eChapters

Introduction

The influence of various socio-economic factors on the willingness of the decision makers to adopt new technologies has been investigated by a number of studies (Roe, 1983; Shakya and Flinn, 1985; Thomas et al., 1990). In most studies of adoption behaviour, the dependent variable is constrained to lie between 0 and 1 and the models used are exponential functions (Kebede et al., 1990). However, the decision to adopt a new technology can be very effectively captured using binary choice models. Binary choice models are appropriate when the choice between two alternatives depends on the characteristics of the problem. Application of a linear probability model to this type of problem, however, suffers from a number of deficiencies (Capps and Kramer, 1985), particularly, the one associated with the estimated probabilities in some cases being greater than one or lesser than zero as a result of neglecting significant interaction effects (Mingche, 1977). These deficiencies could be circumvented through the use of a monotonic transformation (probit or logit specification) which guaranties that predictions lie within the unit interval (Capps and Kramer, 1985). Univariate logit and probit models and their modified forms have been used extensively to study the adoption behaviour of farmers and consumers (Schmidt and Strauss, 1975; Garcia et al., 1983; Shakya and Flinn, 1985; Harper et al., 1990). According to Hanushek and Jackson (1977), the choice between logit and probit models is largely a matter of convenience. However, Maddala (1983) and Shakya and Flinn (1985) have recommended probit models for functional forms with limited dependent variables that are continuous between 0 and 1, and logit models for discrete dependent variables.

Model Specification

Univariate logit model is generally used to study the adoption behaviour of farmers and consumers.

The model is estimated using the maximum likelihood method.

3.1 Definition

 Let f (x) be the function of a random variable ‘x’. If there exists a number ' ' ?

 such that f(x) may be made as close to ' ' ? as we proceed to choose x close to

 a given number ‘k’, then we say that the limit of f(x) as x approaches to k, is ' ' ?

 and we write it symbolically as

 x k

 limf(x)

 ?

 ??

 Example 3.1 : We are giving some examples as below:

 Example 3.1.1 : A function f (x) may become infinite as x tends to a value ‘c’

 and we write that

 x c

 limf(x)

 ?

 ??

 Example 3.1.2 : Consider the function 1 f(x) x ? . Then we say that as x ??,

 f(x) tends to zero and write it as :

 x

 1 lim 0

INTRODUCTION

Lakes are of immense ecological, economic, and cultural significance, representing integral features within our planet's landscapes. Studies show that there are around 117 million lakes worldwide, constituting about 4% of the Earth's non-glaciated land surface (Verpoorter et al., 2014). These lakes play a crucial role in providing essential services to nearby communities and serve as important centres for recreation and overall well-being. However, the increasing demand for natural resources has led to widespread degradation of these lakes (Jenny et al., 2020). Despite efforts to restore lakes, there is a prevailing perception that such programs often encounter high failure rates (Søndergaard et al., 2007; Gulati et al., 2008; Poikane et al., 2024). This concern is particularly pronounced in less economically developed countries, where populations are highly exposed to the consequences of lake degradation, and monitoring, assessment, and restoration programs may be in nascent stages. Additionally, lakes play a pivotal role in climate regulation, influencing local weather patterns and contributing to global processes such as water and carbon cycles. Recognizing and preserving the significance of lakes is imperative for sustaining ecosystems, ensuring water security, and nurturing the well-being of both the natural environment and human societies. 21o 8’ 55’’ 79o 4’46’’E.

Nagpur situated at coordinates 21o 8’ 55’’ N and 79o 4’46’’E, holds the distinction of being the second capital of Maharashtra. Renowned as the "orange city" and the "city of lakes," Nagpur relies significantly on its lakes as a valuable resource base. The current water supply for the city is primarily sourced from, Kanhan River, Gorewada Lake, and Pench Dam. Futala Lake serves an irrigation purpose, benefitting 84 acres of cultivated agricultural land. Ambazari Lake, on the other hand, caters to the water needs of local residents in the MIDC colony, Hingna area, Nagpur, and supports industrial purposes. Other notable water bodies in Nagpur include Sonegaon, Gandhisagar, Naik, Sakkardara, and Lendi. However, these lakes face severe pollution due to anthropogenic activities such as bathing, washing clothes, and improperly disposing of plastic, paper, oil, and worship materials. Moreover, the lakes Gandhisagar and Lendi are now dried.

