eChapters

The deliberation on the methodology has been made to understand the concept, methods and techniques which have been executed for delineating the study, collection of information, analysis of data, and interpretation of the findings for the revelation of hard evidences and formulation of theories. This chapter deals with the method and procedure used in the study consisting of eight main parts

A. Locale of research

B. Pilot study

C. Sampling Design

D. Item analysis and experts rating for devising scoring technique on dependent variables

E. Empirical measurement of the variables.

Research setting refers to the detail information of an area where the study has been conducted. The study area, generally comprises of a particular geographical area viz. a state, district, block, or gram panchayat area selected according to the convenience of the investigator who possesses adequate knowledge regarding the location, communication facility etc. of the locality so that he/she can easily approach each and every corner of the area for data collection. Here. in this study the data collection was done mostly over telephone based on the interview schedule due to Covid-19 pandemic situation. Besides the investigators must also have the basic knowledge about the socio demographic background of the people so as to have an easy understanding of their knowledge, attitude and behavior. 

Concept of uncertainty

Uncertainty refers to a scenario in which a person is unsure of what will happen, i.e. when all possible outcomes of events are unknown, and neither the likelihood nor the outcomes are known. Uncertainty cannot be quantified; however, risk may be quantified using probability principles.

Farming is a financially hazardous profession. Farmers face an ever-changing panorama of possible pricing, yield, and other outcomes that affect their financial returns and overall well-being on a daily basis. Because the effects of decisions or events are frequently unknown until long after they occur, the outcomes may be better or worse than planned. When aggregate crop output or export demand fluctuates dramatically, for example, farm prices can swing dramatically, and farmers may see returns that are significantly below their expectations (Bairwa et al., 2013).

Review of literature helps a scholar describing and citing related works to justify the topic and objectives set in the thesis work. This is a splendid scholastic exercise to vindicate and validate the reasons and output of the research work undertaken.

Following is the classified review of literature for a systematic illustration and interpretation of the problem and axioms:

Source of risk

Boris and Radivoj (2017) reveal that agricultural risk and uncertainty come from many different places, but they can be categorized into five management-related categories: production and technology, prices and the market, finance, legislation, and employees.

Uncertainty is an inevitable character of any system where most of the contradictory factors are either unpredictable or unfathomable. The higher is the complexity, the lesser would be the resilience. Agricultural production system, as it is managed in Indian condition, is the most vulnerable one to a plethora of uncertainties. Mostly managed in an open air rained conditions, it has been exposed to weather and resource uncertainties comprising of market volatility, poor access to technology and a fragile input delivery mechanism. The present study examines and depicts the possible issues and reasons for uncertainty.

15. 1 Introduction

Data collected in social sciences and economic study, are usually based on answers provided by the respondents. These answers are aften based on re-collection of past memories and often suffer from memory lapses, intentional erros etc. As such they provide only a crude informations which are never exact. The analysis of such data on the pattern of other biological data will not yield much information because many times they will not fulfil the underlying assumptions. For such data, the non-parametric (N.P.) methods are most appropriate.

15.2 Parametric Tests

The tests which deal with the parameters of the populations, are known as parametric test e.g. Z, t & F tests are the parametric tests.

14.1 Introduction

The theory of simple correlation (r_xy), regression coefficients (b_yx & b_xy) and their test of significance have been discucsed in chapter 13. The test of significance of observed partial correlation coefficient p₁₂₃(k+2) and multiple correlation coefficient R₁₂₃, have also been discussed in sections 13.4 and 13.5 respectively. Now in this chapter mainly the process of how to calculate spearman’s rank correlation coefficient, partial correlation and regression coefficients and multiple correlation coefficient using their fundamental definitions as well as appropriate statistical formule in terms of pairwise simple correlation coefficient r_ij (i = j = 1, 2, 3) in trivariate populations have been provided to make the students and its users to be familiar with basic and crystal clear concepts of the subject matter with the help of an empirical studies based on some original data on (X₁, X₂, X₃). Here X₁ has been considered as study variate and (X₂, X₃) as covariates.

