eChapters

Extensive studies have been carried out to explore the possibility of using mycorrhizal model system in inducing suppressions of potential soil-borne pathogens. Its relevance becomes all the more important since most of the soil-borne diseases, by and large, are not easily controlled by conventional methods.

Diseases due to Fungal Pathogens

Most of the studies conducted on the interaction of VAM fungi and fungal pathogens suggest that mycorrhizae decrease the severity of disease, while a few studies suggest that there is either no effect or there is an increase in the severity of disease. It has been pointed out that in view of the fact that VAM fungi deter the severity of disease they may be used as biocontrol agents. Various mechanisms by which VAM could suppress the fungal pathogens have been suggested on the basis of experimental evidences.

Factors which influence the colonization and life cycle of VAM fungi have important bearing on the extent to which they may benefit their host. As we move closer to using mycorrhizal fungi to increase growth and productivity of different plant communities, a thorough understanding of the factors which influence their survival, germination, root colonization an d sporulation will be critical. Seasons, physico-chemical characters of the soil, agronomic practices, plant protection chemicals, pollutants and genotypic characters of the host may affect VAM fungi and their potentiality to colonize and provide benefit to the host. In view of this, the effect of such factors on the VA mycorrhizal symbiosis and its benefit to diverse plant communities has been extensively investigated worldover.

Of the various microorganisms present in and around roots, VAM fungi occupy unique ecological position as they are partly inside and partly outside the host. Their internal phase encounters competition and antagonism from other beneficial symbiotic microbes while their external phase faces competition and antagonism from beneficial non-symbiotic microbes present around the roots. Since they have an ensured source of nutrients from the host, they have comparatively better conditions for their growth and may exert greater effect on plants with other symbiotic and non-symbiotic microbes in and around their roots. It has been pointed out that investigations are needed on the interactions between VAM fungi and these beneficial microbes, and their effect on growth and productivity in plants. A bulk of information has been collected related to this aspect; however, authentic generalization has not been possible as yet. Following description gives an account of the work done by Indian mycorrhizasts on the interactions between VAM fungi and other beneficial symbiotic/non-symbiotic microbes in and around the roots.

Favourable effect of VAM association in plants has been attributed to an increased nutrient uptake by mycorrhizal roots. The transfer of ion from the soil to the roots by mass flow or by diffusion depends on the mobility of that particular ion in the soil. Ions such as NO₃^- ,SO ₄^— and Ca⁺⁺ may move rapidly through the soil to the roots by mass flow, and uptake would be limited by the absorbing capacity of the roots. On the other hand, ions such as H₂PO₄^-, NH₄⁺, Zn⁺⁺ and Cu⁺⁺ are poorly mobile and move to the roots by diffusion, the rate limiting steps is likely to be the movement to the root surface. Under such conditions, it is expected that uptake would be increased if ions could be moved rapidly through extramatrical hyphae of the VAM fungi rather than diffusing slowly through the soil. It is now well established that VA mycorrhizae enhance the uptake of phosphate by the roots but it is likely that they can also assist in the uptake of other ions. The following account provides an information on the findings of Indian mycorrhizasts on the effects of VA mycorrhizal association on nutrient uptake in the plants and its physiological implications.

In recent years the importance of VAM fungi for plant growth in most soils has been accepted and acknowledged by soil chemists, agronomists, horticulturists and farmers. Ever since it was shown that VAM could increase nutrient uptake and water uptake from soil in plants, researchers have been trying to extend and manipulate this effect of mycorrhiza in improving the growth and productivity in various groups of plants. Mycorrhizasts of the country have also explored the possibility of utilizing this tool for improving the performance of various crops including cereals, millets, pulses, vegetables, oilseeds and fruit crops. In addition, they have tried to explore the possibility of utilizing this biotool also for improving the growth of various types of trees including forest trees. Following account is related to the work done by the mycorrhizasts of the country on the effect of VA mycorrhiza on the growth and productivity of various groups of plants including cultivated crops.

VAM fungi are of worldwide distribution. They are present in the soil in form of chlamydospores, zygospores and azygospores and have been recovered from the soils of a variety of habitats e.g. agricultural fields, nutrient deficient soils, sand dunes and deserts, industrial wastes, sodic soils, polluted sites, sewage, eroded sites and others like forests, open woodlands, scrub, savana, heaths, grasslands, anthracite and bituminous coal wastes. There are several reports of VAM fungi from diverse habitats from India.

