eChapters

Introduction to Corporate Level Strategy

Corporate level strategy pertains to the choices and initiatives undertaken by a company's senior management to define the overarching direction and breadth of the organization. It entails making decisions regarding which industries or markets to engage in and how to distribute resources among various business units.

Scope of Decision-Making: Corporate level strategy concentrates on the entirety of the organization rather than individual business units or functional areas. It involves strategic decisions related to diversification, vertical integration, mergers and acquisitions, strategic alliances & resource allocation.

Purpose: The primary goal of corporate level strategy is to create value for shareholders by achieving a sustainable competitive advantage and maximizing the overall performance and profitability of the organization. It involves assessing the external business environment, identifying opportunities and threats, and leveraging internal capabilities to gain a competitive edge.

Diversification: One key aspect of corporate level strategy is deciding whether to pursue a strategy of diversification. Diversification can be either related (expanding into industries or markets related to the company's current business) or unrelated (entering completely new industries). The decision to diversify involves assessing the potential benefits, risks, and synergies associated with entering new markets.

Introduction to strategy implementation involves translating strategic plans into actions and achieving desired outcomes. While many organizations focus on the technical aspects of implementation, it is crucial to consider the behavioral dimensions as well. Behavioral dimensions encompass the attitudes, behaviors, and actions of individuals within an organization that impact the success of strategy implementation. Here are some key points to consider regarding the behavioral dimensions of strategy implementation:

Organizational culture: The culture of an organization plays a significant role in strategy implementation. It comprises shared values, norms, beliefs, and practices that influence employee behavior. An accommodating and flexible culture fosters employee engagement, commitment, and alignment with strategic goals.

Leadership and Communication: Effective leadership is essential for successful strategy implementation. Leaders must articulate the strategy clearly, provide guidance, and motivate employees towards the desired outcomes. Communication channels ought to be open, transparent, and promote a culture of collaboration and feedback.

Employee Involvement and Empowerment: Involving employees in the strategy implementation process increases their commitment and ownership. Empowering employees through delegation of authority and decision-making cultivates a sense of responsibility and accountability, leading to enhanced performance and innovation.

Introduction to Corporate Governance

Corporate governance encompasses the framework of rules, practices, and processes that guide the direction, control, and management of a company. It involves the interactions among various stakeholders, including shareholders, management, the board of directors, employees, customers, suppliers, and the community. Successful corporate governance is vital for a company's long term success and sustainability. It helps establish a framework for responsible decision-making, accountability, and transparency, which enhances investor confidence and protects the interests of stakeholders.

 

Introduction

The strategic control process is an essential component of strategic management that helps organizations monitor and evaluate their strategic plans and initiatives. It involves a systematic approach to assess the progress performance, and effectiveness of strategic objectives and make necessary adjustments to ensure alignment with organizational goals. Here are some key points on the introduction to the strategic control process. Strategic control refers to the ongoing monitoring, evaluation, and adjustment of an organization's strategic activities to ensure they are in line with the desired outcomes and objectives. It The significance of strategic control lies in several key reasons. helps organizations track their progress, identify deviations from the intended course, and take corrective actions. It also enables managers to assess the effectiveness of strategies, make informed decisions, and enhance organizational performance.

Introduction to Evaluation in Strategic Management Process

Strategic management is the process of formulating and implementing strategies to achieve an organization's goals and objectives. It entails making decisions and taking actions that align the organization with both its external environment and internal capabilities. Evaluation is a crucial component of the strategic management process as it helps assess the effectiveness and efficiency of the strategies implemented. It involves the systematic and objective assessment of various aspects of the organization's performance, including its strategy, resources, operations, and outcomes. The primary purpose of evaluation in strategic management is to determine whether the strategies are yielding the desired results and to identify areas for improvement. It offers valuable insights into the organization's strengths, weaknesses, opportunities, and threats, enabling informed decision-making and adjustments to strategies as necessary.

1. Indian Major carps belongs to which family?

 a) Cobitidae

c) Ariidae

b) Cyprinidae

 d) Clupeidae

2. Body of carps is covered by which scales?

 a) Placoid

b) Ganoid

 c) Cycloid

3. Family cyprinidae includes......

 a) Catla catla

c) Cirrhina mrigala

d) Ctenoid

 b) Labeo rohita

 d) All of these

4. Selection criteria for cultivable fish includes .........

 a) It should have a fast growth rate

 b) It should be a hardy and resistant to disease

 c) Its flesh should be tasteful and with high nutritive value

 d) All of these

5. Why carps are suitable for culture?

 a) They can tolerate low oxygen content

 b) They have fast growth rate

 c) They feed on plankton,decaying weeds and debris

 d) All of these

1. To which family freshwater prawn belong?

 a) Arthropoda b) Crustacea

 c) Palaemonidae d) Malacostraca

 2. Scientifc name of freshwater prawn is .........

 a) Penaeus monodon b) Scylla serrata

 c) Macrobrachium rosenbergii d) Metapenaeus monoceros

 3. Freshwater prawn is also known as .........

 a) Giant river prawn b) Scampi

 c) Banana prawn d) Both a & b

 4. Identification features of freshwater prawn are .........

 a) Presence of grey and longitudinal streaks of light and dark colour

 b) The largest pereiopod of adult male is dark blue.

 c) The eggs of berried female are yellow in colour

 d) All of these

1. The word aquarium for the first time was used by ..........

 a) Philip Henry Gosse   b) Anne Thynne

 c) Philip Rottz   d) Britz Carles

 2. Who built the first stable marine aquarium in 1846?

 a) P.H.Gosse       b) Philip Rottz

  c) Britz Carles      d) Anne Thynne

 3. In 1853,the first large public aquarium was opened in .........

  a) Roman empire         b) London zoo

    c) Victoria era       d) None of these

4. Taraporewala aquarium came into existence in .........

 a) Year 1951       b) Year1945

   c) Year 1974        d) Year 1981

1. Live feed is also known as .........

 a) Natural food b) Fish food organisms

 c) Both a & b d) Algae

 2. Live feed are organisms used as food by .........

 a) Prawns b) Carps

 c) Oysters d) All of these

 3. The common phytoplankton used as live feed are…………

 a) Eudorina b) Volvox

 c) Pediastrum d) All of these

 4. The common  zooplankton used as live feed are .........

 a) Vorticella b) Artemia

 c) Cyclops d) All of these

1. Which of the following is an example of freshwater mussel?

 a) Lamellidens marginalis b) Lamellidens corrianus

 c) Pictada fucata d) Both a & b

 2. ......... is secreted by the mantle of the pearl oyster as a protection against foreign objects.