1. The term limnology is derived from Greek word.

2. Limne means lake.

3. The term limnology was coined by Francois-Alponse forel.

4. August Thienemann and EinarNaumann co-founded the international society of limnology.

5. Welch 1935 described about biological productivity.

6. Epheirology is defined as branch of science deals with terrestrial habitats.

7. Peter Erasmus Muller is credited with laying the foundation stone of limnological study.

8. Anton Fritsch could be considered as the Pioneer in Lacustrine limnology.

9. F.Simony (1850) is regarded sometimes as the founder of limnology for his discovery of thermal stratification.

10. Junge and Forbes who were the first to treat the native waters as microcosm.

Limonium, commonly known as statice, is an important cut and dried flower valued for its attractive inflorescences and excellent keeping quality. This chapter covers its origin, taxonomy, botanical features, varieties and commercial significance. It discusses climate, soil, propagation, planting, irrigation, nutrient management and crop protection. Harvesting at the correct flowering stage, immediate hydration, cold storage and floral preservatives are emphasized for maintaining quality. The chapter also provides commercial grading standards for varieties such as Misty Blue, Saffron and Glow, along with yield information. These practices support both domestic and export production of high-quality Limonium stems.

This chapter introduces Limonium, commonly known as Statice or Sea Lavender, as a hardy filler flower valued for its attractive colours and exceptional longevity after harvest. It examines origin, distribution, genetics, varieties and cultivation practices. Particular attention is given to its halophytic characteristics and potential for cultivation in saline and degraded soils. The chapter also covers greenhouse production, environmental management, propagation, nutrition, pest and disease management, harvesting and postharvest handling for cut and dried flowers.

ABSTRACT

Lineaments are the surficial expression of sub-surface phenomena which are well discerned from low-orbital data, satellite data with its synoptic view and repetitive coverage and help to bring out more information on structural features hither to unknown by conventional geological surveys. Their study paved way to many new concepts on tectonism, mineralization, structural deformity etc. of an area both in regional and local perspective. In the present study, a small area was selected after careful observation, for their importance in mineral deposits. The lineaments are mostly structurally controlled and analysed for their relationship with the base metal deposits of the area. Moreover satellite parameters like spatial resolution, scale etc., and are observed for their changes in their orientation and relationship with the deposit of the study area, Jambughoda of Panchmahal District, Gujarat Satellite data of Landsat MSS and TM are used for the analysis. The results proved that the satellite parameters are important criteria and play a vital role in lineament analysis for mineral investigation.

Abstract

The frequently recurring earth tremors / Seismicities in parts of Tamil Nadu & Kerala and the earthquakes of higher magnitude in Central India in the recent years have gradually dissolved the concept of shield area stability for the South India and the concept of mid-plate seismicity has emerged in the minds of geoscientists, Hence, the geoscientists have embarked into a number of studies to bringout seismotectonic maps for South India. In this context, a detailed studies have been carried out by the authors in parts of westernghats (Kodaikkanal and Varushanad hills) in Tamil Nadu. In the said study lineaments were interpreted using high resolution IRS satellite data and from the lineament pattern, isofracture, lineament density and lineament interaction intensity the strain maxima axes were drawn. Such strain maximas were observed to fall in N- S, E – W and NE –SW directions. These strain maximas were found to coincide with various drainage anomalies, like radial, compressed meandering and eyed drainages indicating that these lineaments are prone for seismicity/ earth tremors.

8.1 Write True/False

Q.1    Any linear function of sample observations which is used to estimate the population parameter or parametric function,is called the “Linear estimator.”

