eChapters

12.1 Section-A : Write True / False

Q.1 Almost all biological data e.g. crop yield, plant height, seed weight, seed size, protein content in pulse, oil content in oil seed etc. are assumed to be normaly distributed.

Q.2 Let X be a normal variable with N(μ, σ²). The random variable Z def ned as Z = (x - μ)/σ, is known as standard normal variable.

Q.3 The standard normal variable Z is denoted as Z ~ N (0, 1).

Q.4 The p.d.f. of random variable Z is given as

11.1 Section-A : Write True / False

Q.1 Some assumptions or guesses made about the unknown population parameters, is know as "Statistical hypothesis".

Q.2 For the normal distribution N(μ, σ²), the statement (1) μ = 25.0 and (ii) σ² = 37.0, are known as statistical hypotheses.

Q.3 Let p = 0.5 is the probability of getting a head in single throw of a coin. Ten, this statement is not a statistical hypothesis.

Q.4 The statistical hypothesis under consideration (under test), is known as "Null hypothesis".

Q.5 The null hypothesis is denoted by "Ho".

Section-A : Write True / False

10.1.1 Normal Distribution

Q.1 A r.v. X is said to have a normal distribution with parameters (μ, σ²) if its p.d.f. is given by the probability law

9.1 Section-A : Write True / False

Q.1 The probabilistic laws for dif erent values of a random variable are made on the basis of given conditions. T ese conditions are known as "prior considerations".

Q.2 If the probabilistic laws are made for dif erent values of a random variable on the basis of results obtained from an experiment, then such results are known as "a posterior inferences".9.1.1 Discrete Uniform Distribution

Q.3 A random variable X is said to have a discrete uniform distribution over the range [1, n] if its p.m.f is written as

Section-A : Write True / False

Q.1 The moment generating function (m.g.f) of a random variable X (about origin having the probability function f (x) is defned as

Section-A : Write True / False

Q.1 The average value of a random fenamenon is termed as “Mathematical Expectation” or “Expected value” of random fenamenon.

Q.2 The mathematical expression to compute the expected value of a discrete random variable X with its p.m.f. f(x) is def ned as

Section-A : Write True / False

Q.1 A random variable (r.v) is a real number x connected with the outcomes of a random experiment.

Q.2 In tossing of a balanced die at one time, the number coming on the upper most face of the die is a random variable x which takes any one out of 6 values taken as 1, 2, 3, 4, 5, 6.

Q.3 Let s be the sample space associated with a given random experiment. A real valued function defned on s and taking values in R (-∞,∞) is called one dimensional random variable.

Q.4 In general a n dimensional random variable is simply a function whose domain is S and whose range is a collection of n-tuples of real members (vectors in n-space).

Section-A : Write True / False

Q.1 If an experiment is repeated under an identical conditions, we generally come to a result (i.e. outcome) which is unique or certain.

Q.2 In an experiment, the result (outcome) may not be unique but it is one of the several possible outcomes.

Q.3 The phenomena described in Ql, are known as “deterministic or predictable”.

Q.4 In deterministic phenomena, the results can be predicted with certainity

Q.5 A deterministic model stipulates that the conditions under which an experiment is performed, determine the ouctome of the experiment

Q.6 The phenomena which do not lead themselves to deterministic approach, are known as “Unpredictable or probabilistic” phenomena.

Q.7 The phenomena frequently occured in business, economic and social science or even in our day to day life, are known as “deterministic phenomena”.

Q.8 A manufacturer can ascertain the future demand of his product with certainity.

Q.9 A sales manager can not predict with certainity about the sales target next year.

Section-A : Write True / False

Q.1 Dispersion of a given set of values, gives an idea about the homogeneity or heterogeneity of the distribution of values.

Q.2 The measure of scatteredness of a set of values about an average is known as the “measure of variation or dispersion”.

Q.3 The measure of dispersion must be based on all the observations.

