eChapters

NAME OF THE UNIT: 

    1.    Location of Seed Storage 
    2.    Date of Inspections/visit

PPEs are the personal protection equipments which protect the personnel against some hazards in the shop floor. PPEs form important aspects of Environment (safe disposal of treated chemicals, dusts coming out of processing), Safety (to seed storages) and Health (personnel) (ESH) strategy of any shop floor.

The “falling number” is a measurement used by plant breeders and grain quality specialists to determine the soundness of wheat, which is primarily influenced by the degree of sprouting in a grain sample. Hagberg Falling Number is the full name of this international standard of sprout measurement.  The term “falling numbers” refers to the amount of time it takes for the Hagberg steel ball in this sprout analysis tool to fall through a flour/water slurry that has been heated to release the starch from water. 

There are stages in seed supply chain where seed losses happen at harvesting and post harvest stage. These losses may occur due to higher moisture at harvest, due to harvesting methods, processing methods, at seed storage, during transport etc. Estimation of these losses is an important function of seed inventory management. Estimating seed losses help to improve processing and seed distribution efficiency. These simple statistical tools will help to measure quantitative post harvest seed losses. Seed testing can be used for measuring qualitative post harvest seed losses.

To assess the quality of the seed is necessary before it is shipped for packaging and made available to the farming community. To assess certain routine seed quality parameters which are statutory for labelling seed sampling and testing are very critical. Seed testing is not only a mandatory trade requirement but also decides the market potential of a particular product in the market and safeguard the brand image of the company. Seed sampling for labelling and other trade requirements is done at processing plant. The reliability of seed testing is depends on the accuracy with which the sample represents the lot and precision of the laboratory test. In many cases variations in the test results are due to variation in the sampling. Hence, seed sampling is one of the important activities of the shop floor or processing unit.

The management of storage in open and closed storage is critical before start of any seed processing activities. When seed lots reach the shop floor, they need to be stored properly. Unlike commercial produce seed lots cannot be stored in bulk. These need to be stacked lot wise and there is problem of “stackburn”-resulting from successive layers in building a bag stack. In crops like soyabean-”stackburn” can have detrimental effect on seed quality. The quality parameters like germination and viability can get lost instantly if not properly stacked on the pallets.

Storage of paddy seed lots after harvesting is one of the crucial aspects of processing shopfloor. Paddy due to increased mechanised harvesting may loose vital seed quality parameters. Since seed growers cannot buy a combined harvester themselves-they resort to hiring of this machine. Sometimes in the hurry of completing harvesting they harvest the crop at un-safe moisture content. This un-safe moisture content may go up to 50-60% also. Harvesting of seed crop with high moisture has adverse effect on storability. Ensuring safe seed moisture is very critical especially during rabi-summer seed production cycle. Most of the growers depend on sun drying for reducing the seed moisture. But paddy should not be dried >3% in one cycle. Follow tempering and convection cooling after every cycle and repeat the cycle till seed attains safe moisture content (14%).

Term ‘Scientific research’ is too broad to be explained by using one or two definitions. One should go through the different expert views about scientific research in order to internalize the concept of scientific research. In essence a scientific research is characterized by its rigor, testability, replicability, precision, confidence, objectivity, generalizability and parsimony.

Research: Uma Sekaran (2006) defined research as an organized, systematic, data-based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the purpose of finding answers or solutions to it.

Rationally selected and contextually defined research problem is the foundation to the successful research project. If the selected research problem is not in line with the interests of the researcher, available time, resources, infrastructure & knowledge base then achieving success in research process remains highly uncertain. Consequently, in addition to hindering of professional growth of the researcher, it also results in wastage of time, money and human effort. Most importantly, it may sometimes add up incorrect knowledge into scientific base, hindering the process of scientific progress. Hence the identification of the research problem and designing the problem statement is the most important step in the research process.

