eChapters

Introduction
India is the seventh largest having 329.73 million ha of geographical area and second most populous country having more than a billion population in the world. There is no other substitute other than to use every square inch of land and every drop of water effectively and efficiently. Though there was markedly increase in annual food grain production from 51 (1950-51) million tones t0 206 million tones (2000-01), still the death on hunger is visualized.
The demand for food is increasing with ever-increasing population growth rate. So there is no option other than to increase food production. There is very little or no scope of horizontal expansion due to land limitation, the question of vertical expansion is the only way to fit with the situation. Genetically improved crop variety, hybrids, mechanized agriculture, higher rate of fertilizers and chemicals application may give one or two more green revolution, but the question lies how far it can sustain greenness?
Adoption of improved crop cultivars, cropping system, increasing cropping intensities, precision farming can hold good to get out of this situation where whole system is to be considered.
The cropping intensities in the states like Assam, Bihar, MP, Orissa, UP and West Bengal are 137.7, 137.8, 125.6, 154.6, 148.6 and 159.6 percent, respectively (FAI,1994-95). This indicated that a vast majority of lands remain fallow during dry season due to lack of proper technologies, which are under rainfed conditions, neglected. Except West Bengal, more than half of the arable land remains fallow during rainless period. Increasing cropping intensities in rainfed uplands is not easily achievable but there is great potential of intensification of cropping in rainfed lowland by utilizing natural resources and conserved residual moisture. Pulses like black gram, green gram, lentil, lathyrus, chick pea and oilseeds like linseed, groundnut, rapeseed could not only increase the cropping intensities but also come forward to increase national productivity.

Q. 1 LSU dryer was developed at
(A) London State University       (B) Ludhiana State University
(C) Lucknow State University     (D) Lusiana State University

Introduction

Durum or macaroni wheat (T. turgidum var. durum) is an important crop used chiefly for the production of pasta in Western Europe and North America, and two layered bread, single layered bread, burghul, couscous, pasta, frek and feed in other parts of the world (Table 1). In North Africa and the Middle East, durum is also used for producing flat breads. On a global basis only a small amount of durum wheat is used for baking of pan-style and hearth-style bread. Also, durum wheat for baking is often used in blends with common wheat. In India, durum occupies approximately 2.0 mha area and production is estimated to be around 2.5-3.0 million tones annually amounting to 3-4% of the total wheat production. The consumption of pasta is increasing at the rate of around 5% annually and currently it is estimated to be around 10 Lakh tones. The demand for pasta products may increase further due to increasing population and improving standard of living.

Early identification of crop diseases enables timely management interventions and can reduce losses in agricultural production. This chapter investigates the use of artificial intelligence and sensor data for detecting crop diseases at an early stage. It introduces the fundamentals of crop disease detection and reviews AI techniques applicable to agricultural monitoring. Sensor technologies provide continuous information about crop and environmental conditions, while AI models analyze these data to identify disease-related patterns. The chapter presents an AI- and sensor-based framework, including implementation and experimental procedures, followed by performance analysis. Challenges and limitations are examined, together with future research directions aimed at improving accuracy, reliability and real-time disease monitoring.

Plant diseases significantly reduce crop productivity, quality, and economic returns, making early and accurate detection essential for effective crop protection. This chapter examines the application of machine learning and computer vision for identifying diseases at an early stage. It discusses agricultural image acquisition using RGB, hyperspectral, and sensor-based technologies, followed by image preprocessing, segmentation, feature extraction, and classification. Machine learning algorithms such as support vector machines, random forests, and convolutional neural networks are considered for disease recognition. The chapter also explores the integration of AI with IoT and mobile technologies and presents practical applications while addressing challenges involving datasets, environmental variability, model generalization, and computational requirements.

