eChapters

INTRODUCTION
Maintenance scheduling involves planning, organizing, and coordinating the tasks required to ensure equipment operates efficiently. A maintenance schedule is a comprehensive plan that specifies the timing and frequency of maintenance activities for machinery, equipment, and facilities within an organization. Its primary goal is to maintain optimal functionality, minimize downtime, and extend the lifespan of assets. The schedule typically includes an inventory of assets, details of maintenance tasks, their frequency, assigned personnel, required documentation, and identification of resources necessary to complete the maintenance work.
IMPORTANCE OF MAINTENANCE SCHEDULING
A well-structured maintenance schedule is essential for ensuring efficient and reliable operations within the manufacturing industry. By using an effective maintenance scheduling system like MaintWiz CMMS, you can enjoy the following advantages:
• Improved Equipment Performance: Performing routine maintenance enhances the reliability of your equipment, which in turn leads to better overall performance and increased productivity.
• Enhanced Safety: Scheduled maintenance includes regular safety inspections, helping to reduce the likelihood of accidents and contributing to a safer working environment.
• Minimized Downtime: Regularly scheduled maintenance reduces the likelihood of unexpected breakdowns and helps to cut down on unplanned downtime, ensuring the smooth functioning of equipment.
• Optimized Resource Utilization: Scheduling maintenance tasks properly allows for the best allocation of resources, ensuring that tasks are completed based on priority and availability, which boosts operational efficiency.

Origin & Distribution of Cattle

All types of domestic cattle belongs to class Mammalia, subclass Ungulata, order Artiodactyla, suborder Ruminantia, family Bovidae, subfamily Bovinae, tribe Bovini, which includes genus Bos. The aurochs or urus (Bos primigenius) is the probable ancestor of most of the present day Bos taurus and Bos indicus cattle. The humped Bos primigenius namadicus were the forerunner of today’s zebu cattle while humpless Bos primigenius primigenius gave rise to modern European cattle. Both category of cattle possess the same number of chromosomes (2N=60). Other related animals in the Bovini tribe include the gaur cattle (Bos gaurus), banteng (Bos banteng), kouprey (Bos sauveli) of South and East Asia, bison (Bison bison) of North America, European bison (Bison bonasus) and the yak (Poephagus mutus) of central Asia. According to Payne & Hodges (1997), domestication of aurochs (Bos primigenius ) took place first probably on the lower mountain slopes in the region of western Asia known as the ‘fertile crescent’. They suggested that the first domestic cattle were humpless (Bos taurus) Hamitic Longhorn type and were domesticated around possibly 9000 BP (years before the present i.e. 1950). About 2000 years later the humpless (Bos taurus) shorthorn type evolved or was developed in the region and these two types of humpless cattle are believed to the ancestor of Bos taurus. Humped (Bos indicus) zebu cattle were domesticated in the northwest arid areas of the Fertile Crescent around c. 5000 BP.

Dairy byproducts constitute an important segment of dairy industry. An important criterion in the economic choice of any process is the efficient utilization of byproduct that is produced along with the main product. The main byproducts of dairy industry are skim milk, buttermilk, whey and ghee residue. Each and every component of milk must be judiciously processed into edible form not only to improve the profitability of dairy industry but also to make best use of the unique nutritional value of the precious milk constituents on one hand, and to prevent environmental pollution, on the other. However, dairy plants, particularly in India, are usually confronted with the problem of byproduct utilization, especially that of whey and ghee residue in economical manner.

Skim milk is a byproduct obtained during the manufacture of cream. It is rich in solids-not-fat content and has high nutritional value. In dairy plants, it is mostly utilized either in standardization for the manufacture of main dairy products or preserved by removing moisture in milk powder form. The skim milk when utilized in either of these two forms or consumed as liquid, is not considered a byproduct. It is regarded as a byproduct only when it is either not economically utilized or utilized for derived byproducts, like casein and other related products, co-precipitates, protein hydrolyzates, etc.