Whenever we design a scientific study it is designed in a specific contextual background to solve one or another scientific problem. Subsequently it also involves a set of variables/phenomenon operating in a particular context, set of relationships among these variables and the conditions under which they operate. Certainly, to address an unsolved scientific problem we need to carry out research by employing well defined research methodology and have an idea about possible outcomes.  For example when a extension scientist is asked to study the impact of a novel programme, he needs to understand the background and the context under which the programme was introduced, what are the developmental inputs given under the programme, to whom, when (variables), with what purpose (context), what kind of impact he/she is supposed to investigate (presumed relationship), what methods he is going to use to investigate the impact of developmental programme (research methodology) and what are the possible outcomes of the investigation and how programme interventions are related to the outcome (relationship).

Most of the scientific research studies –except few descriptive survey research studies–will have one or more hypotheses to be tested as a part of study. Hypothesis is a prediction intended to be tested in a research study (Aron, Coups, and Aron, 2012). Hypothesis is the guide which directs the purpose and process of research by indicating the dependent and independent variables to be observed, extraneous variables to be controlled and the active variables to be manipulated.

Variable is any character or attribute of the given object/set of objects which varies from object to object or from one point of time to another point of time. For example height of individuals is a variable; it varies from person to person.  Kerlinger (1973) defined variable as a symbol to which numerals or values are assigned.

Importance of variables: In previous sections, we discussed about formulating the research problem and development of research hypothesis. Variables are the building blocks of any scientific research problem and research hypothesis. When we consider a hypothesis to be relational hypothesis, we mean that it make claims about relationship between two or more variables. When we test hypothesis, as such we do not test the hypothesis, rather we test the significance of hypothesised relationship between two or more variables. Since variables play central role in observation, measurement and testing of relationship, it is of prime importance to understand the meaning of variables, different types and the way we define these variables.

There can be no scientific studies which do not involve the process of measuring. Measurement process is the integral part of research activity. Measurement can be defined as the assignment of numerals to the objects or events according to set of rules.  The emphasis in this definition is on phrase ‘set of rules’. If the rules can be set up based on some rational or empirical basis, then theoretically anything can be measured. It is very much easy to understand the concept of measurement in case of physical sciences (Example weight, length, mass etc.) but it is harder understand the concept of measurement in case of psychological objects/phenomenon (intelligence, aggressiveness, attitude etc.) without understanding the predetermined rules of measurement. A rule is a guide, command that tells you what to do/how to do.

Reliability deals with the dependency, consistency, stability, predictability and accuracy of the research data. In our day to day life, when we say that a person is reliable then it means his behaviour is predictable, his behaviour (the way he behaved in past, he is behaving now and the way he will behave in future) will be consistent and he is dependable. On other hand an unreliable person’s behaviour is unpredictably variable and he cannot be depended upon.

In previous section, we discussed about the concept of the reliability and its importance. In this section, we will discuss about different types of reliability and procedures for estimation of the reliability. We already discussed that one cannot measure the reliability but of course it can be estimated.

Reliability aims at assessing the accuracy and consistency of whatsoever the measuring instrument measures irrespective of whether we are measuring what we are supposed to measure or not. Whereas the validity emphasises on the question “are we measuring what we were supposed to measure?”

To clarify the difference between reliability and validity, let us take an example of instruments that measure colour intensity. Assume, an expert develops two separate instruments X and Y to measure the intensity of green and red colour, respectively. But, the commercial manufacturer wrongly tags the red colour measuring instrument as X. When someone uses this wrongly tagged instrument for measuring the green colour intensity, whatever intensity values the instrument yields are reliable but not valid. It is so because the instrument is measuring red colour intensity accurately (hence data is reliable) but it is not measuring what it was supposed to measure –the green colour intensity (hence data is not valid).

In previous sections we discussed about the validity and estimation of validity. Now the question is whether the reliable and valid measuring instrument is sufficient condition to ensure the reliable and valid results in actual experiments? What if the measuring instruments are perfectly reliable and valid but the experiment is conducted in less controlled conditions? Can we expect accurate predictions under such conditions? The answer is No.

In previous section we discussed about the ability of the research design to precisely detect the relationship between cause and effect under controlled conditions. But in social research it becomes difficult to conduct the research under highly controlled conditions and moreover many of the researches in social science aim at prediction rather than understanding.