13.1 Introduction

In the previous chapters, we have considered different tests of significance based on normal distribution, F distribution, student’s t distribution and Chi-square (x²) distribution which are frequently applied by the users of different disciplines of applied sciences and social sciences. In this chapter some more tests of significance are considered which have not been covered in those chapters. These tests are also based on t, F, Fisher’s z transformation,  and some other test statistics are also discussed one by one in the following sections.

13.2 t Test for Testing the Significance of an Observed Sample Correlation Coefficient r ^xy

Let r be the observed sample correlation coefficient based on a small sample of size n drawn from a bi-variate normal population with population correlation coefficient r. The standard error of r is given as

12.1 Chi-Square Variate

The square of a standard normal variate is known as a chi - square variate or chi square statistic with one degree of freedom. Thus if , then the variate Z defined as 

Z is a standard normal variate with mean zero and variance unity (one) and  = Z² is a chi - square variate (statistic) with one degree of freedom.

In general if xi, (i=1,2,.......n) are n independent normal variates with means μ_i and variance , then  is a chi - square variate (statistic) with n degree of freedom. It is denoted by the notation  and follows chi-square distribution. Symbolically, it is expressed as .

12.2 Chi-Square Distribution

Let  be a chi - square variate with n degree of freedom. Then its probability density function is written as

11.1 Introduction

In the preceding chapter 10, we have discussed in detail, the test procedures for which it has been assumed that the sample size n is large, (i.e n >,30). Those tests are also known as approximate tests or z tests. But in case when sample size n is small i.e. n<30, the approximate test fails to deal with such problem and we apply the theory of exact sample, for small sample tests. The exact sample tests however can be applied to large samples also though the converse is not plausible. In all the exact sample tests, the basic assumption is that the parent population is normally distributed.

In this part we will discusse in details the exact sample tests (based on exact sampling distributions namely snedekor’s F, student’s t and chi square (c²) distributions. These distributions will be simply introduced in short because main emphasis has to be given only on the test procedures based on them.

10.1 Normal Distribution

The normal distribution is the most widely used distribution in statistics. Almost all biological data including the agricultural data eg. crop yield, plant height, seed weight, seed size, protein percent in pulse, oil content in an oil seed etc are assumed to be normally distributed. In practice their distribution is rarely verified. In distribution theory, for large values of n, the number of trial (or sample size), almost all the distributions e.g. Binomial, poission, Negative binomial, chi square, student’s t, and Snedenor’s F distributions etc. are very closely approximated by normal distribution. It is a continuous distribution with mean and variance as its two parameters.

9.1 Introduction

In the preceeding chapter, we have studied the estimation part of statistical decision (inference) infered about the parameters of the population on the basis of information available in the sample observations drawn from the parent population under study. In the present chapter we discuss the problems related with the tests of statistical hypothesis and its significance about the population parameters under study. For understanding of the subject matter considered in this chapter, very clearly and easily, one must be familier with the statistical terminologies used in the subject matter. Hence we discuss the various notable terminologies with some suitable examples in a simple way for better understanding.

8.1 Introduction

We have already discussed in details how different sampling schemes can be employed to draw the representative samples suitable to many kinds of population whose sampling units differ in nature. From practical view points, however, it is often more important to be able to make decision about certain unknown characteristics of the population on the basis of informations available in the representative samples which are drawn from the parent populations. Such decisions taken, are known as the “Statistical decisions”. For example, we may wish to decide on the basis of sample data whether (i) One educational system is better than the other or not, (ii) A given six faced die is unbiased (balanced) or not, (iii) A new insecticide is really effective to control the attack of insects or not? etc. Such problems are dealt with in the theory of statistical decisions which uses the principles of sampling theory.

7.1 Introduction

In chapter 5, the cluster sampling has been discussed in which clusters were considered as sampling units and all the elements in the selected clusters were enumerated completely. It has been stated there that the cluster sampling is economical under certain circumstance but the method restricts the spread of sample over the population which results generally in increased sampling variance of the estimator under consideration. It is, therefore, logical to expect that the efficiency of the estimator will be increased by distributing the elements over a large numbers of clusters. Hence instead of observing all the units in the selected clusters, one may like to observe only few elements of the selected clusters. This will involve the sampling work to be done also within the selected clusters. If we do so, the process of selection will involve sampling work to be done at two stage, one for selecting the clusters where sampling units are the clusters and the second for selecting the units (elements) within the selected clusters where sampling units are the elements.

