eChapters

The Sustainable Development Goals (SDGs) are a set of 17 interconnected goals established by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. These goals aim to address global challenges, including poverty, inequality, climate change, environmental degradation, peace, and justice (Fig. 14.1).
14.1. Sustainable Development Goals (SDGs)
The SDGs provide a universal framework to achieve a better and more sustainable future for all. Following is the list of the 17 SDGs
1. No Poverty: End poverty in all its forms everywhere.
2. Zero Hunger: End hunger, achieve food security, improve nutrition, and promote sustainable agriculture.
3. Good Health and Well-Being: Ensure healthy lives and promote well-being for all at all ages.

Seepage is the movement of water through the soil body and foundation of a hydraulic structure. In an earthen dam, uncontrolled seepage may produce excessive water loss, high pore-water pressure, slope instability, internal erosion, piping, or complete structural failure. Seepage analysis therefore determines the quantity and path of water movement and helps engineers design filters, drains, impervious cores, cut-offs, and other protective components. This chapter explains the objectives and importance of seepage analysis, Darcy’s law, permeability, seepage velocity, phreatic lines, flow nets, equipotential lines, and flow channels. It also examines seepage forces, piping conditions, exit gradients, and methods for improving dam safety.

Introduction

Spatial variation structure of soil properties is a key input for digital soil mapping and generally determined through geostatistical techniques (Webster and Oliver 2007). In geostatistics, spatial variation is expressed by semivariogram ?(h) , which measures the average dissimilarity between data separated by a vector h. It is generally computed as half of the average squared difference between the components of data pairs:

One of the major goals in agricultural crop management is to achieve maximum yield with minimum investment while maintaining a good quality of the crop produce. Crop infestation due to insects, diseases, and weeds are some of the major factors responsible for loss in crop yield. Further, an efficient Precision Agriculture Crop Management System is one, which is capable of precisely detecting the crop yield indicators (or crop health parameters) at an early stage and can predict the yield at any point of time of the crop cycle.

Sensor is a physical device used to detect manifestations or variations in a process value and collect data in real time assessment mode for measuring and predicting system/device performance and finally analyzing it to attain a specific task or application. Sensor technology is now-a-days used for interdisciplinary applications for data collection, system analysis and monitoring.

Abstract

Sabatayn basin is one of the two mature basins producing oil and gas in Yemen. Due to the recent decline in oil production, the exploration activities have been extensively concentrated mainly on these two basins (Sabatayn and Sayun-Masilah). So, this present study has been conducted to evaluate the exploration potential of the remaining trap areas within these mature basins. Fractured basement of reservoir becomes very attractive exploration target in Yemen recently, after the recent discoveries of basement fractured oil fields. As the evaluation of basement traps needs time and fund, to reduce the same, this research study uses a new technique which was devised with the help of Geoinformatics by analyzing the surface and subsurface lineaments effectively. In this study, a satellite image Enhanced Thematic Mapper (ETM+7) based analysis was conducted for extracting surface lineaments, and for the subsurface lineaments, the basement lineaments were extracted from seismic, magnetic and gravity data. An orientation analysis of these surface and subsurface linear features was performed to detect the basic structural grains of the study area. The correlation between these structural grains and subsurface oil and gas traps was performed to understand the connectivity to the reservoirs. This paper discusses in detail about the same and the importance of using surface and subsurface lineament analysis for delineating hydrocarbon reservoirs in the study area.

Introduction

Crop models can be broadly classified into two categories: dynamic or process-based crop simulation models (CSMs), and reduced form statistical models. Since simulation models are based on various plant-related processes, they develop or alter in tandem with plant growth (Chetty 2009); as a result, they are inherently nonlinear (Olmstead 2009). To run, calibrate, and validate, they require a significant amount of date input. However, statistical models are helpful in situations where there is a lack of data because they describe the relationships between a subset of variables while keeping the others constant. (Chetty 2009).

Introduction

Site-specific nutrient management (SSNM) is the dynamic, field-specific management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling throughsoil–plantsystems(Sarkar etal.,2017).Thisnewmethodofnutrient recommendationsisprimarilybasedontheinitialnutrientstatus(INS)from thesoil andthecrop’sneedfornutrients toachieve thedesiredyield.