An earthen dam is an embankment constructed primarily from compacted soil to impound runoff and create a reservoir. Such dams support irrigation, water supply, groundwater recharge, hydropower generation, and flood moderation. Their successful performance depends on suitable site conditions, proper materials, adequate compaction, controlled seepage, stable slopes, and safe disposal of surplus water. This chapter introduces the advantages and limitations of earthen dams, construction materials, types of embankment sections, and their principal components. Particular attention is given to cut-offs, impervious cores, casing or shells, foundations, filters, drainage systems, spillways, outlets, slope protection, and common causes of deterioration or structural failure.

Since many decades environmentalists have warned of perils of chemicalised sports grounds, golf courses and cricket fields. They warn that our health is inextricably linked to the widespread use of toxic chemicals and also complain about the non-availability of safe alternatives.
Eco-sustainable sports fields must use the methods : 
    ·    That do not pollute
    ·    That are environment-friendly and effective
    ·    That economize on the use of water and energy

Introduction

The deterioration of interior air quality brought on by dangerous chemicals and other factors is known as indoor air pollution, which can be up to 10 times worse than outside air pollution. This is due to the fact that confined spaces foster the buildup of potential pollutants more than do open spaces. In underdeveloped countries, statistics show that indoor air pollution has a far worse effect on health than outside air pollution. Indoor air pollution can be defined as ‘ the totality of attributes of indoor air that affects a person’s health and well-being’ ( Singh & Jamal, 2012). Indoor air pollution from solid fuels was to blame for 3.5 million fatalities, 16% of particulate matter pollution, and 4.5% of global daily-adjusted life years in 2010. (DALY).

Life cycle cost analysis

Economics of solar energy devices can be calculated through life cycle cost (LCC) analysis. Total life cycle cost of a device is comprised of capital cost, maintenance cost, replacement costs for damagaed components and operational cost. Before adding these above costs, all future costs (C) are converted to present worth considering the relative rate of inflation (i) and discount rate (d).

Where, PW is the present worth of any future cost, i is the relative rate of inflation and d is the discount rate per year and n is the time period in years. Relative rate of inflation accounts for the escalted increase or decrease in prices of a commodity in comparison to general inflation rate. For any commodity, if the price escalation is expected as per the general inflation rate then relative rate of inflation is considered zero. In general case, relative rate of inflation is considered zero. Discount rate accounts the real value of money in future and in most of the economies of the world it is about 8-12%, and therefore 10% is considered in most analyis. The discount rate also refers to the interest rate used in discounted cash flow (DCF) analysis to determine the present value of future cash flows. The discount rate in DCF analysis takes into account not just the time value of money, but also the risk or uncertainty of future cash flows; the greater the uncertainty of future cash flows, the higher the discount rate. A discount rate of 10 % per year would mean that in real terms it makes no difference to a farmer whether he has ? 100 now or ? 110 in one year’s time. Conversely, a cost of ? 110 one year from now has a present worth of ? 100. For a future single cost in n^th year, the present worth of that cost is calculated using Eq (1). However, for future multiple payments, costs are to be converted to present worth for each year and then needs to be cumulated. For calculation of annualized life cycle cost (ALCC) of solar devices, annuity factor (AF) needs to be calculated for a period of life cycle of the device as follows:

6.1 Introduction

Urbanization is now a global phenomenon, but in developing countries the implications are more pronounced. Natural growth of population, reclassifications of habitation and migration trends are important in urban population in India. The population of India, which accounts for 27 per cent of the total population, was 285 million according to 2001 census. International experience shows that the pace of urban development accelerates when a country’s urban population crosses close to 25 percent of the total population (like in India).(Kumar and Chakrabarti, 2010). 

In order to assess the economic value of biochar, various factors need to be taken into account, including production expenses, market demand, potential income sources, and cost reductions. The economic worth of biochar can be determined by considering these factors.

8.1 Production costs

The cost of producing biochar can differ based on various factors such as the type of material used, the scale of production, the method of pyrolysis used, and the particular operational conditions.

i. Feedstock costs

• The cost of acquiring or producing biomass feedstock includes purchasing agricultural residues, forestry waste, or other biomass sources. Additional costs such as land, seeds, fertilizers, and harvesting should be considered using dedicated energy crops.