Introduction

Aquaculture, the farming of aquatic organisms, has been the agro industrial activity with the highest growth rate worldwide in the last four decades. From 1970 to 2008 the production of aquaculture organisms grew at a rate of 8.3% per year, compared to less than 2% of fisheries, and 2.9% of livestock (Luchini et al. 2008). In India, aquaculture feed market was valued at USD 1.20 billion in 2017 and is expected to register a CAGR of 10.4% during the forecast period (2018-2023). Indian feed mills have the capacity to produce 2.88 million metric ton. Andhra Pradesh is the largest feed consuming state in India (Anon. 2017). The growth in production has been faster for fed species than for non fed species indicating that adoption of feed based aquaculture as the major driver of aquaculture production growth (FAO, 2016). However, the demand for aquaculture feed in the country will touch to 7 million tons by 2017-18. Hence, there is wide gap in demand and supply of feed. The feed cost is a major production cost in aquaculture in India yet feeding of fish is still mostly guesswork, each fish producer following different guidelines (feed charts) or adopting different practices. Feeding too much leads to feed wastage, a pure economic loss, and greater waste output. Feeding too little results in less growth and this also represents an economic loss. In intensive and semi intensive aquaculture, natural foods are not sufficient to meet our overall nutritional requirement of the fishes for their survival and faster growth. Hence, the good quality supplementary diet which contains all required nutrients in recommended quantity were recommended by many aquaculture scientists for the better production. It is further opined by the researchers that it is economical to prepare supplementary feeds by using locally available ingredients. The dietary carbohydrates are usually quantitatively the largest constituent of diet (40-55% W/W) particularly in feeds for carps, the most dominant group of fishes with respect to not only world aquaculture but also Indian Aquaculture. The rice bran, corn, and wheat are the conventional carbohydrate sources in India. In North East Region of the country none of these items are produced locally in sufficient quantity (Kumar et al. 2018). Due to less of local production of these ingredients, cost of feed which generally constitute about 40-50% of total cost of production, increase tremendously in NEH region due high transportation cost. Hence, for development of ideal feed for carp based aquaculture in North East Region, optimization of feed cost is very important to make the fish production cost effective and more profitable.

Introduction

Linear programming (LP) is one of the most widely applied operation research techniques and owes its popularity to George Dantzig’s simplex method (Danzig 1963). It is an important field of optimization for several reasons. Many practical problems in operation research can be expressed as linear programming problems. Linear programming is heavily used in microeconomics and business management, either to maximize the income or minimize the costs of a production scheme. Some examples are food blending, inventory management, portfolio and finance management, resource allocation for human and machine resources, planning advertisement campaign etc.

In our economy, there are limited resources at our disposal. Our problem is to make such use of these resources to yield maximum quantity of production or to minimize the cost of production. Therefore, there arises the question of Linear Programming that is defined as a mathematical method of calculating the least cost combination of the factors of production, when there are many possible alternative combinations available to an enterprise.

24.1 Introduction

Linear programming is a budgeting technique which is more refined and systematic than conventional budgeting in determining the optimum combination of enterprises or inputs so as to maximise the income or combination of enterprises or the income or minimise costs within the limits of available resources. It may be defined as “The analysis of the problem in which a linear function of a number of variables is to be maximised or minimised when these variables are subject to a number of restraints in the form of linear inequalities”, In linear programming models, the objective of the typical farm i.e maximisation of net profit or cost minimisation is achieved through an optimal plan generated from its solution.

2.1 Introduction

Consider two linear equations in two variables i.e., x and y, such that equations are

Introduction

Regression analysis is one of the most widely used techniques for studying relationships involving multiple variables for analysing data by expressing a relationship between a variable of interest (the response) and a set of related predictor variables. The regression models include linear and non linear approaches assuming appropriate functional forms. A good account on regression analysis and related topics can be found in Draper and Smith (1998), Montgomery et al. (2001), Chatterjee and Hadi (2006) etc. In this write-up, regression model fitting, some of the detection techniques which are useful in detecting the problem of multicollinearity between the so-called ‘independent variables’ and also outlier detection in data are discussed. Linear regression with qualitative regressor variables is also discussed. Moreover, variable selection procedures, goodness of fit measures for model adequacy and validation are also discussed. In addition, non-linear regressions viz., logistic (both binary and multinomial) when the response variable is qualitative are also covered.