6.1 Introduction

So far we have considered the methods of sampling in which the successive units (elements or clusters) were selected with the help of random numbers. Now, we shall consider a new method of sample selection in which only the first unit of the sample is selected with the help of random number, the rest being selected automatically according to a predetermined pattern. The method is known as systematic sampling. It is operationally more convenient than simple random sampling and which at the same time ensures for each unit, equal probability ofinclusion in the sample. The systematic sampling can also be termed as systematic random sampling because of the first unit of the sample being chosen randomly. The pattern usually followed in selecting a systematic sample is a simple pattern involving regular spacing of units.

5.1 Introduction

In random sampling it is assumed in prior that the population is divisible into a finite number of distinct and identifiable units and these units are known as sampling units. The smallest unit into which the population can be divide, is called an elementary unit (element) of the population. A group of such units is known as cluster. When such cluster is treated as sampling unit, then the sampling procedure adopted is known as cluster sampling. If the entire area containing the population under study is divided into smaller area segments and each element in the population belongs to one and only one such area segment, the sampling is sometimes known as area sampling.

Generally, identification and location of an element requires considerable time. H

4.1 Introduction

In many surveys, information on an auxiliary (supplementary, ancillary or apriori) variable x, which is highly (positively or negatively)correlated with the variable y under study, is readily available and can be used for improving the sampling design. Stratified sampling and probability proportional to size (p.p.s) schemes are two examples of improved sampling designs, in which the use of data on auxiliary variable has been made. However in both the schemes, it implies that the information on auxiliary variable on individual sampling units is available prior to presentation of sampling design. In case, data on auxiliary variable for individual sampling units are not available but only the aggregate value for all units of auxiliary variable is available, the two schemes can not be used. In such a situation, the aggregate data on auxiliary variable can still be used at the time of estimation of parameters under consideration, provided the data on auxiliary variable for the sampled units can be easily obtained at the time of recording the values of the study variable.

3.1 Introduction

Out of all methods of sampling, the most commonly used procedure in surveys is the stratified random sampling. When population units are of heterogeneous nature and if we select a sample using SRSWOR scheme then the sample selected may not be the representative sample and the estimate worked out from that sample will not be the reliable estimate of the population parameter. The estimate of sampling variance of the sample estimator obtained from that sampling scheme may happen to be very high leading to get a less precise estimate of the parameter. In such type of population, to overcome these draw backs, we use another sampling scheme known as stratified random sampling which is defined as follows.

2.1 Simple Random Sampling

The simplest and common most method of sampling is simple random sampling. In this procedure, the sample is drawn unit by unit with equal probability of selection for each unit at each draw. It is sometimes referred to as unrestricted random sampling.

2.1.1 Definition

“A sampling procedure which gives equal chance to all possible samples each of size n which can be drawn from a population of size N, is called simple random sampling.”

1.1 Introduction

The use of sampling in making decisions about an aggregate (or population) is possibly as old as civilization itself. Sampling is first broadly classified into two categories known as subjective and objective. Any type of sampling which depends upon the personal judgement or discretion of the sampler himself, is called subjective sampling. But the sampling method which is governed by a sampling rule or is independent of the sampler’s own judgement is known as objective sampling. The main difficulty with subjective sampling is that the sampler is ignorant of the degree of representativeness of his sample or the accuracy of the final estimates of the population values obtained.

This chapter presents the conclusions of the research study. Based on these conclusions, recommendations relating to the effective implementation and management of Cyber Reference Service (CRS) in agricultural libraries in Maharashtra are made.

6.1 Introduction

The overall aim of this research was to investigate and analyze CRS in the Agricultural College and University libraries of Maharashtra. The study adopted a mixed-methods approach by combining quantitative and qualitative methods to achieve its aims and objectives. 