Introduction 
Biofuels have emerged as a key solution in the energy transition toward sustainable energy, reducing dependence on fossil fuels and cutting greenhouse gas (GHG) emissions. The demand for biofuels has been steadily increasing due to environmental concerns, government promotional policies, and technological advancements. The factors driving biofuel demand, current market trends, and future projections are the global demand for biofuels is experiencing rapid growth due to government policies, energy security concerns, and advancements in sustainable biofuel production. As more countries implement biofuel mandates and invest in next-generation biofuels, demand is expected to rise significantly in the coming years, positioning biofuels as a critical component of the global energy transition. Governments worldwide have implemented various policies to promote biofuel production and consumption. These policies aim to reduce greenhouse gas emissions, enhance energy security, and support rural economies. Ethanol remains the most widely used biofuel, with major producers like the U.S., Brazil, and China increasing production to meet rising demand. The U.S. and Brazil collectively account for over 80% of global ethanol production. Bioethanol production originated in Brazil in the early 20th century, where sugarcane was used to produce ethanol as a fuel additive. However, the United States also played a significant role in the large-scale commercialization of bioethanol, especially from corn, starting in the 1970s during the oil crisis. Brazil remains a global leader in sugarcane-based ethanol, while the U.S. leads in corn-based ethanol production. Biodiesel demand is growing, particularly in the European Union, Indonesia, and Argentina. Indonesia’s B30 program (30% biodiesel blending) has significantly boosted demand. Between 2005 and 2015, the biodiesel production experienced a sevenfold increase and is projected to grow by an additional 35% by 2025 (OECD/ FAO, 2014). Biofuel policies have played a crucial role in shaping the growth of biofuel production worldwide. Since their inception, these policies have provided financial incentives, blending mandates, and research support, which have driven large-scale production. The impact of these policies varies across major biofuelproducing nations, including the United States, Brazil, the European Union, China, and India (Table 1). The U.S. biofuel industry expanded significantly due to the

11.1 Introduction

Economic analysis is the most important parameter for determining the optimum size of farm pond. It is, no doubt that a big size farm pond can meet the irrigation demand of the crops grown in the command areas in addition to meeting the other demand in the command areas. But a big size farm pond is an oversize structure which necessitates large investment to be incurred which may not be economically viable. On the other hand, a small size structure may not be able to meet the crop water demand and also other demands in the command areas and so is called as under-designed structure. It has been observed that as the size of the farm pond gradually increases from a certain minimum size, the total investment cost increases. At the same time, the benefits from the increasing storage capacity of the farm pond also get increased. In such cases, from a purely business point of view, the most economical capacity is that which provides the highest rate of return on investment. As the size of the farm pond increases, the proportion of the benefit/profit to the cost also increases from certain minimum size, attains maximum value at a particular size and then declines as the size further increases. This particular size of the farm pond at which the net profit is maximum, is called as the optimum size. It is to be noted that at the optimum size, the other economic indicators like the benefit-cost ratio and internal rate of return are also high. The variation of net profit with size of the farm pond is a concave curve. The size of the farm pond corresponding to the peak of the curve which represents the highest net profit is the optimum size of the pond.

The principle of obtaining the optimum size of farm pond is to first assume a certain minimum size say five percent of the farm area and conduct the economic analysis by studying the costs and net profits of the assumed size. Then the size is gradually increased and the economic analysis is conducted for each increased size. Then a curve relating the size of the farm pond and net profit is drawn. Such a curve is generally concave shaped. The size of the structure corresponding to the maximum net profit gives the optimum size. It is to be noted that any size chosen more or less than the optimum size would give less net profit and benefit-cost ratio than that of the optimum size.