 a) Pearl b) Swarm

 c) Prismatic layer d) Nacreous layer

 3. Shell of pearl oyster is composed of ......... layers.

 a) Two b) Three

 c) Four d) Five

 4. Which is the outermost layer of shell?

 a) Prismatic layer b) Nacreous layer

 c) Periostracum layer d) None of these

 5. ......... is the outermost, greenish-brown, thin, translucent layer made up of conchiolin.

 a) Nacreous layer  b) Mantle

 c) Periostracum d) Perismatic layer

1. Corals are .........

 a) Animals b) Plants

 c) Diatoms d) Plankton

 2. Corals belongs to which phylum?

 a) Protozoa b) Echinodermata

 c) Cnidaria

 3. The corals consists of a sac-like structure called .........

 a) Septa b) Polyp

 c) Haloclone d) Nephris

 4. ......... build reefs found in shallow tropical seas.

 a) Hermatypic corals b) Ahermatypes corals

 c) Both a & b d) None of these

 5. ......... per cent of the corals are located in the Indian ocean,Red sea and Persian Gulf.

 a) 30% b) 5%

 c) 10% d) 70%

1. Which period is known as the ‘operation flood era’?

  a) 1971 and 1996     b) 1978 and 1999

  c) 1969 and 1974    d) 1974 and 1978

 2. In which year Indian government launched ‘operation flood program’?

   a) 1970     b) 1950

 c) 1960   d) 1972

 3. Which is the largest milk producer in the world?

   a) China   b) Germany

 c) Russia   d) India

 4.  ‘White Revolution’ in concerned with .........

   a) Egg production   b) Meat production

 c) Grain production  d) Milk production

1. Rearing of fowls for eggs and flesh is known as  ….

 a) Poultry keeping b) Husbandry   

 c) Sericulture d) Apiculture

 2. The types of poultry birds are  ….

 a) Broilers b) Layers  

 c) General purpose birds d)  All of these

 3. The layers are reared mainly for  ….

 a) Flesh b) Eggs  

 c) For both d) None of these

 4. Which of the following are layers?

 a) Anconas  b) Australorp   

 c) White leghorn d) All of these

 5. Broilers are reared for  ….

 a) Flesh b) Fish  

 c) Ornamental purpose d) All of these

1. ‘Apis’ means …. . .

 a) Waxf b)   Bee      

 c)  Honey d) All of these

 2. Apiculture is also known as …. . .

 a) Bee-keeping b) Wax preparation                

 c) Both a & b d) None of these

 3. Importance of bee-keeping …. . .

 a) It provides honey      

 b) It provides bee wax             

 c) Honey bees are excellent pollinating agents, thus increasing agricultural

 d) All of these

 4. Species of honey bee is …. . .

 a) Apis dorsata b) Apis indica             

 c) Apis florae d) All of these

1. …. . involves rearing of silkworms.

 a) Lac culture b) Sericulture  

 c) Apiculture d) Pisciculture

 2. ‘Silk Samagra’ scheme was launched by …. . .

 a) Government of Karnataka b) Government of Tamil Nadu  

 c) Government of Punjab  d) Government of India

 3. Match the pairs

 a) Mulberry silkworm          1. Antheraea paphia

 b) Tasar silkworm                  2.  Attacus ricinii

 c) Muga silkworm                 3.  Antheraea pernyi

 d) Eri silkworm                        4.  Attacus altas

 e) Oak silkworm                     5.  Bombyx mori

 f) Giant silkworm                  6.  Antheraea assama

 a) a=5, b=2,c=3,d=4,e=1, f=6 b) a=5, b=1, c=6, d=2, e=3, f=4

 c) a=2, b=4, c=3, d=1, e=6, f=5 d) a=2, b=3, c=2, d=1, e=4, f=6

 4. ‘Silk Samagra’ scheme was launched for period of …. . .

 a) 2017-2020 b) 2016-2019 

 c) 2014-2017 d) 2021-2024

1. The rearing of earthworms for composting wastes into valuable nutrients

is known as …. . .

 a) Organic farming b) Organic manuring

 c) Vermitechnology d) Sustainable technology

 2. Vermitechnology provide …. . .

 a) Vermiwash b) Vermicompost

 c) Organic fertilizer d) All of these

 3. Vermicompost improves …. . . property of soil.

 a) Physical b) Chemical

 c) Biological d) All of these

 4. In class oligochaeta …. . .

 a) Parapodia  absent b) Clitellum present

 c) Pharynx without jaws d) All of these

 5. In which order earthworm is placed?

 a) Oilgochaeta b) Frrantia

 c) Neooligochaeta d) Sedentaria

1. The first scientific account of the lac insect was given by …. . . .

 a) J. Kerr (1782) b) W.Kritz (1882)   

 c) Pamella Britz (1888) d) None of these

 2. The scientific name of lac insect is …. . . .

 a) Tachardia lacca b) Tachardia indica   

 c) Tachardia suzi d) Tachardia essart

 3. Lac is secreted by insects for …. . . .

 a) Feeding b) Reproduction  

 c) Protection from predators d) Both a & b

 4. The life cycle of lac insect takes about …. . . months.

 a) Two months b) Six months   

 c) Ten months d) Twelve months

 5. The life cycle of lac insect consists of …. . . .

 a) Nymph instar b) Pupa   

 c) Adult  d) All of these

1.0 General

 The design of canal involves following three major aspects:

 A Irrigation and drainage canal network planning.