Q.4 Measure of dispersion should be amenable to further mathematical treatment.

Q.5 Measure of dispersion should be affected as little as possible by fluctuation of sampling.

Q.6 The measures which express the spread of observations in terms of distance between the values of two selected observations, are known as “distance measures” e.g. range and interquartile range (or quartile deviation).

CONTRACEPTION DUE TO MISALLIANCE AND TERMINATION OF PREGNANCY IN BITCHES

It is obvious that breeders maintain varieties of breeds but mismating do occur within their own premises and sometimes by unwarranted stray animal.

Misalliance complicates too many other problems.

Incidence of mismating is 15 to 20 percent in canine breeding.

TRUE ANESTRUS IN A HEIFER OR POST PARTUM COW / BUFFALO

The animal has both ovaries small and inactive

No palpable structures in the ovaries

Animal does not exhibiting estrus signs

Accurate early diagnosis of pregnancy is essential for successful breeding programme.

Ability is required for successful practice.

Skill is to be developed to indicate the duration within weeks in early and months in late pregnancy.

RECTAL EXAMINATION

It provides practically all the information required.

It is the simplest, accurate and reliable method with no cost involved.

Things Required

A full arm rubber sleeve with attached glove or disposable plastic glove.
A rubber or plastic apron.
Non-irritant soap or lubricant.

There is widespread belief now that forests can be used to reduce the costs of slowing climate change. While the role of forests in the global carbon cycle has long been acknowledged, recent discussions within the context of the United Nations Framework Convention on Climate Change, as well as efforts to write climate change legislation in the United States, have emphasized the role forests might play. The most recent policy efforts have focused on near-term actions to reduce deforestation in tropical countries. The rationale for considering forests at all in the policy mix stems partly from the physical components of the issue. The world’s forest estate is exceedingly large:

As a developing country, India does not have any emission reduction target, but it is able to sell CERs pursuant to the CDM, to large emitters covered by the EU ETS, countries that have emission reduction targets under the Kyoto Protocol, or any other entity that wishes to purchase such CERs for compliance purposes. It can also supply ERs and VERs for the growing voluntary markets.

4.1 Status and Trends

India’s participation in the carbon markets has contributed to the recognition that it is a useful tool in attracting climate friendly investments. India was an early player in the market, and it used the hosting of the Eighth Conference of Parties to the UNFCCC in Delhi in October 2002 to sensitize the business community about the opportunity provided by carbon finance and the modalities of the emerging CDM. In 2004, the growth in the Indian carbon market was fostered by a healthy number of indigenous management and technical consultants, which began to offer services to potential sellers of emission reductions, and the response from primarily the private sector to capacity building initiatives concerning the nascent market. In its capacity as the Designated National Authority (DNA) for the CDM, the Ministry of Environment and Forests (MOEF) also had the foresight to develop simple and transparent rules, whereby project developers can obtain a Letter of Approval (LOA) theoretically within sixty days, which is a key step required for project registration. By the end of 2004, India was the market leader in the forward sale of emission reductions, with 50 % of the supply market. Early successes included the registration of two projects which consisted of the thermal oxidation of HFC-23 generated as a buy-product during the manufacturing of the refrigerant HCFC-22, but also a number of small scale co-generation projects using bagasse, rice husk and mustard crop residues as a source of biomass, and small scale hydropower projects.