Q. 1 Annual depreciation of a machine is included in
(A) Fixed cost         (B) Variable cost
(C) Hiring cost         (D) None of the above

Education has continuously evolved in response to technological, economic, and social change. Education 5.0 represents a further transformation toward human-centered, personalized, inclusive, creative, sustainable, and collaborative learning environments supported by intelligent technologies. This chapter examines the evolution from Education 1.0 through Education 4.0 and explains how Generative Intelligence contributes to the emerging Education 5.0 paradigm. Earlier models progressed from teacher-centered knowledge transmission to standardized education, digitally connected learning, and technology-enabled competency development. Education 5.0 extends this evolution by emphasizing meaningful collaboration between humans and intelligent systems rather than simple technology adoption. Generative AI can support personalized content, automated assessment, intelligent tutoring, adaptive learning pathways, multimodal resources, and immersive educational experiences. The chapter also considers the role of AI, IoT, AR/VR, robotics, big data, cloud technologies, and other digital systems in shaping future learning ecosystems. Importantly, Education 5.0 must address data privacy, algorithmic fairness, academic integrity, digital inequality, sustainability, and human control. The chapter presents Education 5.0 as a human-centered transformation in which technology supports creativity, inclusivity, lifelong learning, social responsibility, and sustainable development.

The weather plays a very significant role in crop production. The agricultural production depends upon four factors i.e., soil, genetic material, management practices and weather. Of these four factors, the farmers have least control on weather and have to evolve management strategies to cope up with the weather conditions. The crops can be grown only when the seasonal weather conditions are conducive to crop production. The different weather factors on crop growth and development are as follows:
13.1. Temperature
For each species of plants, there are upper and lower limits of temperature beyond which the growth is negligible. The optimum temperatures at which growth is maximum may vary from crop to crop but will be within the range of 15 to 30°C. Temperatures beyond 45°C cause wilting of plants. There are also optimum temperature requirements during different stages of crop growth.
13.1.1. Warm Season Crops: The Warm season crops are rice, sorghum, maize,
pearl millet, groundnut, pigeon pea, cowpea etc. These crops are also called tropical crops. These crops can be grown during monsoon season and in early summer season. The optimum temperatures for these crops fall in the range of 15°C to 38°C.
13.1.2. Cool Season Crops: The crops which grow best under cool weather
conditions are called cool season crops and are generally grown during winter season. The important cool season crops are wheat, mustard, Chickpea, barley, potato, Safflower. These crops are also called temperate crops. The optimum temperature for better growth of these plants range from 10°C to 30°C.
13.2. Effect of Temperature on Growth and Development
The crop development is described in terms of its various stages of growth including germination of seeds, emergence of seedlings, tillering, flower initiation, grain formation, physiological maturity and harvest maturity etc., before it completes its life cycle. The various stages of growth, the crop passes through are called phenophases. Phenology is the study of timing of the phenophases. The temperature and day length (photo period) will have considerable influence on phenology of the crops. The temperature from which crop development begins is called base temperature. The base temperature is considered as 8°C for the crops grown during kharif season and 5°C for the crops grown during winter

Abstract

Garden peas of two cultivars (Pb-87 and Pb-88) were procured. Pods were shelled manually, grains were blanched and dehydrated. Other lot of peas of both the varieties was allowed to mature on the plant till the plant foliage dried up, harvested, pods were dried in shade for few days, shelled manually and grains were further dried in shade. Grits were prepared by grinding rice and pea (dhal) in a laboratory grinder to pass through 20 mesh sieve. Blends were prepared by mixing rice and pea grits in the ratio of 100:0, 85:15 and 70:30. The blends were subjected to extrusion cooking using Brabender single screw extruder. The extruder die pressure, specific energy consumption (SEC), expansion ratio, density, water absorption index (WAI), water solubility index (WSI) and organoleptic characteristics were studied. Addition of pea grits resulted in a decrease in die pressure in all the blends. Addition of pea grits showed a significant effect on SEC. A decrease in expansion with increase in the level of pea grits in blend was observed. The extrudates having lower expansion showed higher density values and vice versa. Effect of replacement of rice grits with pea grits on WAI was found to be significant. Level of pea grits significantly affected the WSI. Incorporation of pea grits in the rice grits significantly affected the sensory characteristics. Replacement of rice grits with mechanically dried pea grits resulted in deterioration of acceptability score, but addition of field dried pea grits improved the same.