 B Determination of discharge / capacities of  canals and drainages.

 C Determination of shape and size i.e. geometrical dimensions of the proposed canal section, generally

 known as design of canal section. More appropriately it can be called as design of internal section

 of canal. It can be further divided in three following parts viz.

 i. Design of unlined canals:

 ii. Design of lined canals: or a canal may be partly lined and partly unlined.

 iii. Choice between the unlined and lined canals. This aspect should be distinguished from the selection of type of lining. However, the scope of choice of lining may include both the aspects.

 The design of canal section remains incomplete without the design of its embankments, either in lined or unlined canal, so the next aspect is

 iv. Design of canal embankments. Still to complete the design of a canal one more aspect is required i.e.

 v. Canal aesthetics and canal arboriculture.

Theoritical aspects of design has been discussed in chapter 1. Discharging capacity of canal is

 computed by Manning Equation (1)and continuity Equation (2) (Para 1.4).

 In the Manning's equation  V  =   R2/3  S1/2 / N (Eq. 1, Para 1.4);  N and R play most important role.

 The effect of  R has been discussed in para 1.4.1, Chapter 1. In this chapter the complexities of Manning's n (or N, also known as Manning's rougosity coefficient

 or simply rougosity coefficient) and some practical aspects of composite sections and velocity in best

 Mehboob, trapezoidal or rectangular sectioned channels are discussed.

2.0 Manning's Rugosity Coefficient (n)

Value of N depends upon a number of factors such as , surface roughness, vegetation, obstructions, type of lining, age of lining, erosion and damages in lining, curves, discharging capacity , perimeters, depth of flow, velocity etc. These are discussed below and commonly adopted values of n are given in tables 2.1 to 2.3.

 

A canal of approximate discharge of less than 80 cumecs (3000 cusecs) discharge may be called a medium or low capacity canal).

3.1 Mehboob Section

A Mehboob Section is only a modification of best trapezoidal section by making its complete bottom circular as shown in figure 3.1. Also see figure 1.6 and para 1.7. That is why these canals are also known as canals of zero bed width, or trapezoidal canals with curved bottom, or cup shaped canals.

But these cannot be called as triangular canals with curves, as often spoken. In fact it is a truncated triangle circumscribing the semi circle. Trapezium is also a truncated triangle circum scribing a circle.

More accurately it can be said as a combination of semi circular and best trapezoidal section so as to take full advantage of their hydraulic efficiency and practicability.

This is such a beautiful section that the designer can never commit a mistake, even if he wants to commit a mistake. The dimensions are so geometrically fixed that no one can play with any dimension.

The value of this section was realised for the first time by Er S I Mehboob (1943) in his Punjab Irrigation Branch Paper No.260" Lining of Canals". There after this canal section became well known as Mehboob Section after his name. He also stated that the most economical section i.e. the maximum area for the minimum perimeter is when the centre of the arc is at the full supply line of the section. This is also evident in figure 1.6. This is also the condition for all best hydraulic sections. This section is also useful to a higher silt carrying capacity than a wide shallow one..

 

 

A canal of more than 80 cumecs (3000 cusecs) approximetly may be called as a high capacity canal.

It has already been pointed out in chapter 1, that for the best section of any shape, R is always equal to d/2 only, and such depth is the most optimum depth. But such channels are not always practical or most economical due to many other practical reasons, (see para 4.1 and 6.3) and many times shallower depth then this depth is adopted. Typical examples are lining of existing earthen channels, given in para 6.3.

All the best sections have fixed ratio of dimensions very near to the semi circle. For large discharges, radius of semi circle increases that, means depth of canal as well as velocity increases. But higher velocity is not permissible with every type of lining (see table 2.1 and para 2.3). Similarly depth also has limitation of construction, maintenance, stability of slopes, etc. see example–4.2.

Because of limiting velocities and depth, deeper canal section are not practically feasible/possible. Therefore for large discharges, say more than 100 cumecs, Mehboob or best trapizodal sections are just not possible/practical, and recourse has to be taken to trapezoidal section with increased bed width then depth. This section is hydraulically inefficient or uneconomical. Larger the B/D ratio, larger is the inefficiency and section is more costly. (For Mehboob Section, B:D ratio is zero and for best trapizodal section it is much less than 1, see para 3.1, table 3.1 para 3.5 and table 3.4).

A. Rectangular Canals

5.1 Design of Best Rectangular Canals

After the Mehboob and Trapezoidal canals, next best section is rectangle. This section is also generally adopted for small discharges say upto 10 cumecs (350 cusecs). This section ranks at No. 4. Yet, this is the only section most commonly adopted for flumes of discharges upto140 cumecs (5000 cusecs). Typical example is Orsang acqueduct, 1 km. long for 1140 cumecs (40000 cusecs) in ractangular sections on Narmada Canal Project, Gujrat, India, built in the years from 1990 – 2000. Such sections are common in power house canals due to limitation of space and very sharp curves. It is free from the effect of change of side slopes as in the trapezoidal canals (see para 3.3) Still some small side slope (batter) is adopted in thick masonry / concrete lining. It adds to structural stability with economy in thickness of wall and as well as hydraulic efficiency. It may still be called as rectangular section.

Details of its hydraulic efficiency in comparison to semi circle, Mehboob and trapeziodal sections are discussed in chapter 1, para 1.8

6.0 General

No lessons can be drawn without the experiences of the past. In fact, much of engineering, rather all sciences have developed in bits and pieces from the experiences of the past. The engineers by and large have never become wiser in one day. They have become wiser slowly and slowly with their own experiences and from the experiences of past engineering works. Wisdom lies to gain wisdom from experiences/draw lessons from the marvellous engineering works constructed so far. It helps us in a direction for further development of technological skills.

Here an effort is made to draw lessons from experiences of the canals constructed in the past.

 There are several aspects of such experiences. A few important aspects of studies are:

 (i) Internal hydraulic sections of the canal with respect to discharge, topography, stability of side slopes, type of lining, operation and maintenance problems and their solutions etc.