As we discussed earlier, aim of science is development theory, understanding, prediction and explanation of natural phenomenon. In many of the cases we are more interested in predicting the occurrence of social phenomenon rather than explaining it or understanding its relationship with other phenomenon. Under such conditions internal validity is of less use because a relationship that is established in artificially controlled conditions (with high internal validity) may fail to express itself in natural setup due to effect of extraneous variables. When the relationship between cause and effect is weak enough to be expressed in natural setup then it is of less use in predicting the phenomenon. Under such conditions we require experiments that are conducted in nearly natural setup, so that we can generalize the findings of the research to the wider domain.

Sampling is the process of drawing a part of population supposed to be the representative of that population.

Types: Sampling process can be either random or non-random. Most frequently we use random sampling method in our studies. But sometimes the purpose of the study demands for non-random sampling in one or other phase of sampling (Example: When we want to study the impact of life skills development programme on the disabled female beneficiaries)

Not all the scientific problems demand the detection and establishment of causal relationship among natural phenomenon. Many of the social research problems seek to study the status quo rather than the relation between two or more natural phenomenon. Few of the impact assessment studies (which are kind of causal relationship studies to an extent) also demand for knowing the status quo before and after implementation of interventions. Many other studies demand for benchmark assessment to make policy decisions. Under such circumstances, survey research is a useful research method.

Every practical problem need not have a documented solution. Most of the times to solve a practical problem, we need to apply our experience, creativity and experiment with the novel solutions in order to address the problem which have no readymade solution. Under such circumstance, we will apply our knowledge and experience in selecting, planning, experimenting and evaluation of the possible solutions. This type of problem solving approach involves action research.  Action research is a type of social research that aims at gaining an insight into the problem by learning from experiences gained in an effort to solve the problem. Here, action itself is a source of knowledge.

Quantitative studies are preferred over qualitative studies for their relatively higher objectivity. But many of the times, complexities involved in the occurrence of phenomenon, its causes, and consequences demand for in-depth analysis of a single unit of study. Whenever we want to study a single unit in depth both qualitatively and quantitatively then case study is the best method of research. Case study is a best tool to use, when we are interested in exploring a new phenomenon on which there is very less scientific information available and also when we are interested in formulating new hypothesis for causal study.

Scientific research is always based on observable and measurable facts. This applies to physical as well as humanitarian fields of science. In order to investigate social or psychological phenomenon, most of the times we need quantified information related to the phenomenon under investigation. In order to secure this data, we need one or more method/ means of collecting observable/measurable data.

Interview method: It is a two way interactive (either face-to-face or through multimedia technology), method of data collection wherein the researchers elicit required response from the respondent by presenting stimulus in the form of series of questions pertinent to it.

Interview schedule: It is a data collection tool consisting of properly worded, sequentially arranged questions, tests relevant to objective of research.

t is the method of gathering the data by seeing, hearing and then recording the observation on the phenomenon/behavior of the respondent rather than the subjects’ self-reporting of the response.

Communication material is the joint product of knowledge, skill and attitude base of the communicator. Further, most of the communication done on behalf of certain organization also reflects the objective, work culture, strategy and contribution of the organization. Hence analysis of communication material is of greater relevance in exploring the trends, vision, behavior and future course of action. In this chapter, we are going to discuss the content analysis; it’s meaning and practical application.

Existing positive as well as negative interactions among the members of the society determine the communication structure, leadership and technology diffusion pattern within the given social system. Sociometry is a method for gathering and analyzing data on communication choice and interaction pattern of individuals, within a group in order to identify communication structure and pattern.

It is not possible that every respondent is willing to and capable of giving honest and true response. Sometimes, it is practically not possible to make the respondent reveal about personal information, but true research requires true response. When direct stimuli fail to elicit true response, then we must use indirect and extractive methods to elicit the required response indirectly from the respondents, which further can be interpreted in the relevant format. This indirect and ambiguous stimulus based methods are known as projective methods. Projective methods are technique of data collection on the person, value, attitude, emotions and motives etc. by using a highly unstructured and ambiguous stimulus (Wilkening, Wilkening & Wilkening, 1973).