Buttermilk is the byproduct obtained during butter manufacture. Sweet cream buttermilk resembles skim milk in gross chemical composition and is usually admixed with bulk of skim milk for further spray drying or even product manufacture in dairy plants. Desi buttermilk, on the other hand, has long been an important domestic beverage in India. It has high nutritive and therapeutic value. It also finds its way in the preparation of a host of culinary dishes such as kadhi, dhokla and idili. Also a number of federations and private plants sell salted and spiced buttermilk in 200 ml pouches.

Dairy Industry in India
Milk holds a deeply symbolic place in Indian culture, far beyond its role as a mere food item. It is intricately woven into the cultural and historical identity of the nation, with the dairy industry serving as a cornerstone of India’s economy. The sector is a driving force behind rural development providing a crucial pathway to prosperity for millions of small-scale farmers. It plays a significant role in alleviating poverty and generating employment opportunities. The dairy sector by empowering farmers, developing modern infrastructure embracing technological advancements, and promoting sustainable practices, fostering widespread socio-economic progress in rural India. In the 1920s, modern milk processing and marketing technologies were introduced in India. The National Dairy Development Board (NDDB; founded in 1965), implemented Operation Flood (1970-96) to modernize the dairy sector through cooperatives. This led to the rise of dairy cooperatives countering the exploitation by private milk plants. The cooperatives followed the Anand model, a three-tier structure comprising village-level societies (producers), district-level unions (milk collection and processing), and state-level federations (marketing). Originating in Anand, Gujarat in 1946, the model was later adopted nationwide. The progress termed as White Revolution transformed India from a nation struggling with milk shortages into the world’s largest producer and consumer of milk. By 1997, India became the global leader in milk production with a volume of 70.9 million tons, surpassing the United States (FAO data rearranged by Our World in Data). Further in the year 2014, for the first time, it beat the entire European Union. Since 1950, India’s milk production has seen tremendous growth, rising from 17 million tonnes in 1950-51 to 230.58 million tonnes in 2022-23. The per capita availability of milk increased from 124 g/day in 1950-51 (PIB 2022) to 459 g/day in India during.

Introduction
Among flowers, the rose enjoys the greatest adorable position over the world. In real sense, rose almost hypnotised the man most by its colour and fragrance. Apart from its aesthetic value, the roses are of high economic benefits, and religious significant. Besides, it finds it place in art and culture from times immemorial. The rose thus merits pride of place as the ‘Queen of flowers’.
Origin
The damask rose is originated in ancient Persia (now Iran), and later on spreading over Europe and Northern Africa. Further, widespread farming of R. damascena for rose oil manufacturing began in Turkey and Bulgaria. Many countries of the world grow scented roses for extraction of the volatile essential oil. The genus Rosa grows wild in many parts of the temperate and sub-temperate regions. The major growing countries are Bulgaria, France, Italy, Turkey, Russia, China and India. Bulgaria is the principle European country for supply of rose oil to the world market. Among different species, Rosa centifolia Linn. is largely grown in France and Italy, whereas Rosa
damascena Mill. in Turkey, Bulgaria, Russia and Persia. Rosa damascena Mill. was first introduced successfully in middle east countries during 1680, resulting in the establishment of entire area as ‘Valley of flowers’. It belongs to family rosaceae.