 (ii) Design of canals banks (embankment) with respect to discharge, depth of flow, depth of cutting /filling, below/above natural ground level, type of soil, topography, operation and maintenance, drainage of banks etc.

 (iii) Canals aesthetics and arboriculture

 (iv) Other utility/service of canal such as multi-objectives of hydropower, irrigation, drinking/industrial water supply, social needs of recreation, forests etc.

A brief description of IGMC is given in Para 6.6 with details of the canal sections. Here an attempt is made to review the hydraulic performance of the Main Canal.

7B.1 Study of Flow Characteristic of the Main Canal

The main canal is designed for a maximum discharge of 524 m3/s at head i.e. at Km. 0. Large of number of branches, distributaries and minors off take from the main canal at different location all along the canal. These are shown in the L- section, Figure 7B.1 and a statement off taking canals is given Table 7B.1. Many distributaries such as lift canals have been planned subsequently. Also discharges of few off taking channels have been changed. A clear and updated data are not readily available. So data available are indicated in the Table and plotted in the L - section. A half bottom plan of the canal is also shown in Figure 7B.2

8.1 Design of Canal Embankments

 There are two aspects of canal embankment design.

 (i) The general requirement, such as free board, dowel, service/inspection road, drainage of banks etc. The aspect of free board is discussed in Para

8.2 to 8.3 and that of service/ inspection road in Para

 8.4 and drainage of banks in Para 8.5 in this chapter.

 (ii) Safe design of embankment i.e. Hydraulic and structural requirement.

It includes stability of inner and outer slopes, Structural safe design of inner cut slopes in deep cuttings in different types of soils/rocks of varying nature and ground water conditions. Stability of slopes may include drainage, so as to prevent rock falls/debris in canal. For outer slopes in embankment (filling sections) hydraulic gradient/pharetic line, toe drains etc, similar to the earthen dams, on different types of soil/rock foundation with respect to varying ground water level conditions are important.

The canal embankments in filling may even fail through its foundations by sloughing or liquefactions. All these aspects of canal embankments are discussed in this chapter in Para 8.4 to 8.9.

So far much have been talked about the best hydraulic sections, as these appeared to be most economical sections. But as it appears may not be always true and there are exceptions to each individual site conditions.

9.1 Suitable and Economical Cross Section

Best hydraulic sections are most economical in lined and unlined canals. In lined canal this section is economical as long as the cost of earth work (excavation and embankment) is less than the cost of lining per unit length of channel and this is the most usual case. These sections, besides having least area and perimeter also have minimum top width. Therefore land width required is also minimum, in comparison to other wide sections. But if the cost of earth work / excavation is more, such as in rocks, or in very heavy cutting/filling, best hydraulic section do not remain most economical, and recourse to alternative section must be worked out.

Even in unlined channels in deep excavation or in deep filling recourse must be taken to narrower and deeper sections as far as possible.

Also the type of lining in the bed may be cheaper than the lining on sides and in such cases, lining of best section is no more most economical and cost economics of various alternatives including increase in bed width has to be worked out, see para 4.1 and 6.3.

Introduction

An integrated approach to understanding the changing dynamics in water resources involves taking into account various factors that influence the management of water resources under changing climatic conditions (Pahl-Wostl, 2007). It is a holistic approach that combines different disciplines, including hydrology, climatology, ecology, geography, and social sciences, to develop a comprehensive understanding of water resources. The integrated approach is essential for several reasons, including addressing climate change impacts, sustainable management of water resources, enhancing water security, addressing water quality issues, and promoting collaboration among stakeholders. Climate change is causing alterations in precipitation patterns, melting glaciers, and changes in river flows, affecting water availability and quality. An integrated approach helps in understanding the climate change impacts on water resources and developing adaptation strategies (Asif et al., 2023).

Introduction

R language is a popular and versatile tool for hydrological data analysis and visualization, offering several powerful packages for handling and analyzing hydrological parameters such as river flow, precipitation, groundwater levels, and water quality parameters. R language provides a wide range of statistical functions for hydrological parameter analysis, including time-series analysis, frequency analysis, trend analysis, and regression analysis. Moreover, R’s powerful graphics capabilities make it possible to create high-quality and customizable visualizations of hydrological data. In addition to its statistical and graphical capabilities, R also offers a wide range of packages for data manipulation and cleaning, making it a powerful tool for preprocessing hydrological data. R’s ability to handle and analyze large datasets also makes it well-suited for processing and analyzing large hydrological datasets. Furthermore, R has a strong community of developers and users who regularly contribute new packages and tools, providing a constantly evolving stage for hydrological analysis. Overall, R is a versatile and powerful tool for hydrological data analysis and visualization, offering numerous capabilities and resources for informed decision-making in water resources management and related fields.

Introduction

Hydrological data visualization plays a crucial role in analyzing and understanding the behavior and characteristics of water resources. By visualizing hydrological data, we can gain insight into the distribution, variability, and trends of different hydrological variables such as river flow, precipitation, groundwater levels, and water quality parameters. This information can help decision-making in areas such as water resources management, flood forecasting, and climate adaptation.

R Language is a popular and versatile tool for hydrological data visualization, offering several powerful packages for data analysis and visualization. Each of these packages has its own strengths and limitations, which can be leveraged depending on the specific needs of a given analysis. For example, ggplot2 is an extensively used package for creating publication-quality static plots that are customizable and visually appealing. This package is based on the grammar of graphics, which provides a new plotting system that combines the benefits of both base and lattice graphics. ggplot2 can be used to create a wide range of plot types, including time-series plots, spatial maps, and scatterplots. It provides several options for customizing plot elements such as axis labels, legends, and plot themes. On the other hand plotly offers interactive and dynamic plots that can be easily shared and embedded in web applications. With plotly, we can create plots that allow for zooming, panning, and hovering over data points to display additional information. This package is mainly useful for exploring relationships between hydrological variables. In addition to these packages, hydroTSM provides functions for reading, manipulating, and plotting time-series data. hydroTSM can help us create time-series plots of variables such as river f low, precipitation, and temperature, with options for adding trend lines error bars, and annotations.