5.1 Introduction

This chapter presents analysis of data collected through the questionnaire survey from agriculture libraries in Maharashtra (Constituent including University Libraries i.e. Government and Affiliated i.e. Private colleges). The purpose of the survey was to explore the status of CRS (Cyber Reference Service), various aspects pertaining to the CRS, and the ICT infrastructure used for the CRS in the agriculture universities and college libraries. It also aimed to obtain agriculture library heads’ perceptions about the CRS, and explore issues and problems affecting implementation and management of the CRS in the agriculture libraries in the Maharashtra state.

4.1 Introduction

With the birth of the new millennium and the advent of Information and Communication Technology, the outlook of academia towards research has completely metamorphosed into a more productive and vibrant activity. Digitalization of data, the convergence of new and challenging insights in one’s own research fields at the world-wide-web and technology generation are being made faster and more reliable. With the arrival of Laptops and Smartphones simultaneously with the facility Wi-Fi, the demand for information of any kind has become unquantifiable! The academic world is no exception, indeed! Such a pang of hunger for information led to considerable investments in infrastructure development. The virtual, electronic, automated world became inseparable from academic and research institutions. Small wonder that with these, the pressures on the institutional and specialized and dedicated libraries are on the increase.

Research methodology is a systematic approach to solve any research problem and arrive at a conclusion that adds to the already existing fund of knowledge. The research methodology seeks to define the solution by adopting appropriate research techniques. They are generally applied during the initial stage of the study to delineate and outline the rationale for the chosen methods and their functionality and effectiveness towards achieving a decided objective. Research methodologies are an organized approach to accomplish the selected objectives. The research methodology comprises different techniques used during the experiment with surveys, tests, etc.

2.1 Introduction

The journey through the research work and the resultant publication of research articles, review notes and books by innumerable authors in the field of Information Technology, and Communication, Library Sciences, Social Media and Popular Culture has been the basis for this chapter: The Review of Literature. The following is a report of how and what lead to arrive at the objectives, hypotheses and methods of researching this field and the concerns of the researchers and the authors. Reading innumerable articles, research papers, and books helped in writing this literature review strengthened the knowledge about the topic of this study. It has been of immense use to learn essential concepts, research methods, and experimental techniques used in this field. Another benefit of the extensive reading of relevant literature has been the knowledge and understanding of how to present the insights, research findings, and claims discussed and explained scientifically and systematically. Through this reading exercise, the topic of the research and its objectives were finalized. 

1.1 Background

The whole world is characterized by diverse cultures, different approaches to life, divergent outlooks and world views and dissimilar living conditions and environments. The people in it live with cultural, social, economic and educational dissimilarities, differences, inequalities, and goals. Despite the disparities in everything, the whole world is unified by Information, Communication and Technology through the global media culture, technocapitalism, and information revolution. Gone are the days when people confined themselves to their hearths, farms, educational institutions and places of work, managing their lives with basic minimum requirements and needs! 

Introduction

The microbial cells of bacteria, actinomycetes, fungi, algae and protozoa are visible by light microscopy. Beyond the resolution of light microscope are the unique organisms that are sub microscopic and intracellular parasites known as viruses. The term virus is a Latin word means poisonous substance. The viruses are functionally and metabolically different from the cellular organisms as (i) they are infectious agents and contain only one type of nucleic acid encapsulated in a protein coat. Some may possess membranes but do not have any cytoplasm or metabolism of their own. (ii) They are much smaller in size than the smallest bacteria and can pass through bacteriological filters. 

Introduction

The term actinomycetes, coined in the late 1800s (actins, meaning ‘ray’ and myces, meaning ‘fungus’) is a misnomer. Because actinomycetes have a considerably smaller aerial mycelium than fungi and can produce a large number of asexual spores called conidia, which give the colonies a ‘powdery or chalky’ look.