VA-mycorrhizal associations occur widely throughout the plant kingdom. They have  been reported to be present in Bryophytes, Pteridophytes, Gymnosperms and Angiosperms in nearly all the geographical regions of the world. Most of the agricultural and horticultural crops belonging to diverse families have been shown to form VA-mycorrhizae. Many species of plants of economically important families have been reported to be mycorrhizal. Likewise, many of the plantation crops like coffee, tea, citrus, timber trees, many of the fruit trees and medicinal plants have been shown to form VA-mycorrhiza.

Mycorrhiza research is relatively recent. Although, it was in 1885 that A.B. Frank made reference for the first time to the term “Mycorrhiza”, it took him nine more years to establish the symbiotic relationship between the fungus and its host plant. It took a much longer time to convince the rest of the scientific community of the validity of Frank’s theory. Researches carried out between 1917 to 1950 confirmed that “Mycorrhizal colonization is a beneficial symbiosis between tree and fungus”. It is an unique partnership in nature, where the higher plants share their photosynthates with their fungal partners and provide them shelter, the fungal partners in return provide protection to the root system and assist it in performing its functions especially anchorage and absorption of nutrients / water from soil more efficiently.

The ectomycorrhizal fungi have been in use as a biological tool in improving the performance of forest trees since long. Better performance of forest trees is ensured by making them mycorrhizal, employing efficient strains of ectomycorrhizal fungi.

Abstract

Cyanobacteria were first oxygen evolving photosynthetic organisms on Earth, which not only survived under harsh conditions of primitive Earth but are also dominant and efficient photosynthesizers and N₂ fixers of present time which significantly contribute to fertility of paddy fields as natural biofertilizers. Cyanobacteria require solar radiation to carry out photosynthesis and nitrogen fixation. The stratospheric ozone depletion which has resulted in an increase in ultraviolet-B (UV-B; 280 - 315 nm) radiation on the Earth’s surface has been reported to inhibit a number of photochemical and photobiological processes in cyanobacteria. However, they have evolved mechanisms such as synthesis of photoprotective compound scytonemin and their derivatives to counteract the damaging effects of UV-B. In addition to Photoprotection, scytonemin has biotechnological potentials and several applications in biology and allied sciences.

In factorial experiment it may sometime happen that certain factors require bigger plots than other for convenience of operation of the experiment.  For example, in an agricultural experiment involving two factors, say, irrigation and fertilizer, it is organizationally very inconvenient to apply different levels of irrigation to small neighbouring plots.  But there is no such difficulty for the application of different levels of fertilizer. To meet such situations it is desirable to have different sizes of the experimental units in the same experiment.

In the foregoing experiments performed either in CRD or RBD or LSD. We were primarily concerned with the comparison and estimation of the effects of a single set of treatments like varieties of paddy, different methods of cultivation etc., and such experiments which deal with one factor only may be called simple experiments. In factorial experiment the effects of several factors of variation are studied and investigated simultaneously in a single experiment, such experiments are known as factorial experiments. In these experiments an attempt is made to estimate the effects of each factors and also the interaction effects i.e. the variation in the effect of one factor as a result to different levels of other factors.  These experiments provide an opportunity to study not only the individual effects of each factor but also interactions.  In many biological and clinical trials factors are likely to have interaction. Therefore, factorial experiments are more informative in such investigations.

In many situations, experiments may not known in advance which contrasts they wish to compare, or they may be interested in more than (m – 1) possible comparisons.  In many exploratory experiments, the comparisons of interest are fixed only after preliminary examinations of the data.
 
13.1 Scheffe’s Method for Comparing All Contrasts

Scheffe (1953) has proposed a method for comparing any all possible contrasts between treatment means.  In the Scheffe method, the type I error is at most a for any of the possible comparisons.

12.1 Introduction
 
The design of the experiment includes (i) planning of the experiment (ii) obtaining the relevant information from it regarding the statistical hypothesis under study and (iii) making a statistical analysis of the data. Design of experiment may be defined as the logical construction of the experiment in which the degree of the uncertainty with which the inference is drawn may be well defined. It may also be redefined as the choice of treatments, the method of assigning treatments to experimental units and arrangement of experimental units in varies patterns to suit the requirement of particular problems, are combined known as the design of experiment.

11.1 Introduction

The analysis of variance is a powerful statistical tool for tests of significance.  The test of significance based on t-distribution is an adequate procedure only for testing the significance of the difference between two sample means. In a situation when we have three or more samples to consider at a time an alternative procedure is needed for testing the hypothesis that all the samples are drawn from the same population, i.e., they have the same mean. For example five fertilizers are applied of four plots each of wheat and yield of wheat on each of the plot is given we may be interested in finding out whether the effect of these fertilizers on the yields is significantly different or in other words, whether the samples have come from the same normal population. The answer to this problem is provided by the technique of analysis of variance. Thus basic purpose of the analysis of variance is to test the homogeneity of several means.