Introduction

Evapotranspiration is a vital process in the Earth’s water cycle, and it involves two main processes—evaporation and transpiration. Evaporation refers to the conversion of water from its liquid state to its gaseous state, which occurs primarily from the surface of bodies of water, wet vegetation, and soils. This process requires energy, which is typically provided by direct solar radiation and ambient air temperature. Transpiration, on the other hand, is the process by which plants release water vapor through small openings in their leaves called stomata. This process is essential for plant growth and function as it helps regulate the plant’s temperature and allows for nutrient uptake and water transport. Like evaporation, transpiration also requires energy, with factors like vapor pressure gradient and wind speed influencing the rate at which water is released.

To accurately measure evapotranspiration, it is necessary to consider a range of factors such as temperature, radiation, wind speed, and humidity. Plant parameters, such as the presence of a cuticle or the size and number of stomata, can also greatly influence the transpiration rate. Other factors that may impact evapotranspiration include the season, time of day, and soil properties such as water holding capacity and root depth. Evapotranspiration is a complex concept that has been studied by various scientists and organizations over time. Three main evapotranspiration (ET) concepts have been developed: Reference ET, ET under standard conditions, and ET under nonstandard conditions. The goal of determining potential or reference evapotranspiration is to exclude crop-specific variations from the evapotranspiration process. The potential evapotranspiration concept, introduced in the late 1940s and 50s by Penman, assumes constant crop conditions and defines it as “the amount of water transpired in a given time by a short green crop, completely shading the ground, of uniform height and with adequate water status in the soil profile.” ETo, which is a climatic parameter, represents the evaporating power of the atmosphere at a certain location and time of the year and is computed from weather data. It does not take into account crop characteristics or soil conditions, and the only factors affecting it are climatic parameters. The range of ETo varies from 1–2 mm/day in humid and subhumid temperate regions to 6–9 mm/day in warm arid and semi-arid temperate regions and from 2–3 in cool humid and subhumid tropics to 6–8 mm/day in warm arid and semi-arid tropics. Actual ET is the amount of water on a large area of grass that is actively growing and covering the entire soil surface it actually uses. The amount of water that is actually evacuated from a surface as a result of the processes E and T is known as ETa. Crop water needs can be calculated by the following equation.

Introduction

Droughts are considered to be a significant environmental disaster and have captured the attention of various experts, including environmentalists, ecologists, hydrologists, meteorologists, geologists, and agricultural scientists. These natural calamities are known to occur in almost all climatic zones, regardless of whether they are high- or low-rainfall areas. Generally a decrease in the amount of precipitation received over an extended period such as a season or a year, is the primary cause of droughts. However, other factors such as high temperatures, strong winds, low relative humidity, and characteristics of rainfall (e.g., intensity, duration, onset, and termination) also play a crucial role in their occurrence. It’s important to distinguish a drought from aridity, which is a permanent feature of a climate and is limited to low rainfall areas (Wilhite, 1992). A drought is a temporary phenomenon and can last from a few months to several years. Some people often mistake droughts for heat waves, but Chang and Wallace (1987) highlighted the fundamental difference between these two events. A heat wave typically lasts for a week, while a drought can extend for several months or even years. When a heat wave and a drought coincide, it can lead to dire socio-economic consequences.

Introduction

In recent decades, severe weather and climatic occurrences have become more frequent, affecting human populations across the globe. These changes are caused, in part, by anthropogenic climate change. Vulnerable communities are particularly affected by these rapidly changing patterns, which can have catastrophic consequences. To mitigate the impact of climate change, communities must have access to accurate and up-to-date climate information. Weather predictions, from short-term forecasts to long-term scenarios based on data from previous decades and centuries, can provide valuable information to socioeconomic sectors that are vulnerable to climate change. Climate services offer scientifically-based data and forecasts that can assist decision-makers in managing the risks of climate variability and change. Globally accepted indices generated from daily temperature and precipitation data can indicate the more severe features of climate change. These indices are developed and analyzed by the CCI/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) and have made a significant contribution to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

Introduction

The hydrological cycle transfers water and energy between the land and atmosphere, but its regional characteristics make accurate modeling challenging. Hydrological models aim to predict hydrological events and processes, but there are uncertainties in their output due to the complex nature of the system and assumptions made. Empirical models use statistical or mathematical relationships, while process-based models use equations to represent physical processes.

Hydrological modeling involves various steps, leading to uncertainties that can affect the accuracy of model predictions. The four main types of uncertainties commonly encountered are data, model, parameter, and climate uncertainty. Data uncertainty arises due to errors or gaps in the input data, model uncertainty stems from limitations in the model structure or assumptions used, parameter uncertainty arises due to uncertainties in the values allocated to various model parameters, and climate uncertainty refers to uncertainties in the future climate projections used as inputs.

Introduction

Time Series Analysis (TSA)

TSA is a crucial tool in the study of climate change, as it allows researchers to carefully examine patterns and changes in weather parameters over time. By analyzing time series data for temperature, precipitation, and other weather variables, scientists can identify trends and develop models that accurately describe the underlying structure of the series. These models can then be used to make predictions about future changes in weather patterns and the potential impacts of climate change on the environment and human society. The accurate forecasting of climate change using TSA can inform policy decisions and facilitate adaptation strategies to mitigate the effects of a changing climate.

Components of Time Series

When analyzing a time series, we often want to separate the different components that contribute to its overall behavior. The classical decomposition method is a widely used technique that breaks down a time series into four components: trend, seasonal, cyclical, and irregular.

Introduction

Time series analysis is a powerful tool for detecting and describing sequential processes, and it has become increasingly important in water resource management, especially in the context of global warming and climate change. Statistical methods have been widely used for analyzing climatological time series, such as precipitation, air temperature, and evapotranspiration, and hydrological time series, such as streamflow and surface water quality. However, most of the linear time series models depend on strict assumptions, such as homogeneity, trend-freeness, stationarity, non-periodicity, and lack of persistence, which are often violated in real-world datasets. Therefore, there is a need to develop new models that are more flexible and can accommodate different types of data (Machiwal & Jha, 2006).