Multiple Linear Regression Modeling

Let the response variable (variable to be forecasted) be denoted by Y and the set of predictor variables, by X1 , X2 , …, Xp , where p denotes the number of predictor variables. The true relationship between Y and (X1 , X2 ,…,Xp ) can be approximated by a multiple linear regression model given by

9.1 Introduction

Water is a precious commodity. Necessity of conservation of water supply is increasingly felt all over the world as the demand of it continues to increase when the sources of supply are increasingly scarce. The conveyance and proportional distribution of water is one of the most important part of any irrigation project. Water available in the stream, reservoirs or wells is carried to the field of use through canals. The economy of any irrigation project largely depends on how efficiently the water is being transported and distributed in respect to water losses and cost.

Water losses in irrigation channels are mainly due to evaporation from the water surface and seepage from the bed and side of the channels. Seepage losses some time become the single important factor which causes unavailability of water particularly in downstream of the channel in canal irrigation system and excess pumping of water in tube wells and river lift irrigation system.

8. 1 Introduction

Water is a precious commodity. Necessity of conservation of water supply is increasingly felt all over the world as the demand of it continues to increase when the sources of supply are increasingly scarce. The conveyance and proportional distribution of water is one of the most important part of any irrigation project. Water available in the stream, reservoirs or wells is carried to the field of use through canals. The economy of any irrigation project largely depends on how efficiently the water is being transported and distributed in respect to water losses and cost.

Water losses in irrigation channels are mainly due to evaporation from the water surface and seepage from the bed and side of the channels. Seepage losses some time become the single important factor which causes unavailability of water particularly in downstream of the channel in canal irrigation system and excess pumping of water in tube wells and river lift irrigation system.

Lining irrigation channels means to cover the channel earthen surface by any substance, which can considerably reduce the seepage loss. The concrete, brick, stone, natural clay of low permeability, bituminous material, plastic compounds, etc. , are used for channel lining.

Linkage

When two or more genes are located on the same chromosome, they are said to be linked. Genes on different chromosomes are distributed to the gametes following the law of independent assortment. However, genes located on the same chromosome, tend to stay together during gamete formation. For example, results of test-crossing dihybrid individuals will produce different results depending upon whether the genes are linked or are located on the same chromosome. This is illustrated as follows:

Operational agricultural meteorology is a critical field that applies meteorological knowledge and tools to support agricultural decision-making, enhance crop yields, and mitigate the impacts of weather and climate variability on agricultural systems. Effective collaboration and coordination among various stakeholders are essential for the successful application of agricultural meteorology (Fig. 12.1).
This chapter explores the linkages between key players in operational agricultural meteorology, including national meteorological services, research organizations, development departments dealing with agriculture and allied sectors, agricultural universities, and non-governmental organizations (NGOs).
Importance of Collaboration in Agricultural Meteorology
The integration of meteorological information into agricultural practices requires a multidisciplinary approach. By working together, stakeholders can ensure that weather and climate data are accurately interpreted and applied to support farmers, policymakers, and other decision-makers. Collaboration can lead to improved agricultural productivity, reduced risks associated with weather extremes, and more sustainable agricultural practices.
12.1. Key Stakeholders and Their Roles
• National Meteorological Services (NMS): Provide weather and climate data, forecasts, and warnings that are critical for agricultural planning and management.    

There streams of thought are presented in this paper:

One, that rural women are not only the warriors, creative actors in the agricultural scene, or rural areas, but also the urban poor, the  migrants , the homeless, the source of labour supply into the expanding urban spaces, including trafficking.
 
I would then argue [two] that this spread of the location of “rural women” is the phenomena that we should now look at, the connectivity between the old divisions of rural and urban, as it indicates the pressures that the current development paradigms operating in our countries, is putting on the RURAL … and therefore that we should look at the macroeconomic policies that are in place, globally and regionally and  nationally.