The actinomycetes constitute a specialized group of soil bacteria that occur in soils throughout the world. The actinomycetes are prokaryotes confirming cellular structures and functions of true bacteria and their morphological similarity to the fungi (Eukaryotes). Between bacteria and fungi, actinomycetes are a transitional category. On their hyphae, most actinomycetes produce single, pairs, or chains of asexual spores.The diameter of the hyphae varies from 0.5 to 1.0 µm. 

Unlike root-free soil, the rhizosphere around growing plants is a very dynamic environment that hosts a much higher number of microbes, in particular bacteria. Microbes interact with each other and with plants in symbiotic, associative, neutral or antagonistic ways. A plant colonized or penetrated by a microorganism can either result in asymptomatic or disease or form an association or symbiosis, depending on the cells’ perception of each other and how the environment affects that interaction. Microbes that penetrate and colonize plants have developed elaborate strategies to subvert plant defenses.

Introduction

In soil, microorganisms influence not only the availability of carbon and nitrogen, which are important constituents of plants, but also of sulfur (S), phosphorus and many other trace elements for plant absorption. Sulphur is found widely distributed and considered as tenth most abundant element in the nature. Sulfur is considered as an essential element for survival of plant and animals and found as gypsum (CaSO₄.2H₂O), pyrite (FeS₂) and other combined form and also in elemental form (S⁰) in the nature. The requirement of sulphur by plant is comparatively less than the primary nutrients, hence sulfur is recognised as ‘secondary’ nutrient. 

Introduction

Phosphorus is only second to nitrogen as a mineral nutrient required for plants, animals and microorganisms. It is a major constituent of nucleic acids in all living systems essential in the accumulation and release of energy during cellular metabolism. Phosphorus is found in soil, plants and microorganisms in number of organic and inorganic compounds. It is the second major inorganic nutrient after nitrogen in term of quantitative requirement for both crop plants and microorganisms. This element may be added to soil in the form of chemical fertilizers, or it may be incorporated as leaf litter, plant residues or animal remains. In cultivated soils it is present in abundance (i.e. 1100 kg/ha), but most of which is not available to plants, only 15% of total soil phosphorus is in available form. Thus, phosphorus occupies a critical position both in plant growth and in the biology of soil.

The excessive and imbalanced use of nitrogenous fertilizers in the last few decades to meet the challenges of growing food demand of Worlds population has led to serious consequences of soil and water pollution, leaching of toxic nitrate into ground water and volatilization of nitrogen oxides into the environment. Therefore, judicious management of nitrogen in the environment is very much essential in the development of more productive and sustainable agriculture. Rapid declining of non-renewable energy sources viz. petroleum products, natural gas as well as energy crisis in the recent days, release of several pollutants during production of fertilizer N has also necessitated the development of alternative and/or supplemental technologies to obtain the fertilizer N for crop production.

Introduction

The various reactions of nitrogen (N₂) cycle, transform one form of the element to another. Mineralization liberates nitrogen in inorganic form, immobilization converts its back to an unavailable state, while nitrification changes the nitrogen from a reduced to oxidized state. Certain transformations of nitrogen lead to a net loss of element from the soil through volatilization. For the purpose of crop production, nitrogen volatilization has a deleterious influence because it depletes part of soils reserve of an essential nutrient. The sequence of steps that results in gaseous loss is called denitrification. So denitrification refers to the reduction of nitrates and nitrites to gaseous nitrogen products, principally di-nitrogen and nitrous oxide, coupled to energy production through oxidative phosphorylation.

At the point where ammonium is produced, the reactions involved in organic nitrogen mineralization come to a halt. This most reduced form of inorganic nitrogen is the starting point for the nitrification process, which involves the biological synthesis of nitrate or nitrite from reduced nitrogen molecules. The ability of nitrifying bacteria to create nitrate, which is the primary nitrogen source ingested by higher plants, determines their relevance. Nitrate is formed in a variety of places, including soil, marine environments, manure piles, and sewage treatment, where it is the result of the final stage of turning organic nitrogen inoffensive.