One of the most popular and widely used classical time series models is the Autoregressive Integrated Moving Average (ARIMA) model, which has linear statistical properties and can be built using the Box-Jenkins methodology (Box and Jenkins, 1970). This model adoption is important for hydrological analysis because it can help in accurately characterizing and predicting the behavior of hydrological time series data. ARIMA models can capture complex patterns and trends in the data and can be used to forecast future values of the time series. This can help in better understanding the underlying processes that drive hydrological variability, such as changes in precipitation patterns or land use. Furthermore, hydrological data often exhibit seasonality and long-term trends, which can be accounted for in ARIMA modeling. ARIMA models can also be used for anomaly detection, identifying unusual events that may have significant impacts on water resources. Overall, ARIMA modeling provides a f lexible and powerful framework for analyzing and forecasting hydrological time series, which is essential for effective water resource management in the face of changing environmental conditions.

Introduction

Climate variability can have significant impacts on agricultural production, affecting both the demand and supply sides of farming. As climate change continues to negatively impact our ecosystem, food production, and human livelihood, this topic has gained widespread attention from researchers and academics worldwide. Given that agriculture is highly dependent on factors such as precipitation and temperature, it is considered one of the most climate sensitive sectors (Sagar et al., 2022).

Climatic data is complex and nonlinear, with significant fluctuation across time components, resulting in greater volatility. This means that stochastic climatic variables, including rainfall, temperature, relative humidity, and other weather parameters, often exhibit conditional heteroscedasticity, leading to irregular residual mean and variance across time series.

Introduction

Drought is an evolutionary process often triggered by precipitation deficiencies and may result in enormous economic losses. Effective early drought warnings and predictions may be beneficial for the farmers. To determine drought and its severity level, meteorological drought indices such as the standardized precipitation index, effective drought index etc. are utilized. Drought events can be foreknown using prediction models and by predicting these indices. Many studies on prediction have primarily focused on statistical approaches such as Holt-Winters, auto-regressive integrated moving average (ARIMA), etc. These statistical techniques are insufficient for providing long-term predictions. However, with improvements in machine learning techniques, there seems to be a mechanism to forecast drought in the long run with high accuracy. Frequently used machine learning algorithms in drought forecasting are artificial neural network (ANN), support vector machine, etc. Machine learning techniques have a powerful and versatile data-driven algorithm for capturing and representing complex input and output relationships.

Introduction

Rainfall is a crucial component of the Earth’s climatic system that affects various aspects of human life. It has a direct impact on the ecosystems, water resources, agricultural production, and overall water management. Dependence on rainfall is particularly high in regions where it is the primary source of water, making it essential to have an accurate understanding of future rainfall patterns. Given its importance, rainfall forecasting is a critical concern for a wide range of stakeholders, including governments, businesses, risk management organizations, and the scientific community. However, predicting rainfall is a complex task as it is a random occurrence influenced by numerous factors (Samantaray et al., 2020). Even in similar weather conditions, the possibility of rainfall may vary. This makes historical data and predictive analytics crucial in forecasting future rainfall and taking necessary actions to manage its effects (Avinash et al., 2022). The impact of rainfall extends beyond water management to affect various human activities, including power generation, construction, tourism, and forestry, among others. Therefore, it is crucial to accurately forecast and analyze rainfall patterns to help mitigate the risks and consequences associated with it (Hernandez et al., 2016).

Introduction

Time series data is data that has been collected in a specific pattern with time stamps. (Nayak and Ghosh, 2013). This type of time-oriented data is collected at regular intervals, such as hourly, daily, monthly, or weekly. Time series data can be used to make accurate predictions in a variety of fields and domains, such as foreign currency rates, stock market trends, energy consumption estimates, and climate change. Machine learning and data mining techniques can be used to uncover hidden patterns in historical data and forecast future trends (Aftab et al. 2018a, 2018b, Mishra et al. 2017).

Weather forecasting based on historical data is a complex but very beneficial task that comes with several issues that must be resolved to achieve optimal results (Wu and Chau, 2013). Weather data includes attributes or features such as temperature, pressure, humidity, and wind speed. Machine learning techniques are used to forecast future weather conditions by identifying hidden patterns and relationships in historical weather data (Aftab et al. 2018b). Prediction of precipitation is an important part of the weather forecasting process. The use of machine learning and artificial intelligence techniques is regarded as a critical component in farmer services and policy formulation. Farmers require weather forecasting to plan their activities following the predicted weather. Accurate and timely rainfall prediction in farming, in particular, can be extremely beneficial in planning and security measures for operations, agricultural tasks, water reservoir systems, and construction and transportation activities (Chau and Wu, 2010). Forecasting rainfall will become increasingly important in the future.

Introduction

Drought is defined as “when a region receives below average precipitation,  resulting in prolonged shortages in its water supply, whether surface  or ground water or atmospheric. It can have a substantial impact on the  ecosystem and agriculture of the affected region.” When drought arises in  a particular area, obviously it affects the crop and livestock production. In  order to reduce the effect of drought on farm productivity and to stabilize farm  income, farmers have to take some systematic measures, and such measures  are referred to as drought coping mechanisms. The objective of the study was  to identify the determinants of the adoption of drought-coping mechanisms to  balance and stabilize the farm income of the stakeholders. The study also helps  to know how to mitigate the effect of drought on farmer’s livelihoods.

The existing literature on the adoption of drought coping mechanisms is  quite meager. Akwango et al., (2016), Chandan Kumar Jha et al., (2017),  Emmanuella Ellis (2017), Ronald Twongyirwe et al., (2019) assessed the adoption strategies by considering multinomial logistic regression and the  results state that the use of different tree planting, crop varieties, soil and water  conservation, early and late planting, and furrow irrigation. The data related to  the adoption of any agriculture technology have qualitative response variables  with two or more categories. Most of the studies used qualitative response  models such as the logit model, probit model, and multivariate techniques like  discriminant analysis to measure the farmer’s perception toward the adoption  of any agriculture technology. In this study, we chose machine learning models  such as Decision tree, to assess the classification ability and to determine the  factors influencing adoption.