Agriculture has long been at the mercy of the weather, and in today’s datadriven world, harnessing meteorological information is more critical than ever. Weather intelligence—the process of collecting, analyzing, and disseminating weather data—supports decision-making in diverse fields such as aviation, transportation, emergency management, and especially agriculture (Fig 3.1). This chapter explores how advanced data acquisition methods and robust database management techniques underpin agricultural weather intelligence. It covers the variety of data sources available, details strategies for integrating and ensuring the quality of vast datasets, and examines the forecasting techniques that transform raw data into actionable insights for precision forecasting and decision making in food production. Fig. 3.1. Weather Data Acquisition System In this chapter, we not only discuss the technological means by which weather data is collected—from satellites, ground sensors, and IoT networks—but also dive into the structure of weather databases. These repositories house surface weather observations, radar and satellite imagery, upper air measurements, and outputs from weather models. Further, we outline the different forecast types and forecasting methodologies that are essential to interpreting this data, ranging from nowcasting for immediate weather conditions to seasonal forecasting that supports long-term planning. Finally, the role of big data analytics in supporting precision forecasting and optimizing decision-making in agricultural production is examined, highlighting the integration of advanced techniques like numerical

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

Now we have to learn how to enter numbers and text, how to edit any erroneous entry and then how to do simple calculations. Open empty excel spread sheet by clicking on the start menu in the window tool bars, click on Programs and then click on the Excel menu item.

6.1 Entering Numbers

Numeric entries are constant, can be integers, decimal fractions, integer fractions and scientific nations which can be entered by typing the number in the active cell [cell which can be made active by clicking the mouse on the cell.

6.1 Introduction
Our journey through remote sensing has, thus far, focused on the remarkable capabilities of various platforms and sensors to observe Earth’s atmosphere and surface. From the localized, high-resolution insights provided by groundbased instruments like Wind Profilers and SODARs, to the expansive, global coverage offered by an array of satellite-borne imagers, sounders, radars, and lidars, we’ve seen how a continuous stream of invaluable meteorological data is acquired.
However, the mere act of collecting this data, no matter how sophisticated the technology or comprehensive the observations, represents only the initial phase of its lifecycle. The true power and utility of remote sensing data are unlocked only when it can be efficiently organized, securely preserved, and readily available to the diverse community of meteorologists, climate scientists, researchers, and operational forecasters who rely on it daily. This necessitates a profound understanding of the principles and practices governing data formats, data storage, and data access.
The sheer volume, velocity, and variety of remote sensing data pose significant challenges. Petabytes of information are generated annually, requiring robust infrastructure and standardized methodologies to ensure its integrity, interoperability, and long-term usability. Without effective strategies for managing this deluge of information, the potential of remote sensing to enhance weather prediction, advance climate science, and inform critical decision-making would remain largely untapped. 

Introduction 
The preceding chapters have firmly established the conceptual foundations of the systems approach and introduced the diverse world of crop models, highlighting their intricate mechanisms and their transformative potential for agroadvisory. However, even the most elegantly formulated and sophisticated crop model is ultimately a framework; its predictive power and reliability are inherently tethered to the quality and availability of the information it receives. As the adage in data science goes, Garbage in, garbage out– a truth that resonates profoundly within the realm of agricultural system simulation. The chapter shifts focus from the theoretical constructs of models to the vital practical considerations of data requirements. This chapter will delve into the indispensable role that accurate, comprehensive, and well-structured data plays in every stage of crop modeling, from initial parameterization and rigorous calibration to robust validation and reliable application. Without the right data, even a theoretically perfect model cannot accurately mimic the complex dynamics of the soil-plant-atmosphere continuum or provide trustworthy insights for agricultural decision-making. This chapter will systematically unpack the various categories of data essential for crop modeling. It will meticulously detail the specific characteristics and sources of weather data, which serve as the primary atmospheric drivers. It will then explore the crucial properties and measurement techniques for soil data, underpinning the understanding of water and nutrient availability. The discussion will extend to crop genetic and physiological data, which define the inherent capabilities and responses of specific plant varieties. Furthermore, the chapter will cover management data, illustrating how human interventions are integrated into the modeling framework. Beyond merely listing data types, this section will critically examine the challenges associated with data acquisition, including issues of quality, resolution, consistency, and spatial representativeness.

