eChapters

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations.

Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

Correlations and regression are important components of animal breeding analysis. The genetic correlation is an important genetic parameter that is useful in the planning of breeding programs involving multiple traits. In this chapter, the basic concepts of correlation and regression including method of estimation, assumptions and test of significance are presented. An example using SPSS software is also given at the end for better understanding.

Estimation of variance is primary for any breeding program as all the genetic parameters are dependent on these estimates. The properties of an estimate, their assumptions, type of data, availability of data, and computational requirement make it important to understand the method of estimation of variance components so as to make a wise choice that would suit the breeder’s requirement. This chapter explains the methods of estimation of variance with merits and demerits of each and the reader could use these as a guide to choose the right method of estimation.

The genetic parameters are the basis for any animal breeding program and the important parameters such as heritability, repeatability and genetic correlation are ratios of (co)variances and their reliability depends on the accurate estimation of variance components. Estimates of variance components are used extensively in animal breeding.

7.1 The Formula Palette

By clicking on the equals sign in the formula bar a drop – down panel called the formula palette appeared.

Consider an example, type number 25 in cell A1 and move the cell pointer to cell B2. type or click the equal sign in the formulabar. Adrop – down panel will display formula result = and wait for further inputs. Enter sqrt result to 5 at B2.

The dialog box divides into parts as

(a) Name of the function at the top left of the argument entry box.

(b) Below of this there is the definition or description of the function.

(c) Next, there is an entry explanation.

(d) Formula result.

Things to remember: Any statistical or mathematical function can be activated with = (equal to) sign.

Diagrams and graphs are the effective and attractive ways of presenting data. Widely used diagrams for data presentation are pi chart and bar diagrams. Bar diagrams are also of various types; (i) Simple bar diagram (ii) Component or sub-divided bar diagram (iii) Multiple bar diagram (iv) Percentage bar diagram. The widely used graphical presentation are histograms, frequency polygon and frequency curve.

There are numerous libraries to create charts and graphs in R programming language.

Pie-chart- A pie-chart is a representation of values of variable as sectors of a circle with different colours/shadings. The colours/shadings used for different sectors are indexed at the side of the graph. pie() function is used to create pie chrt in R.

Let us create an exel file names as ‘exp_sports’ which contains the expenditure made (in Rs.) under various sports in a sports club. The name of the sports are cricket, hockey, tennis and football. The names of sports are placed under the column head ‘sports’. The corresponding expenditure (in Rs.) are 7200, 3600, 1800, 1800 and they are placed under the column head ‘expenditure’.

The basic syntax for creating a pie-chart using the R is.

1.1    General Introduction, History, Ownership of Tangible and Intellectual Property
    
1.2    Patentable Subject Matter
    
1.3    Basic Requirements of Patentability
    
1.4    Novelty, Public Domain, Obviousness and Non Obviousness
    
1.5    Inventive Step (Non-Obviousness) and Industrial Application

Introduction

A fundamental issue when analysing trade policy reform in global seafood market is the extent to which domestic agricultural commodity markets in developing countries respond to changes in international prices. Price trans mission from the world to domestic markets is central in understanding the extent of the integration of economic agents into the market process.

Studies on the transmission of price signals are founded on concepts related to competitive pricing behavior. In spatial terms, the classical paradigm of the Law of One Price, as well as the predictions on market integration provided by the standard spatial price determination models (Enke, 1951; Samuelson, 1952; Takayama and Judge, 1972) postulate that price transmission is complete with equilibrium prices of a commodity sold on competitive foreign and domestic markets differing only by transfer costs, when converted to a common currency. These models predict that changes in supply and demand conditions in one market will affect trade and therefore prices in other markets as equilibrium is restored through spatial arbitrage.

The absence of market integration, or of complete pass-through of price changes from one market to another has important implications for economic welfare. Incomplete price transmission arising either due to trade and other policies, or due to transaction costs such as poor transport and communication infrastructure, results in a reduction in the price information available to economic agents and consequently may lead to decisions that contribute to inefficient outcomes. Fisheries and seafood food trade policy reform, especially, is a priority issue in the next WTO negotiations, as trade liberalization is viewed as encouraging allocative efficiency and long run growth.

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.