Data collected by using different data collection methods remain meaningless until it is screened, cleaned, coded, analyzed and summarized. Data cleaning and screening mostly come under the purview of statistical logics and techniques hence not dealt with in this practical guide. Since these aspects will be dealt by the statistical experts from the allied statistical wings. Presuming that students are aware about data cleaning and screening, we will proceed further with data coding.

Hypothesis testing is the statistical procedure used in order to accept or reject statistical null hypotheses. One of the major purpose for which we formulate the hypothesis is, to test the statistical significance of the relation, difference or variation etc. among given sample/s at a given point of time or at different time points.

Based on the nature and purpose of the content, there are numerous classes of scientific communications. Theses represent the class of research communications that are of great importance to the students. Thesis is part and parcel of the master’s and doctoral programme in almost every academic institution. In some institutions, even the bachelor students are expected to submit their brief report on project.

The concept of Self Help Group (SHG) has its roots in rural areas and it has been mooted along the rural poor to improve their living conditions. Though it is applicable to both men and women in our country, but it has been more successful among women and they can start economic activities for improving their livelihoods through SHG movement. Self Help Group (SHG) is a small voluntary association. It is informal and homogenous group of not more than twenty members. SHGs consist of maximum 20 members because any group having more than 20 members has to be registered under Indian legal system. That is why, it is recommended to be informal to keep them away from bureaucracy, corruption, unnecessary administrative expenditure and profit motive. In fact, it is a home grown model for poverty reduction which simultaneously works to empower and shape the lives of its members in a better way. Groups are expected to be homogenous so that the members do not have conflicting interest and all the members can participate freely without any fear. SHG movement has triggered off a silent revolution in the rural credit delivery system in India. SHGs have proved as an effective medium for delivering credit to rural poor for their socio-economic empowerment. In the development paradigm, SHG and micro-finance has evolved as a need-based programme for empowerment and alleviation of poverty to the so far neglected target groups (women, poor, deprived, etc.).

Self Help Group (SHG) and micro finance may be said a recent movement and the literature on SHG and micro finance is relatively one of the youngest. Review of past works in the field of SHG and related issues since 1995 has been considered, which helps to identify the research gap. Therefore, studies related to SHGs were reviewed and presented covering various aspects comprehensively.
 
2.1 Profile of the SHG Members
 
Hemalatha (1995) conducted study in Haryana and found that majority of women taking up pottery activity were illiterate, Sirki making women’s education ranged from illiteracy to primary level and bakery unit were illiterate.

In India, majority of the people live in rural areas and are engaged in agriculture, earning a subsistence wage. Development which has been focused on them seems to have just passed by them. In India out of the 147.90 million rural households around 60 per cent are cultivation households. Eighty-eight per cent are headed by small/marginal farmers with holding less than 2 hectares (Throat, 2005). Therefore to finance this category of the poor, there is an obligation to create opportunities where credit can be used in a meaningful way.

Present study was conducted in an Eastern Indian State ‘Chhattisgarh’, which was purposively selected keeping in view the importance of SHG movement in empowering rural poor in Eastern Indian States, home of the largest proportion of population below poverty line. To have in-depth micro level analyses, one district of Chhattisgarh was randomly selected, which happened to be the Kanker district.

A research methodology is the logical and systematic planning and directing of a research process to accomplish research goals. The components of research methods used in the present study are as follows:
5.1 Hypotheses
5.2 Sampling plan 
5.3 Variables and their measurements
5.4 The interview schedule
5.5 Statistical analyses

Findings of the present investigation on effectiveness of self-help groups (SHGs) in improving livelihood and empowerment of rural poor in Kanker district of Chhattisgarh are presented under the following headings:
    6.1 Features of SHGs formed under different programmes 
    6.2 Socio-personal, socio-economic and communicational profile of the members of SHGs
    6.3 Dynamics and effectiveness of SHGs  
    6.4 Impact of SHGs on livelihood of the members 
    6.5 Influence of SHGs on empowerment of rural poor
    6.6 Overall impact of SHG approach 
    6.7 Constraints faced by SHGs 

The main purpose of this chapter is to summarize the results to draw the conclusions on the basis of the foregoing analyses and to indicate some of the significant implications of the present study.
 