10.1 Introduction

In previous lessons we have been dealing with problems connected with variation occurring in any one variable, say the income of a person, the height or weight of an individual, the wage of a worker in a factory, the sales of a shop, demand of a commodity etc.  The values of a single variable were obtained from every member of the population and the different characteristics were described by certain descriptive constants after grouping into frequency distribution.  In this lesson, we shall be dealing with problems wherein there are paired variables and every individual or member of the population exhibits two values, one for each of the variables under consideration.  Example of the two variables which are to be studies together are (i) the income and expenditure of a household, (ii) the price and demand or supply of a commodity, (iii) the height and weight of a student, and (iv) the grain yield and straw yield of paddy, (v) Height of father and height of son, etc.

We have seen the procedures of testing hypothesis about population parameters in chapter 11. Frequently our interest is to test whether a set of observed values are in agreement with those which would occur if some specified hypothesis were true. The X² statistic provides a measure of agreement between such observed and expected frequencies.

The X² distribution has a number of applications in testing of hypotheses. It is used to test, (1) the independence of attributes, (2) the  goodness of fit, (3) the homogeneity of variances, (4) the homogeneity of correlation coefficients, and (5) the linkage in genetic problems. For applying X² test procedure we have to use only the actual observed frequencies and not the percentages or ratios. Further, the samples should be independent and the observations within a sample should be non-overlapping. The expected frequency in each category should be more than 5. The number of observations should be large, say, more than 50.

8.1 Statistical Hypotheses

in attempting to reach decisions about the populations, it is useful to make assumptions or guesses about the populations involved. When the assumption or statement about a phenomenon occurring under certain conditions is formulated as proposition it is called a scientific hypotesis. We can construct criteria by which a scientific hypothesis may be ‘the  yield of a new paddy variety will be 3500 kg per hectare’. In statistical language it may be stated as ‘the random variable (yield of paddy) is distributed normally with mean 3500 kg/ha’. Such a statement or proposition about the probability distribution of a random variable is called the statistical hypothesis.

7.1 Meaning of Sampling
    
Before we consider sampling theory, we first define the terms population and sample.
 
The word ‘population’ or ‘universe’ in statistics is used to refer to any collection of individuals or of their attributes or of results of operations which can be numerically specified. For example the assemblage of heights of all the individuals in the population is called a population of heights.  Similarly we may consider the population of weights, wages, yields of grain; prices of wheat, mileages of automobile tyres, etc. a population containing a finite number of individuals or members is called a finite population.  For instance the population f ages of the workers in a factory constitute a finite population of ages. A population with infinite number of members is known as infinite population.  For example the population of pressures of temperatures at various points in the atmosphere is an infinite population. 

It has been a general notion that if an experiment is conducted under identical conditions, values so obtained would be similar. But the experiences of people have dispelled this belief. Observations are always taken about a factor or character under study, which can take different values, and the factor or character is termed as variable.
Hence, we have a set of outcomes (sample points) of a random experiment. A rule that assigns a real number to each outcome (sample points) is called random variable. The rule is nothing about a function of the variable, say, X, that assigns a unique value to each sample point of the sample space.

In day-to-day life, one comes across many statements which create doubt about certain happenings and one may want to determine the chance of occurrence of a particular happening, e.g., if one desires to know the chance of rain based on some metrological observations.  In the case of coin tossing, one wants to know what will be the chance of winning if a person always stakes for head in a fixed number of tossing. There can be innumerable such queries which one would like to make and meanwhile evaluate mathematically the chance of the occurrence of such possibilities.  This invoked the need of probability theory. The theory of probability came in existence when problems of games were referred to mathematicians like Karl Pearson, Laplace and others. Complicated applications involving advanced theory of probability and axionmatic approach of probability are not dealt within this book.

4.1 Introduction and Measuring

A measure of central tendency exhibits only one of the important characteristics of distribution, namely they typical representative figure to the whole set of its values. This measure alone will not adequately describe a set of observation unless all the observations are alike. Moreover one cannot understand completely about the data set with a measure of central tendency.  Because in real practical situation there may exits a number of sets of observations (or different distributions) whose measures of central tendency are same but they may differ individually from each other in a number of ways.  In particular two sets of values have the same mean but whilst one set of data is grouped close to the mean the other one is more spread out.  The following examples will illustrate this view point clearly.