There are two mini-cycles in the soil nitrogen cycle:

1. Soil-based processes such as mineralization, ammonification, nitrification, and immobilisation include the conversion of organic nitrogenous molecules to plant-useable mineral forms, and subsequently back to biomass (microbial, plant and indirectly animal biomass).
2. The process of converting atmospheric nitrogen into forms available to the living components of the ecosystem, i.e. nitrogen fixation and the transformations within the soil, as well as the return of the fixed nitrogen to atmospheric inert forms of nitrogen through denitrification, are all soil and biosphere resident processes.

To a soil microorganism, the rhizosphere is like a lush oasis in the desert. In comparison to near-starvation conditions of the bulk soil, the rhizosphere is the place where nutrients are plentiful and microorganisms flourish. The term rhizosphere was first used by Hiltner in 1904 to describe the zone of soil under the influence of roots. The rhizosphere can extend more than 5 mm from the root and is the area of increased microbial activity due to exudation of low molecular weight compounds from root cells. The rhizoplane is the surface of plant root and any strongly adhering soil particles. The microbial populations that colonize the interior of the root and from intimate associations with the root are considered endophytes.

Introduction

The soil protozoa are unicellular and the simplest forms of animal life. Most of the protozoa are devoid of chlorophyll but transitional genera resemble the algae and possess chloroplasts containing chlorophyll pigments. The active life stage is commonly known as trophozoite phase where they feed and multiply but under adverse climatic conditions, they form resting stage or cyst, which develop a thick coating on the cell surface. Mostly, the protozoa multiply by longitudinal or transverse fission but some species reproduce sexually by fusion of two cells followed by exchange of genetic material between their nuclei and cell division.

Introduction

Soil algae are not as numerous as bacteria, actinomycetes and fungi. Furthermore, these are photosynthetic microorganisms. Recent work has led to a more complete knowledge of the ecology and importance of terrestrial algae. These algae are found abundant in the habitats in which moisture is adequate and light is accessible.

Classification

Soil algae are classified into four major classes:

1. Cyanophyceae (Blue green algae)
2. Chlorophyceae (Green algae)

Fungi are a diverse group of multicellular eukaryotic organisms with wide variation in vegetative and reproductive morphologies and diverse life cycles. They are more abundant, on a mass basis, in soils than any other group of microorganisms; their biomass ranges from 500 to 5000 wet kg ha^-1 (Metting, 1993). Fungi inhabit almost any niche containing organic substrates, so that they are active participants in ecosystems as -

• degraders of organic matter
• agents of disease as well as beneficial symbionts - lichen (algae and fungi), mycorrhizal association

Introduction

The microorganisms exist as single cell or cluster of cells capable of orderly growth, metabolism and reproduction in two identical daughter cells. These includes cellular organisms {protozoa, algae and fungi (Eukaryotes), bacteria and archaea (Prokaryotes)} and viruses (non cellular entities). The study of microorganisms contribute to understanding of :

1. Basic biological sciences - It acts as a tool for providing the basic information for life processes. The discovery of DNA, RNA, proteins and enzyme functions all came from microbial study.
2. Applied biological sciences - Medicine, agriculture, industry, plant and animal pathology, immunology, plant and animal nutrition, biotechnology all have origin from the understanding of the cell function and metabolism of microorganisms.

ntroduction

Millions of living organisms present in soil make it a living and a dynamic system. Under a microscope, it reveals a complex array of soil particles and pore spaces filled with air and water. In these pore spaces that plant roots and millions of organisms develop, which are ranging from sub-microscopic to macroscopic in size. The physical and chemical characteristics of soil determine the nature of the environment in which vast number of soil microorganisms especially soil microorganisms are found. Living organisms in soils include both macro (earthworms, rodents, termites etc.) and microorganisms (bacteria, fungi, actinomycetes, algae and protozoa). These organisms not only help in the development of soils but also carry out a number of transformations, facilitating the availability of nutrients to the plants.

Methods and procedures for the qualitative characterization and quantitative analysis of different constituents of soil organic matter are presented in this Chapter.

20.1. Moisture

The soil sample should be in an air-dry condition after mixing, sieving and grinding. When stored in a moisture-tight container, the free H₂O content should not vary appreciably during storage. While most determinations can be done on air-dry samples, for certain purposes such as obtaining summation values, a moisture content value is required.

