduction

Mithun (Bos frontalis), or ‘Gayal’ or ‘Eso’ or ‘Sial’ or ‘Sandong’ or ‘Wei’, is a rare bovine species, exclusively found in the northeastern hill region (NEHR) of India at an altitude of 300-3000 m. It plays an important role in the socio-economic and cultural life of indigenous tribes of Arunachal Pradesh, Nagaland, Mizoram and Manipur and is aptly referred to as the ‘Pride of Northeast India’ (Mukherjee et al. 2019). Besides India, mithun population is distributed to China, Bangladesh, Thailand, Bhutan, Myanmar, and Malaysia. The Indian mithun is a medium- to large-sized majestic ruminant with a characteristic jet-black body, an ash-colored forehead, and ‘white stockings’ on all the limbs. This unique animal has a special preference for salt feeding, is a skilled browser even in steep, hilly terrain, and does not need pasture land like cattle. Mithun is primarily reared under a free range system as an important source of lean meat, rich in amino acids, proteins, PUFA, etc. On September 1, 2023, the Food Safety and Standards Authority of India (FSSAI) officially certified the mithun as a food animal (Joshi et al., 2021; Lalchamliani et al., 2024; Ramesh et al., 2024). Compared to milk from other species, mithun milk has much larger quantities of fat, lactose, solids-not fat (SNF), calcium, important vitamins A, D, and E, essential amino acids, and polyunsaturated fatty acids (Devi et al., 2023).

ABSTRACT

This paper takes cues from the previous paper on the evolution of IPR concepts and attempts explaining the nuggets of TRIPS and analyzes the current trends in its implementation.

Introduction

As the TRIPS Agreement signifies stronger protection of conventional and new areas of intellectual property, the question that how implementation of TRIPS could influence developed and developing countries is to be addressed. The implications of such influence have far reaching consequences in terms of socio-economic development of Nation States, including geopolitical strategies and hence, a proper understanding on the plausible influence is essential.

7.1 Tuple Introduction

A tuple is a collection which is order and unchangeable. In python tuples are written with round brackets. Tuples in python are very similar to list which contains different types of elements with the following major differences.

It is declared as

tuple1=(1,2,3,“ram”,“shyam”)

Program implementation import timeit

listtime=timeit.timeit(stmt=“[1,2,3,4,5,6,7,8,9]”,number=1000000)

tupletime=timeit.timeit(stmt=“(1,2,3,4,5,6,7,8,9)”,number=1000000)

print(?List takes time:”, listtime)

print(?Tuple takes time:”, tupletime)

19.1 Python Turtle Graphics

Turtle is a special feathers of Python. Using Turtle, we can easily draw in a drawing board.

First we import the turtle module. Then create a window, next we create turtle object and using turtle method we can draw in the drawing board.

19.2 Some Turtle Method

Example

from turtle import *

t = Turtle() #object in python

wn=Screen()

wn.title(?my first graphic”)

wn.bgcolor(?yellow”) #color filling in window

t.shape(?turtle”) #turtle shape given

t.color(?red”, “green”)

The cold chain is the lifeline of the ice cream industry because the product must remain continuously frozen from factory hardening until consumption. This chapter examines the complete cold-chain network, beginning with hardening and factory cold storage and extending through CFA or distributor cold rooms, refrigerated transport, retail freezers, and final consumer handling. It explains temperature requirements, monitoring systems, equipment selection, cold-room management, stock rotation, and quality assurance. Particular attention is given to Indian challenges such as power interruptions, inadequate freezer maintenance, poor retailer practices, and temperature abuse. The chapter demonstrates how disciplined cold-chain management protects texture, flavour, shape, overrun, shelf life, and ultimately consumer confidence

Artificial intelligence is becoming an important technology for transforming agricultural decision-making and farm management. This chapter introduces the fundamental concepts, tools, and applications of AI in agriculture. It explains how machine learning, deep learning, computer vision, predictive analytics, and intelligent systems can process large volumes of agricultural data. AI applications include crop monitoring, disease detection, yield prediction, precision input management, and automated decision-making. The chapter also discusses the major drivers of AI adoption, including increasing food demand, resource constraints, climate change, and labour shortages. Examples of AI-based agricultural applications demonstrate its potential to improve productivity, sustainability, resource efficiency, and resilience.

ABSTRACT

Concomitant with paradigm shift from agrarian and brick and mortar economy to knowledge and service based economy, India needs to adhere to the international conventions for the benefit of enriching its knowledgebase with which economic development and social welfare could be achieved. Although geoscientific research has been at the forefront and contributed immensely to our country’s economic development, number of applications for patents, copyrights, etc., on geoscientific concepts, databases, innovations, designs or processes has been negligible when compared with other branches of science. This paper intends drawing the attention of geoscientists on this lacuna and provides information on fundamental aspects of IPR that may be of help to geoscientists whose innovations are waiting to be filed for patents or copyright.