Introduction

Drought occurs when a region experiences below average precipitation, resulting in prolonged shortages in its water supply, which can have a significant impact on agriculture and the ecosystem. To reduce the effect of drought on farm production and stabilize farm income, farmers must take systematic measures called drought coping mechanisms. These measures can be categorized into two broad categories: Ex-ante coping mechanisms, which are taken before the occurrence of drought, and Ex-post coping mechanisms, which are taken after the occurrence of drought. Ex-ante coping mechanisms include activities such as crop, spatial, and varietal diversification, income diversification, and the use of savings. Ex-post coping mechanisms include changing consumption patterns, borrowing money, liquidating assets, seeking government drought relief funds, and migration. Drought can have a significant impact on crop and livestock production, causing farmers to adopt drought coping mechanisms to stabilize their farm income. This study goals to identify the factors that determine the adoption of such coping mechanisms, to mitigate the effects of drought on the livelihoods of farmers.

Introduction

Agriculture is backbone of India’s economy and is largely dependent on the monsoon. Rainfall plays a dominant role in agricultural production and productivity, despite advancement in many technological fronts. Forecast of rainfall is essential for planning and management of water resources especially in an agriculture-based country like India. About 65 per cent of the total cultivated land in India is under the influence of the rainfed agriculture system. Especially, monthly and seasonal rainfall forecasts provide useful information for water resource management, agricultural planning and its associated crop insurance application (Garbrecht et al., 2010).

The early prediction of rainfall is essential which help farmers to make decisions on crop production and harvesting, as well as help prevent flooding, protect human lives and resources. Rainfall forecasting is very challenging due to its uncertain nature and dynamically changing climate. It is an application of science and technology to predict precipitation in advance. It’s always been a challenging task for meteorologists. Prediction of precipitation is categorized into short-range prediction and long-range prediction. Forecasting is done through the collection and analysis of weather and climate data. Avinash et al. (2022) studied temporal variation of land use pattern in Karnataka through indices and concluded that due to fluctuations in the rainfall the forest land is degrading year by year.

Introduction

Artificial intelligence (AI) is a field that mainly focuses on creating intelligent technologies that can easily perform tasks requiring human intelligence. One subfield is machine learning (ML), which enables machines to learn from data without being explicitly programmed. There are two main types of ML: supervised and unsupervised learning. Supervised learning trains machines on labeled data to predict output, while unsupervised learning trains machines on unlabeled data to find patterns. ML includes techniques such as regression, classification, clustering, and deep learning. ML has the potential to revolutionize many fields but also raises ethical, social, and legal issues. Developing trustworthy, responsible, and ethical ML systems is crucial. The MuCulloch and Pitts Model, proposed by Warren MuCulloch and Walter Pitts in 1943, is the first formal model of ML techniques. It is also known as the linear threshold gate (Fig. 1).

Introduction

The study of spatiotemporal time series forecasting has gained significant attention in recent years, as it involves analyzing records of events that occur over both space and time. Spatiotemporal models group time- and location-specific discrete variables and are used in various sectors, including agriculture, transportation, meteorology, economics, disease mapping, sociology, environmental sciences, and ecology. However, the popular Autoregressive Integrated Moving Average (ARIMA) (Box and Jenkins, 1970) model has two major flaws: it assumes linearity, which is not always the case in time series data that may exhibit nonlinear interactions, and it is a univariate model that cannot capture spatial linkages. Hence, when investigating time series data that are not perfectly linear and involve spatial correlation, the ARIMA model should not be used. Other multivariate time series models like VARMA have also been used to represent spatiotemporal data. However, VARMA models require more parameters, and the STARMA model introduced by Cliff and Ord has fewer parameters (Cliff and Ord, 1975). In previous literatures, the STARMA model was commonly used for modeling different spatiotemporal time series data (Kamarianakis, 2003). The STARMA model was previously employed to forecast the spatiotemporal data in different fields such as economics, disease mapping, climate modeling, agriculture, etc. (Pfeifer and Bodily, 1990; Lee, 2005; Rathod et al., 2018; Saha et al., 2020 and Rathod et al., 2021).

Introduction

Maize is the second most important cereal crop in the world in terms of acreage and is called the ‘Queen of Cereals’. Corn production is a vital component of the global agricultural sector, contributing significantly to food, feed, and industrial demands. Maize, it is the most popular crop among the farmers all over the world. Maize grows throughout the year.USA leads in the production followed by China. India ranks 3rd in global maize production; the major maize-growing states are Karnataka, Madhya Pradesh, Maharashtra, Rajasthan, Bihar, and Uttar Pradesh. After the cereal production of wheat and rice, maize has significantly held a strong position in the third position out of the many crops produced in India. The major usage of maize is for human and animal consumption, it is also used in baby corn oil, corn starch, corn oil production, and much more. In the India, not less than 15 million farmers are engaged in maize cultivation and it generates employment for more than 650 million person-days at farming and its related ecosystem levels. Corn could be grown in variety of soil under Indian conditions but most preferable soil is light, free-draining soil that is full of organic matter. This type of soil obviously provides great drainage, water retention and nutrition. Rich, light soil enables the roots of corn plants to grow downwards quickly, which allows them to get a good foothold which stabilizes the plants effectively. Corn is known for its shallow root system, which means it is inherently unstable and will struggle in dry conditions, which makes soil quality all the more important. High winds can easily damage the plants and prevent pollination. Corn is a water-hungry crop with a shallow root system so it needs to be watered regularly. Under watered corn quickly becomes stressed, which results in early maturation and underdeveloped cobs.