In previous Chapter, the study described a detail account of 12 SHGs, three SHGs from each of four schemes viz. NABARD’s SHG-Bank Linkage Programme (SBLP), National Rural Livelihood Mission (NRLM), Integrated Watershed Management Programme (IWMP), Agricultural Technology Management Agency (ATMA) in Chhattisgarh state. However, looking back to this study with the salient findings and implications would be the precursor for looking ahead.

In the study of rural development, there are three frameworks of analysis, viz. neo-classical, Marxian and systems approaches. While the latter two are holistic approaches, the neo-classical framework provides the underpinning of modern microfinance, with its focus on the decision making of individual economic agents towards (short-term) profit or utility maximisation. Neo-classical discussion and policy prescriptions in microfinance are directed towards creating the conditions for the operation of free rural financial markets. Institutional economists have further used the imperfect information paradigm to focus on transaction costs of credit delivery that serve to explain and justify high interest rates charged by village moneylenders (Tankha, 2002).

Introduction

Geomorphology is the study of earth’s landscapes over time and space, and involves an analysis of both its surface features as well as the processes responsible. Historically, the subject evolved through monumental studies on supposed evolution of the landscape, based on intuitive knowledge of the researchers on landscape response to endogenic and exogenic forces, as well as visions of possible changes over a time scale of centuries to millennia or more. Too much emphasis on the study of possible landscape evolution in an era of little technological back-up facilities meant that the discourses were largely philosophical in nature, with fewer measurable parameters to substantiate the concepts or the reasoning, although reflecting deep understanding of the researchers involved. As older concepts began to be confronted with newer ones, and applied aspects started receiving greater attention, a need was felt for quantification of the features and processes. This was more so for an understanding of the fluvial landscape, although aeolian and other process-based studies also gradually incorporated quantitative analysis as a means of understanding the landscape. Despite the awareness, lack of adequate equipment hindered the progress of quantification in the pre-World War II period.

Abstract

In the emerging era of wide spread use of IT tools, up-to-date information on water resources is the key to support economic development, conserve the nature as well as to improve the quality of life. Due to rapid rise in population  and food demand, growing economy and improving living standards; the pressure on our water resources is increasing and it will become scarce in the coming decades if not managed at this stage in a integrated way. Water resources management requires a multi-disciplinary approach that combines a collection of technical tools and expertise along with stakeholders of diverse interests and priorities in planning and management and an operational water resources information system at national level is the first requirement.

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).

Abstract
 
Mention about the Great Himalayan river of Vedic Period (10000-8000 BC) is found in ancient Indian literature. The river called as ‘Saraswati’ in India and ‘Hakra’ in Pakistan, was flowing with full majesty through Thar Desert around 6000 BC and dried up during 3000 BC due to climatic and tectonic changes in Himalayan region. The relict of this lost river is still found as palaeochannels in this region. Large numbers of archaeological sites of Harappan civilization occur along the dry river bed. 

Abstract

Land degradation is defined as human induced or natural process that negatively affects the lands to function effectively. It is temporary or permanent lowering of the productive capacity of land (UNEP, 1992). Several definitions of land degradation have been suggested by different authorities to express the degree of deterioration in potential of land in terms of physical, chemical and biological aspects. It has both environmental and economic consequences. The information on land degradation is needed for a variety of purposes such as to improve productivity levels in degraded lands through reclamation programmes, rational land use planning and also to bring more areas under cultivation.

Abstract
 
India shares 16% of the world population, while its land is only 2% of the total geographical area of the world. Naturally, the pressure on the land is often beyond its carrying capacity. Therefore, the productive lands, especially the farmlands in the India are in the constant process of various degrees of degradation and are fast turning into wastelands. At present, approximately 68.35 million hectare area of the land is lying as wastelands in India. Out of these lands, approximately 50% lands are such non-forest lands, which can be made fertile again if treated properly. It was unprotected non-forestlands, which suffered the maximum degradation mainly due to the tremendous biotic pressure on it. In the last 50 years it is India's lush green village forests and woodlots have been deforested to the maximum. 