Introduction

Impact Assessment is simply defined as the process of identifying the future consequences of a current or proposed action. The “impact” is the difference between what would happen with the action and what would happen without it (International Association for Impact Assessment). It’s a structured process for considering the implications, for people and their environment, of proposed actions while there is still an opportunity to modify (or even, if appropriate, abandon) the proposals. It is applied at all levels of decision-making, from policies to specific projects.

IA aims to

• provide information for decision-making that analyses the biophysical, social, economic and institutional consequences of proposed actions;

• promote transparency and participation of the public in decision-making;

• identify procedures and methods for the follow-up (monitoring and mitigation of adverse consequences) in policy, planning and project cycles; and contribute to environmentally sound and sustainable development.

Introduction 
India’s urban transition is among the most significant in the world, both in scale and complexity. With over 34% of the population residing in urban areas as of 2018 and projections indicating that nearly half the nation will be urban by 2047, the quality of urban governance will decisively shape India’s developmental trajectory (United Nations, 2018). Urban areas are expected to contribute over 70% of the GDP by 2030. However, cities face multifaceted governance challenges ranging from infrastructure deficits and environmental stress to inadequate service delivery and institutional fragmentation (NITI Aayog, 2021). In response, digital urban governance has emerged as a transformative paradigm, leveraging information and communication technologies (ICTs) to enable responsive, efficient, and inclusive urban administration. Digital technologies are increasingly embedded into urban governance structures and processes, from e-governance portals and grievance redressal apps to real-time traffic monitoring systems and digital financial tools. This shift is reflected in flagship national programmes such as Digital India, Smart Cities Mission, and the National Urban Digital Mission (NUDM), which aim to digitise public service delivery, enhance citizen engagement, and enable data-driven decision-making at scale (Ministry of Housing and Urban Affairs [MoHUA], 2021; MeitY, 2015). At the heart of this transformation is the creation of robust digital infrastructure such as Integrated Command and Control Centres (ICCCs), city data platforms, and open APIs combined with evolving institutional frameworks that promote interoperability, decentralisation, and cooperative federalism (Kapur & Joshi, 2021; NIUA, 2023). The chapter explores the evolution and current landscape of digital urban governance in India by first outlining the conceptual underpinnings of urban governance and then transitioning into the digital domain. It analyses the role of urban digital infrastructure and the institutional architecture that supports it, highlighting the coordinated efforts under NUDM to build an open, interoperable, and citizen-centric digital ecosystem across India’s 4,400+ towns and cities (MoHUA & MeitY, 2021). Complementing this are the

5.1 Introduction
One of the main methods for cost avoidance and prevention is value engineering/ value analysis. “Society of American Value Engineers (SAVE)” states that value Analysis is the systematic use of known procedures which determine the function of a product or service, define a monetary value for the function, and provide the essential function reliably at the lowest overall cost. If these are applied to a new product, it is called value engineering. By analysing the function of any product, material part, component, system, or service, it is possible to find extraneous expenditures connected with them and effectively remove them without compromising the product’s quality, functional dependability, or ability to provide services. The principles and steps of value analysis/ value engineering will supplement the effort of design thinking.
5.2 Symptoms for Introducing Value Analysis (VA)
If one or more of the following symptoms exist, starting a VA programme is expected to yield very positive effects.
i. Sales of the company’s products are declining.
ii. The prices of the products of the company are greater than those of the products of its rivals.

Introduction

Soil is a dynamic natural body, characterized by high degree of spatial variability due to combined effect of physical, chemical or biological processes that operate with different intensities at different scales (Goovaerts 1998). Reports have shown that there is large variability in soil properties, crop yield, disease, weed etc., not only in large-sized fields (Godwin and Miller 2003,Vrindts et al. 2005), but also in small-sized fields (Bhattacharya et al. 2008). Therefore, knowledge of hydro-physical properties of soils is crucial for management practices for all kinds of agricultural practices for efficient utilization of input resources. Plant growth and development is highly related to soil hydro-physical properties, which not only depend on soil intrinsic textural and chemical characteristics (Hamza and Anderson 2005) but also on soil management.