Abstract

Biological databases that are composed of a large collection of digital nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer, which is made available to the research community worldwide using web servers. The vast amount of information generated in molecular biology is analyzed, curated, tabulated, and arranged in databases. There are different types of biological databases, based on their contents. Besides, the database management system, a program to manage the databases, can be of two types: distributed DBMS and relational DBMS. This chapter briefly narrates the importance and purpose of biological databases.

• The date is a good source of crystalline sugar.
• It is a dioecious and monocotyledonous fruit crop.
• Dates can be grown in sandy loamy soils.
• Date palm needs 3300 units of heat. (A unit of heat means a degree above a daily mean of 18°C between flowering, fruit development and ripening period.

Cultivars

• Halawi of makes tolerant of rain. It is 28.5 to 42.2 per cent Dakastage Dang Zoo, Hayni Khadrawy, Shamran (Sayen) are tolerant to high humidity. Medjoal, Bastree, Zahidi – is rain and high humidity tolerant.

Introduction
Davana (Artemisia pallens Wall.) is one of the important aromatic herb found abundantly in humid habitats in the plains all over India. It is popularly known as sage brush, worm wood and mug word. The davana (Artemisia pallens) is a small to medium height; annual and herbaceous aromatic plant. This herb is erect, and grow up to 60 cm height. Its leaves are highly divided and bears small size yellow coloured flower. Its leaves and stem found protected with greyish-white tomentum. The leaves are lobed, alternate, and petiolate.
Origin & Distribution
It is native to southern India, where it is traditionally grown in Karnataka, Maharashtra, Kerala, T.N., and A.P, growing widely in tropical to subtropical
plains as Rabi season crop. It also found growing in wild form in the Himalayan region in the Simla, Kashmir valley, and Nainital hills. It is widely used by Ayurvedic healers to balance the three energies. It is an erect growing
plant up to 60 cm height. It does not grow wild, but grown as a garden plant in South India. It is a crop of irrigated conditions; and do well when planted in November-December, yielding significantly higher oil yield in comparison to crop planting in July-August. The crop should be harvested 2-3 inches above ground level during bright sunny days at 50 percent flowering stage to have high oil content and oil yield.

The content presented in this chapter is exploratory and reflects emerging trends in integrating weather intelligence with decision support systems. The term “weather intelligence” is introduced as a framework, with ongoing research expected to further refine and expand upon these ideas as technologies evolve. In an era where climate variability and extreme weather events increasingly threaten global food systems, the integration of advanced decision support systems (DSS) with what we are now calling weather intelligence has emerged as a transformative paradigm. This chapter explores how food production— encompassing agriculture, fisheries, and animal husbandry—can harness integrated weather intelligence to inform real-time crisis management, strategic planning, and risk mitigation. The discussion is exploratory in many respects, as the concept of weather intelligence represents an innovative framework that challenges traditional forecasting methods and decision support tools (Fig. 9.1). Drawing on multidisciplinary research and practical case studies, we examine the building blocks of decision support systems tailored to food production. We then discuss the integration of weather intelligence into crisis response and strategic planning, highlighting both established risk mitigation approaches and emerging tools designed to anticipate and respond to extreme weather events. By contextualizing these developments within the broader scope of sustainable food production, this chapter offers an in-depth analysis and a forward-looking vision for decision support and response management in a rapidly changing climate.