9.1 Introduction Numpy

Numpy: is apython package which stands for “Numerical python”.

This was created in 2005 by Travis Oliphant.

Using numpy we can perform the following functionalities:

1. Mathematical and logical calculation on array.

2. Fourier transforms and routines for shape manipulation.

3. It has built in functions for linear algebra random number generation.

4. Used for scientific calculations.

5. It is faster than python list.

6. It is fast because it is associated with c programming.

NumPy is often called as an alternate for MatLab (a programming platform designed specifically for engineers and scientists for data analysis, developing algorithms, create models and application etc.)

“Necessity is the mother of invention”, all of us must have heard this statement at some juncture in our lives. Most of us may even have even gone through the motion of inventing something when needed. However, more and more inventions happening today may not necessarily be necessities. These inventions definitely perform the task of improvingand making convenient our overall living environment. Past few years have seen an increasing awareness and understanding in the field of intellectual property. Among all aspects, one aspect of intellectual property that is gaining increasing attention of intellectual property owners is management of the intellectual property rights.

This brings to our minds one question, how was intellectual property management practised by early mankind? We all do know that early mankind has left no stone unturned to make theabove statement”Necessity is the mother of invention”, a reality. Mankind prospered by leaps and bounds on account of sharing their various discoveries and inventions. Way back then knowledge was meant to be shared, as aptly captured in the Sanskrit Sloka:

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.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

Introduction
 
Arid western part of Rajasthan which contains the Indian segment of the Thar Desert, accounts for ~62% of the country’s hot arid area and is a region of low rainfall. Under such physico-climatic situation, fluvial processes function much less frequently and more episodically than the aeolian processes, but their impacts on landforms and society are large, as noticed on several occasions in arid regions. There is a distinct east-west gradient of rainfall and temperature in the region. Along the eastern margin bordering Aravallies, the mean annual rainfall is 500 mm while in the westernmost part at Jaisalmer, it is less than 150 mm. The rainfall is largely monsoon driven which brings 80 - 90 per cent of total rainfall received during the year. There is large inter-annual rainfall variability of rainfall which leads to climatic extremes of drought and floods of different magnitudes. For example on an average, the Thar Desert in India experiences flood-causing rainfall in 2-3 years in a decade, but a maximum of 5 flood causing rainfall years in a decade was recorded during the 1950s, 1970s and the 1990s (Rao et al. 2006).

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.

ABSTRACT

This paper discusses IPR concepts in the light of Indian culture and issues emanating from enforcement of those concepts in developing countries such as India.

INTRODUCTION

The adage “Knowledge is wealth” suits the present era well than any other period. India is known for its innovative thoughts since ages. However, it never monopolized the knowledge it possessed. In India, the knowledge of a person is thought to be utilized for the benefit of the society as an obligation of the knowledge holder. Does this mindset of Universal humanity (Vasudevo Kudumbagam) and common good (Sarva Sugino Bavandu) would benefit us in the materialistic society? This paper is an attempt to answer this query.

1.0 Introduction

Population in dry lands of the world is increasing; more fresh water is required in order to meet the growing demands. To meet this challenging situation developing new technologies for identification of potential sites for rainwater harvesting (RWH) is an important step towards maximizing water availability in semi-arid areas.

Mungbean is one of the most important pulse crops. It is grown in almost all parts of the country. Mungbean is primarily a crop of rainy season; however, with the development of early maturing varieties, it has proved to be an ideal crop for spring and summer seasons. Middle of March to last week of June is the most suitable growing period where there is a plenty of life-giving sunshine. During this period high temperature and low humidity keep insects and disease infestations at their lowest. Summer season offers an excellent opportunity for raising short duration pulses.

Experimentation is fundamental to scientific investigation because it provides a systematic basis for studying natural and controlled systems and drawing conclusions from observations. In forestry and horticulture, experiments are often affected by developmental, environmental, genetic, sampling, and experimental variations. This chapter introduces the principles required for planning, conducting, analysing, and interpreting such experiments. It explains comparative experiments, the need for experimentation, experimental units, sampling units, plots, blocks, treatments, factors, levels, and sources of variation. Particular emphasis is given to the basic principles of experimental design—randomisation, replication, and local control—which help improve validity, precision, and coverage of experimental results.

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

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.