Abstract

This paper portray the generation of spectral signature library for semi arid area crops using hyperspectral data and the scope of the application of the generated digital library for the classification of the satellite imagery of similar regions.

Hypersprctral data have narrow bands which help to provide additional information related to observed features. In case of this agriculture study crop related information are gathered using hyperspectral sensors The variation in the observations are due to physic-chemical characteristics such as biochemical composition, physical structure, water content and plant ecophysical status. Using the hand held spectroradiometer the reflectance is captured for the bajra, moong, moth crops from the Rajasthan, India’s semi arid region. The observations have been taken for different dates considering the phenology of the crops. Hyperion data from EO-1 satellite have been used to implement the classification operation based on the spectral signature library of bajra. It is observed that the image with the crops can be classified according to the phenology difference.

Abstract

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on regional scales. Large historical datasets are required in drought studies to understand the complex inter-relationship between the climatic and meteorological data. Extraction of valuable information from such large data archives demands an automated and efficient way. Data mining is answer to above problem as it has the potential to search for hidden pattern and identify the relationship between the datasets. For the current research, rainfall data from 1951-2006 was used to compute Standardized Precipitation Index (SPI) and NOAA-AVHRR NDVI images for the period 1981-2006 was used to calculate Vegetation Condition Index (VCI). SPI and VCI were used as input parameters in the Apriori Algorithm for generating the association rules. Interesting rules were developed identifying the relationship between user-specified target episodes and meteorological events. Therefore, spatial and temporal pattern of drought was achieved with 88% confidence.

Abstract

Quality of water is a matter of serious concern for arid and semi arid region like Rajasthan. The study area chosen is extreme western arid part of Rajasthan state where water quality and quantity both are very important issues. Due to absence of any perennial river, scanty rainfall and scarcity of surface water resources, ground water is very deep and saline. The groundwater potability assessment was based on 1119 wells water quality data. The purposes of this investigation were (1) to provide an overview of present groundwater quality and (2) to determine spatial distribution of groundwater quality parameters which affect water potability and (3) to map groundwater potability in the study area by using GIS and Geostatistics techniques. Arc GIS 9.3 and Arc GIS Geostatistical Analyst were used for generation of various thematic maps and Arc GIS Spatial Analyst to produce the final groundwater potability map.

Introduction

Agriculture contributes to 29.93% of India's GDP with a share of 27% of the world's agriculture production. India is ranked 6th in the world. In Rajasthan significant portion of economy of is agrarian. The agriculture sector of the state accounts for 22.5% share of agriculture in GSDP which is estimated to 18.39% in 2009-10. About 80% of the total population resides in rural area and largely dependent on agriculture as the source of their livelihood. The hot arid region of western Rajasthan occupies 61% of the total geographical area and 39.51% population of the state. During the last four decades the burgeoning human and livestock population and increased societal demands for land based products led to  significant expansion and intensification of agriculture by putting even marginal lands under plough, excessive exploitation  of ground water and importing of surface water for irrigation.

Abstract

Population of Rajasthan as per Census 2011 is 68,621,012 persons which is much higher than census 2001 that records 56,507,188. It includes 12 districts of Rajasthan. Western Rajasthan covers about 61% area of the total geographical area and 39.51% population of the total population of the Rajasthan. The growth rate of population is much higher than that of the Rajasthan State. In the census 1981 Western Rajasthan had registered a phenomenal increase of 35.48 per cent over 1971 population. Regional pattern of growth rate in western Rajasthan shows that extreme hot arid region district (Barmer) is on top having 32.55 per cent growth rate whereas Indira Gandhi Canal Region (Ganganagar) district has least population growth of 10.06 per cent only. In 1981, the average urban growth rate had been 54.52 per cent. In 2001, the rural density of population was 83 persons per sq. km.