Managing soil organic matter is of paramount importance in sustainable crop production. Human interventions always lead to a decrease in soil organic matter content and biological activity. Therefore, even for maintaining moderate levels of organic matter in soils, sustained efforts should be in place. Such sustained efforts should include adopting high-residue crops in rotation and ensuring regular return of residues to soils.

It is all the more difficult to maintain the organic matter content in well aerated soils and soils of hot and arid regions, because in these soils added materials decompose rapidly. However, in comparison, it may not be so difficult to maintain the organic matter levels in fine-textured soils with restricted aeration and in soils of cold temperate regions.

19.1. Measures that Decrease Soil Organic Matter Content

The study of soil organic matter is becoming increasingly important by the day as world agriculture attempts to ensure sustainability of soils, while at the same time striving to enhance food production to feed an ever-burgeoning population. The use of green manures and pasture leys, the return of residues and additions of organic amendments to the soil are often resorted to in an attempt to increase soil organic matter which immensely benefit the physical, chemical and biological make-up of the soil.

The nuclear magnetic resonance spectroscopy (NMR) has led to major advances in the studies on the nature and chemical composition of soil organic matter. The technique has the potential for characterizing organic matter in the intact soil and its size fractions without the need for extraction and fractionation. Furthermore, it can provide information on the compositional changes in crop residues, peat and litter of the forest soils during biodegradation and humification.

Humic substances, the natural organic materials, represent a mixture of relatively small organic components, which form supramolecular structures held together by dispersive forces such as n - n and van der Waals’ interactions. Identification of the best analytical method for complete characterization of humic substances is still being discussed. Humic substances differ in molecular weight, elemental composition, acidity and cation exchange capacity and often are classified into three major fractions according to their solubility as humic acids, fulvic acids and humins.

Humic substances are the most widely-present, natural, complexing ligands occurring in nature. They are found not only in soils, but in natural waters, sewage, compost heaps, marine and lake sediments, peat bogs, carbonaceous shales, coals and other miscellaneous deposits.

The fate of natural organic matter, especially humic substances, has over recent decades attracted increasing attention of scientists representing various disciplines. Humic substances constitute about 25% of the total organic carbon on the earth. These are a group of naturally occurring complex molecular structures into which biomolecules derived from plant and animal residues are transformed progressively through biotic and abiotic pathways, by aggregation and accumulation processes

It is well known that organic matter plays a unique role in the binding of pesticides in soil, and that this phenomenon is usually the most important cause for interaction of pesticides in the soil environment. Fulvic or humic acids are most commonly associated with the binding interactions. Binding can take place with either the original pesticide or a transformation product, the reaction being induced by abiotic or biotic agents (microbial or plant enzymes). The reactions or processes involved are apparently the same as those responsible for the formation of humic substances, i.e., for the humification process.

Organic matter is of immense importance in forming good aggregates in a wide range of soil types, particularly those representing Mollisols, Alfisols, Ultisols and Inceptisols. Organic matter is somewhat less important in the Oxisols where hydrous oxides may play apredominant role. These relationships are depicted in Fig. 13.

Fig. 13.1: Relationship between OM and the formation of soil aggregates

Aggregates do not directly influence plant growth, but they alter the physical and chemical environments in which plant roots grow through their effects on porosity, aeration, moisture retention, etc., when well-irrigated, even fine- textured soils permit adequate exchange of gases with the atmosphere. The polysaccharides of soil have received the greatest attention in producing stable aggregates, but other organic substances bonded to clay through association with Fe or Al may also be involved.

Extensive studies have shown that much of the humified material in soil is finely bound to colloidal clay. However, it is uncertain to what proportion of the clay surface in any given soil is coated by organic substances as this will depend on the organic matter content and the type and amount of clay.

The ways in which organic substances are retained in soil are as follows:

1. As insoluble polymeric complexes of humic and fulvic acids.
2. As polymeric complexes of humic and fulvic acids bound together by di- and trivalent cations such as Ca²+, Fe³+ and Al³+.