Abstract

Paddy seeds of variety PR - 116 were obtained from the PAU seed farm. These seeds were cleaned with commonly used seed processing equipment. Samples were divided into 2 parts. One part was kept as such for taking the observations of feed (control) and the remaining part was passed through specific gravity separator for its refinement. The various fractions obtained were recovery, middlings, and rejections. These graded fractions as well as the feed were packed in 5-kg cloth bags and stored under ambient conditions. Different quality parameters viz. moisture content, physical purity, thousand grain weight, percent germination, root/shoot length, vigour index and speed of germination were recorded at 30 days interval. The physical purity, thousand grain weight, percent germination, root length, shoot length and vigour index were found to be higher in recovery and minimum for rejections while moisture content was highest for rejection as compared to all other fractions. Germination, root/ shoot length, vigour index and speed of germination increased with the duration of storage while moisture content, thousand-grain weight and physical purity followed a reversed trend. Compared to feed, the recovery had higher quality attributes after 4 months of storage viz. moisture content decreased by 0.3%; physical purity increased by 0.67%; thousand grain weight increased by 2.97 gm/ thousand grain, germination increased by 8%; vigour index increased by 481.1 points; speed of germination increased by 3.2 seedlings per day. The grading resulted in improvement in overall quality of sunflower seed.

Objective: Equip participants with the skills to communicate clearly, confidently, and empathetically in verbal, non-verbal, and written forms to foster collaboration, reduce misunderstandings, and build stronger workplace relationships.
Why It Matters
Effective communication is the cornerstone of professional success. It ensures ideas are conveyed clearly, builds trust, and prevents conflicts. Poor communication can lead to misaligned expectations, decreased productivity, and strained relationships. For example, a vague email can cause project delays, or a misunderstood tone in a meeting can create tension. This module empowers participants to master communication in diverse workplace scenarios.
Learning Outcomes: By the end of this module, participants will:
1. Understand the components of effective communication (verbal, nonverbal, written).
2. Practice active listening and empathetic responses to enhance interpersonal connections.
3. Craft clear, professional written messages tailored to their audience.
4. Apply communication strategies in real-world scenarios through interactive exercises.

Rice farming under different subsystems of lowland ecology
1. Rainfed lowland with limited water at seeding and excess water at later growth stages: In rainfed lowland areas, the rice is direct seeded in dry soil in April-May before onset of monsoon (June) and seeds germinate with the showers received after sowing. Drought at the seedling stage kills seedlings and results in poor stand establishment. Furthermore, heavy rains in catchment areas of river basins result in water logging and flooding at late growth stages. The water hinders tillering and results in poor panicle number less than 200/m2 as against 350 /m2 of high yielding rice cultivars.
2. Rainfed lowlands with limited/excess water at transplanting and excess water at later stages: When on-set of the monsoon is delayed, transplanting delayed because the farmer has to wait for adequate rains for land preparation. Monsoon rain submerges the transplanted crop, resulting in poor stand. In some areas, the land gets inundated before transplanting and farmers generally transplant aged and tall seedlings to keep the plant parts above the water. Plant survival in deeper water depends on rise in water level and on the elongation ability and submergence tolerance of the plant. Varieties that elongate fast develop weak culms and may lodge when the water level recedes. In this situation tillering is poor and panicle per unit area are less.
3. Assured irrigated areas with excess water during crop growth due to poorly drained condition: Rice grown in the river deltaic areas of Krishna, Godavari and Indo-Gangetic rivers suffer from water stagnation even though some areas are having irrigation facilities. Heavy rains in the catchment cause flooding and stagnation in rice fields because of congested drainage or lack of drainage caused by topographically saucer-shaped land. These conditions develop during peak monsoon (July-September). The late planted crops, being at an early stage of growth, suffersfro water stagnation. In these areas, transplanting early in the season is necessary for plants to grow enough to escape submergence.