3.1 Overview of the Global Carbon Market in Pre-Kyoto Protocol Phase

At the First COP in Berlin in 1995, the UNFCCC launched a pilot phase of projects to build experience and “learning by doing”. The pilot phase was termed Activities Implemented Jointly (http://unfccc.int/program/coop/aij/aij_np.html). Under the Activities Implemented Jointly, Annex I Parties could implement projects in other countries that reduced emission of greenhouse gases or enhanced removals of sinks. The number of projects registered under Activities Implemented Jointly has been growing since its launch and as of 2002 (the latest report available) there were 157 projects – 45 percent of which are in developing countries. The total amount of money invested by private sector companies as of 2000 was about $160 million. Because of the expectations that sink projects would be included as a mechanism for achieving targets, several pilot projects were voluntarily implemented involving mostly private sector entities (e.g., electric utility companies, oil companies and environmental non-governmental organisations). Between the beginning of 1996 and the end of 2003, 76 transactions involving carbon sequestration in the LULUCF sector were signed, representing 40 million tCO2-e. Relative to all of the carbon transactions that took place over the same period, sink projects accounted for 21 percent of the total number and 23 percent of the total volume. In volume terms, carbon sequestration projects were the single largest project category, and more than three-quarters of this volume was transacted between 1996 and 1999.

2.1 Overview of the CDM Project Cycle

The ultimate governing body of the CDM is the Conference of the Parties/Meeting of the Parties to the Kyoto Protocol (COP/MOP). The COP/MOP is the annual meeting of the signatories to the Kyoto Protocol and is responsible for managing the CDM Executive Board and for overseeing its work. The Executive Board supervises the CDM process under the authority and guidance of the COP/MOP; it is fully accountable to the COP/MOP. The Board decides on rules for the implementation of the CDM, as well as making the final decisions about acceptance of methodologies, registration of projects and issuance of CERs. The Board is composed of 10 members and 10 alternates. The CDM Executive Board created an A/R Working Group to specifically oversee LULUCF under the CDM. The A/R Working Group provides guidance to LULUCF project developers and, using expert reviewers, makes recommendations on acceptance or rejection of LULUCF methodologies.

1.1 Policy Background

Human activities have resulted in the alteration of the composition of our atmosphere triggering change in the Earth’s climate. The world’s population has grown at an alarming rate with a corresponding increase in demand for natural resources, energy, food, and goods. As a consequence of increase in consumption, vast quantities of gases and effluents are discharged that change the composition of the atmosphere and its capacity to regulate its temperature. The rise in the global temperature is caused by the accumulation of the so-called “greenhouse gases”, namely, carbon dioxide (CO₂), methane, nitrous oxide, and chlorofluorocarbons. Energy received from the sun is absorbed as short wavelength radiation and is eventually returned to space as long wavelength infrared radiation. Greenhouse gases absorb the infrared radiation, trapping it in the atmosphere in the form of heat energy.

16.1 Introduction

The ancient Greek philosopher ‘Heraclitus’ said, that, “You can’t step into the same river twice”. By this, he meant that permanence is an illusion and changes are inevitable. One of such changes, which has created buzz all over world is climate change. To understand the science behind climate change, the first step would be to know about the periodic changes in earth’s climate. Scientists devised innovative techniques to recover evidence from the distant past, first from deposits left on land, then from sea floor sediments, and one of the finest techniques of drilling deep into ice. Evidences from past climates help us to understand the evolution of the earth’s atmosphere, oceans, biosphere, and cryosphere. At the same time, paleo-climate studies help in quantification of properties of earth’s climate, including the forces that drive climate change and the sensitivity of the earth’s climate to those forces. When scientists investigate causes or complex processes in the present and future, they may find evidence in the past that helps them in mapping out the complexities (McMullen and Jabbour, 2009). The paleo-climatologists succeeded brilliantly and discovered strangely regular pattern of glacial cycles, which helps in prediction of future changes.

15.1 Introduction

There is now adequate evidence about the impending global climate change and the consequences thereof. The fourth assessment report of IPCC observed that ‘warming of climate system is now unequivocal, as is now evident from observations of increase in global average air and ocean temperatures, widespread melting of snow and ice, and rising global sea level’ (IPCC, 2007). Though climate change is global in its occurrence and consequences, it is the developing countries like India that are likely to face more adverse consequences. Globally, climate change is seen as a failure of market mechanisms wherein the polluters haven’t had to pay for the negative externalities (Stern, 2007) that their economic activities caused.