Given this immense significance, the ability to accurately predict the behavior of the monsoon has always been a paramount concern. The science of monsoon forecasting has evolved significantly over time, transitioning from rudimentary observational methods to sophisticated numerical weather prediction models powered by advanced computing capabilities. This chapter delves into the intricate world of monsoon forecasting, exploring its historical roots, the scientific principles that underpin it, the diverse methodologies employed, and the continuous efforts being made to enhance its accuracy and utility. We will embark on a detailed journey through the complexities of the Indian monsoon system, examining the atmospheric and oceanic factors that drive its variability. From understanding the large-scale circulation patterns to deciphering the influence of regional phenomena like the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), we will unravel the key drivers that forecasters consider. The chapter will then explore the spectrum of forecasting approaches, ranging from statistical models based on historical data to dynamic models that simulate the intricate physical processes of the atmosphere and ocean. We will also discuss the role of satellite technology, ground-based observations, and high-performance computing in generating increasingly detailed and reliable forecasts. Furthermore, this chapter will critically assess the current state of monsoon forecasting, acknowledging both its advancements and its inherent limitations. We will address the challenges in achieving the desired levels of accuracy, particularly at regional and local scales, which are most relevant to farmers. Understanding these limitations is crucial for setting realistic expectations and guiding future research directions. Finally, and perhaps most importantly, we will examine the crucial link between monsoon forecasting and the needs of the agricultural sector. We will revisit the expectations of farmers regarding forecast information – their need for accuracy, timeliness, localization, specificity, and actionable advice. By understanding the gap between current forecasting capabilities and these requirements, we can better appreciate the ongoing efforts to bridge this divide through improved forecasting techniques and effective dissemination strategies. This chapter aims to provide a

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

Ice cream defects can result from formulation imbalance, poor ingredient quality, processing deviations, inadequate freezing or hardening, packaging problems, storage conditions, and cold-chain failures. This chapter provides a systematic framework for understanding, classifying, diagnosing, and correcting defects affecting flavour, body, texture, melting, colour, appearance, packaging, and storage stability. It emphasizes that a visible defect in the finished product may originate much earlier in the production or distribution chain. Examples include iciness caused by slow freezing or temperature abuse, weak body associated with formulation or overrun, and flavour problems originating from raw materials or microbial activity. Defect analysis therefore becomes an important tool for troubleshooting, quality improvement, training, and preventive action.

Introduction

Resistance to disease in a host plants is a condition in which the plant conquer the pathogen’s attack and thus suffers small or no injury. Resistant hosts prevent or slow the development and reproduction of the majority of pathogen propagules that they come into contact with. Different plants defend themselves in different ways. Each kind of plant, probably, employs different defense mechanism against each of the pathogens that attack it. The defense barriers erected by plants are a co-ordinated system of molecular, cellular and tissue-based responses to pathogen attack.

Philosophy of defence in plants

Plants cannot move and must resist or tolerate co-located pathogens.

Plants have evolved very powerful preformed defense systems in the form of structural and toxic barriers.

Plants cannot keep intruders out, they can only limit (contain) them.

Plants can tolerate more nutrient runoff. They often sacrifice a piece of tissue to contain pathogens.

1.1. General Discussion of Milk

“The cow is the foster mother of the human race. From the days of the ancient Hindoo to this time have the thoughts of men turned to this kindly and beneficent creature as one of the chief sustaining forces of human life”.

William Dempster Hoard (1836-1918)
Former Governor, State of Wisconsin, USA (1889-1891)
Founder of Hoard’s Dairyman (1885)

The oldest written records of the human race are in Sanskrit and are preserved in India. At the time these records began, about 6,000 years ago, milk had already become an important article of food. In fact, so important was the cow to these early people of South and Central Asia that wealth was measured in numbers of cattle, and the cow was in time made a sacred animal and is still so considered in India.

According to the best authorities, the domestication of cattle must have occurred somewhere between 6,000 and 10,000 years ago. The cow was worshipped in Babylonia, and in Egypt about 20,000 B.C. Over fifty references to cows and milk are found in Old Testament and the Promised Land was described as “a land flowing with milk and honey”.

Milk is the only food of the young mammal during the first period of its life. The substances in milk provide more energy and the building materials necessary for growth. Milk also contains antibodies which protect the young mammal against infection. A calf needs about 1000 litres of milk for growth, and that is the quantity which the primitive cow produces for each calf. There has been an enormous changes since man took the cow into his service. Selective breeding has resulted in dairy cows which yield more than 5,000 litres of milk per calf, i.e. five times as much as the primitive cow. Some cows can yield 10,000 litres or more.