Soil physical properties which change with the variation in soil structure include bulk density, total porosity, pore geometry (size distribution, shape, continuity and tortuosity), penetration resistance and aggregate stability, consequently affecting soil hydraulic properties such as water retention characteristics, plant available water capacity, infiltration capacity and saturated hydraulic conductivity. These soil properties are dependent on seasonal climatic conditions, management practices, crop development and biological activity (Reynolds et al. 2007). The hydro-physical properties of soils, i.e., water retention and water permeability in both saturated and unsaturated zones not only shape soil water balance but also decide the conditions for plant growth, development and yield. They also determine water availability for the plant root system and the transfer of water with chemical compounds dissolved in it into deeper layers.

Biometrical Genetics - Historical Background

It is a science built on the principles of Mendelian genetics and the way in which the genetic factors influence the statistical properties of traits in families and populations. The roots of biometrical genetics go back to Gregor Mendel, who first demonstrated the existence of the hereditary factors and to Francis Galton who first applied statistical methods to study biological inheritance. Galton introduced the concepts of regression and correlation for studying the continuous variation in humans, but was not aware of the principles of genetics discovered by Mendel. Galton, Karl Pearson and Walter Weldon together started the journal Biometrika in 1901, with the aim of promoting the statistical studies in biological phenomena. The differences of opinion persisted between the Biometricians and Mendelians in the early 20th Century. In 1918 Ronald A. Fisher published a landmark paper ‘The correlation between relatives on the supposition of Mendelian inheritance, in which he showed the two seemingly contradictory approaches could be reconciled in terms of a simple biometrical genetic model. Ronald A. Fisher’s paper laid the foundation for the discipline of Biometrical Genetics.

Foundation

a) Mendelian Inheritance

Mendel proposed three principles based on his work and now known as Mendel’s laws of inheritance

• Law of uniformity: if two homozygote parents of different alleles cross together, all offspring must have the same genotype

• Law of segregation: an individual receives with the equal probability one of the two genes from the genotype of the mother and similarly from that of the father.

• Law of independent assortment: the segregation of the genes for one trait is independent of the segregation of genes for other traits in the formation of reproductive cells

In association studies, the law of segregation is the basis for the transmission disequilibrium test (TDT). The possible violation of law of independent assortment forms the basis of the gene mapping technique, linkage analysis. These principles are used to check the quality of parent-offspring genetic data. Mendelian principles though developed for dichotomous traits, they can be applied to the inheritance of traits influenced by multiple genes and are fundamental to genetic analysis.

Veterinary Nuclear Medicine (VNM)

Veterinary Nuclear Medicine (VNM) is a specialized field that uses radioisotopes to detect, diagnose, and treat diseases. Gamma scintigraphy involves injecting radioactive tracer pharmaceuticals into the animal’s body and imaging them as they move through the body using a gamma radiation-sensitive camera. This technique provides a 2D or 3D image of the whole body or a specific organ, allowing for the estimation of the quantitative distribution within the region of interest (ROI). Advancements in gamma scintigraphy have allowed for the acquisition of information about both static and dynamic high-resolution images, such as heart wall motion, blood flow, and myocardial perfusion. Unlike other diagnostic modalities viz X-rays, ultrasonography, and CT; gamma scintigraphy offers functional information of specific organs. The most commonly used gamma radiation-emitting isotope is 99m-Technetium (99mTc), which emits a gamma energy of 98% 140-keV photon with only 10% internal conversion. Other radionuclides used in veterinary nuclear medicine facilities include iodine (123I and 131I), Gallium (67Ga), and Thallium (201Ti).

The human civilization and environmental eco-system are the two most important components of real world. The technology intervention to safeguard human civilization and the environment has always been the foremost priority of scientific and engineering community through their innovative expeditions.

10.1 Introduction
Having explored the advanced analytical techniques that allow us to extract meaningful insights and build predictive models from meteorological data, we now arrive at a crucial stage in the scientific process: communication and understanding. The chapter, “Visualization of Meteorological Data,” focuses on the art and science of transforming complex numerical datasets into insightful visual representations. In a field as inherently spatial and temporal as meteorology, effective visualization is not just about making pretty pictures;it is a fundamental tool for exploration, analysis, and the clear conveyance  of information about atmospheric phenomena (Fig. 10.1).
Consider the sheer volume and multifaceted nature of meteorological data. We grapple with variables spanning three spatial dimensions (and often more when considering altitude), evolving dynamically over time, and encompassing a wide array of physical quantities – temperature, pressure, wind vectors, humidity, precipitation rates, radiation fluxes, and more. Raw numbers and statistical summaries, while essential for quantitative analysis, often fail to reveal the intricate spatial patterns, temporal evolutions, and interrelationships that characterize weather and climate. This is where visualization steps in,
acting as a bridge between the abstract world of numerical data and our intuitive human capacity for visual pattern recognition.