Introduction

After harvesting of crop, a huge or less amount of residues get produced from crop fields. Crop residue is the most reachable and ready to serve form of biomass. For agricultural lands, to improve soil quality, crop derived residue is considered as “the greatest source of soil organic matter” (Tisdale et al., 1985). But for farmers, the easiest way to get rid of this residue is on farm burning of it. This type of residue management practices not only give rise to fire hazards and one of the main source of atmospheric pollution but also lead to loss of nutrients and organic matter from soil. The Ministry of New and Renewable Energy, reported that almost five hundred M tons of crop remains is produced every year from India. It has also been reported that a major part of these crops remains is being used in the form of fodder, forage and other industrial purposes. In India, farmers burn their crop residue for feasible management of it, although it costs a lot to our soils. It is estimated that various crops residue contains around 80 percent of Nitrogen, 25 percent of Phosphorus, 50 percent of Sulphur and nearly 20 percent of Potassium and thus, crop residue burning causes loss of these nutrients. The burning of the one tonne of residue leads to loss of 5.5 Kg Nitrogen, 2.3 Kg phosphorus, 25 Kg potassium and 1.2 Kg sulphur, along with the nutrient loss, burning also hampers organic carbon in soil as well as increase in greenhouse gas emission. It also leads to climate change. In agriculture, loss of nutrients from soil depends on the crop which is burned. Sugarcane trash is responsible for maximum nutrient loss followed by rice and wheat straw.

Introduction

Agriculture ranks as the second-largest contributor to greenhouse gas emissions, accounting for 20% of the total, following the energy sector. Converting the agricultural waste from trash to treasure is very challenging issue for the researchers/scientists. The practice of indiscriminate dumping and burning crop residue poses serious human and environmental health risks and threatens food and energy security (Singh and Chauhan, 2022). Addressing crop residue burning requires strong technological interventions and policy support due to factors like labour scarcity, lack of appropriate technology and most important the short window of 15-20 days between harvesting and the next crop sowing. On-field burning not only degrades the environment but also leads to the loss of valuable nutrient content in crop residue. Agri-waste burning constitutes approximately 25% of total agri-biomass production globally, underscoring the urgent need to find economically viable alternatives to mitigate this issue and curb outdoor degradation. The scientific community is increasingly focusing on valorizing lignocellulosic-based agricultural waste for biofuel generation and industrial enzyme production. Given the pressing concerns regarding energy use and environmental degradation, there is a growing imperative to transition from non-renewable to renewable energy sources. Renewable energy has the potential to satisfy a significant portion of global energy demand by 2050. Leveraging lignocellulosic waste from agricultural systems is crucial for renewable energy generation and environmental sustainability. Agri-waste, composed mainly of cellulose, hemicellulose, and lignin, boasts high energy conversion efficiency compared to fossil fuel-based technologies. Effective agri-waste management techniques can address energy security and greenhouse gas emissions issues (Varjani et al., 2022). The sheer volume of agricultural residue globally, equivalent to around 50 billion tons of oil, highlights the substantial potential of agricultural biomass for energy production. Similarly, composting agri-waste offers an eco-friendly alternative to energy-intensive inorganic fertilizers, promoting sustainable agricultural crop production. Retaining or incorporating agri-waste into soil can enhance soil fertility and crop productivity (Yadav et al., 2019). Moreover, due to its diverse biochemical composition, agri-waste holds promise for various industrial applications.

Introduction

The management of cereal waste has emerged as a critical issue in the quest for sustainable development, particularly given the central role that cereals play in the global food supply. Major cereal crops such as wheat, rice, maize, barley, and oats are foundational to diets around the world, providing essential nutrients and serving as key economic commodities. However, the inefficiencies and losses that occur at various stages of cereal production, from pre-harvest to consumption, represent a significant challenge. These losses not only contribute to food insecurity but also pose severe environmental concerns, including unnecessary greenhouse gas emissions and the squandering of valuable agricultural resources like water and energy. Addressing cereal waste is thus paramount for ensuring both food security and environmental sustainability.

The scope of cereal waste is broad and multifaceted, occurring at several critical junctures within the food supply chain. Pre-harvest losses are often due to pests, diseases, and unpredictable weather conditions that can devastate crops before they are even harvested. Post-harvest losses arise from inadequate storage facilities, improper handling, and inefficient transportation systems, which can lead to significant quantities of cereals being spoiled or lost. During processing, the use of outdated technologies and practices can result in considerable wastage. At the distribution level, logistical inefficiencies further compound the problem, while at the consumption stage, improper storage, over-purchasing, and food spoilage contribute significantly to waste. Each of these stages presents unique challenges and opportunities for reducing waste.

 

Introduction

Agriculture is one of the prime economic sectors which generate enormous amount of waste from numerous agricultural operations and has a major share in the overall economy of India.The world’s human population has increased four times in the last century unaccompanied. This population boom,in part a result of improved agricultural and industrial techniques, places continuous pressure on food production in order to feed the growing numbers. Explosion of human population has directed the need in the rise of agricultural production which eventually hassled to the generation of huge amount of agricultural waste. India is an annual gross crop residue producer of which wheat and paddy residues. Agricultural wastes are as the residues from the growing and processing of raw agricultural products such as fruits, vegetables (vegetables and fruit peels) meat, poultry, dairy products and crops (Cereals crops- rice, wheat, maize, pulses, millets, oilseedsand sugarcane contributed the highest crop residue), cereals husks, legume haulms, sugarcane bagasse. Highest in the state of Uttar Pradesh is a leading state of India for residue generation followed by Punjab Maharashtra and West Bengal. Cereal crops (paddy, wheat, maize, millets) contributes 70% residue. Disposal of paddy residue has turn out to be a huge problem in Indian states, resulting farmers prefer to burn the residues in-situ. Improper management practices like dumping of agricultural waste. Based on the global perspective of resource management, education and awareness in the field of waste management is in huge demand. Awareness programmes on these emerging issues could also improvise the regulation of agricultural waste management and lessen its possible health and environmental risks. Systematic utilization of agricultural waste also helps to improve environmental conditions by reducing pollution caused by disposal of huge agricultural waste. Agricultural waste is composed of organic wastes as animal excreta in the form of slurries and farmyard manures, spent mushroom compost.