The effective application of agricultural meteorology in supporting agricultural decision-making and productivity relies heavily on the skills and knowledge of the individuals involved. As the agricultural sector continues to face growing challenges from climate variability, weather extremes, and changing environmental conditions, the need for well-trained and informed professionals has never been more pressing. Skill Advancement and Knowledge Empowerment (SAKE) is crucial for ensuring that stakeholders, including farmers, agricultural advisors, policymakers, and meteorological service providers, have the necessary expertise to harness the benefits of agricultural meteorology (Fig. 13.1). This chapter focuses on the education, training, and capacity-building initiatives required to enhance the skills and knowledge of individuals working in operational agricultural meteorology. It explores the various levels of education and training needed, from basic awareness and understanding of meteorological concepts to advanced technical skills and specialized knowledge in areas such as agrometeorological modeling, data analysis, and decision-support systems. The chapter begins by discussing the importance of SAKE in the context of agricultural meteorology, highlighting the key challenges and opportunities in this field. It then examines the different types of education and training programs that can be implemented to address these challenges, including formal education, vocational training, workshops, and online courses. The role of knowledge empowerment through access to relevant information, tools, and technologies is

The ancient partnership between agriculture and weather forecasting, initially rooted in observational knowledge and rudimentary tools, has been fundamental to sustaining human populations for millennia. Traditional agricultural meteorology has diligently documented climate patterns, analyzed weather phenomena, and established empirical relationships between meteorological variables and crop yields. This foundational knowledge remains invaluable. However, the stark realities of the 21st century, dominated by the accelerating and often unpredictable impacts of climate change, necessitate a radical departure from conventional thinking. The comfortable reliance on historical data and linear projections is increasingly inadequate in a world characterized by unprecedented c limate variability, intensifying extreme weather events (as you‘ve extensively researched), and the urgent need for enhanced food security for a growing global population.
• The limitations of traditional agricultural meteorology become increasingly apparent when confronted with the complexities of climate change. For instance, relying solely on historical rainfall patterns for irrigation planning becomes precarious when those patterns are drastically altered by shifting climate regimes. Traditional pest and disease forecasting models, often based on established temperature and humidity thresholds, may fail to accurately predict outbreaks as these thresholds themselves are disrupted by climate anomalies. The need for real-time, localized, and predictive insights into the dynamic interplay between the atmosphere and agricultural systems has never been more critical.
• This is where the transformative power of emerging technologies steps in. The next generation of agricultural meteorologists stands at a pivotal juncture, tasked with leveraging advancements in diverse technological domains to revolutionize the field. Consider the sheer volume of data now available from a constellation of sources – weather satellites providing high-resolution global coverage, dense networks of ground-based sensors capturing hyperlocal environmental conditions, agricultural drones offering detailed field-level monitoring, and IoT devices collecting real-time soil and plant health parameters. This deluge of information dwarfs anything

Smart farming, otherwise called smart agriculture, uses progressed agriculture advancements to improve ranch the farm management. By consolidating Industry 4.0 advancements, Precision farming achieves doable outcomes like extend production, resource insurance, and lessened petroleum derivative result. Internet of Things (IoT) accepts a fundamental part in Smart farming, by Precision agriculture to screen and deal with things, as a matter of fact. Information examination is in like manner essential for isolating huge encounters from the colossal proportion of data created by IoT devices. The reconciliation of present day modern digital technology has change into smart farming as information empowered and information driven activity. After affiliation, further examination of each IoT and data analytics frameworks in the agricultural region can be positive.
The total population is predicted to increase by 10 billion in 2050, by enhancing interest for agricultural goods, and notable food. This has brought about a developing interest supply befuddle. Issues including restricted assets, natural changes, and work deficiencies increment the jumble, endangering food security and agricultural production. To defeat these worries, farming should be feasible and utilize state of the art innovation. One important choice is “smart farming,” that attempt to increased food productivity by meeting the population demand in a sustainable manner. This is steady with the Assembled Countries’ Sustainable Development Goals (SDGs), which hold back nothing security utilizing reasonable ways.
Smart farming strategy for agriculture business had changed Ancient farming into smart agriculture. Ancient times, human cultivated the soil to provide food for livelihood. Such activity is known as agriculture, and it follow a long path between agriculture 1.0 to agriculture 4.0. Producers employed agricultural implements including pitchforks and sickles for cultivation throughout the 17th century, a period called as Agriculture 1.0. Farming practices, a huge number of workers are needed to conduct agricultural tasks. Moreover, agricultural

11.1 Introduction

The application of greenhouse in agriculture is vast. Greenhouses are used for growing flowers, vegetables, fruits, transplants and varieties of certain crops in a controlled environment which is appropriate for the plants. The edible flowers are served in some good restaurants to grow them precise calculation is required. Sensor network can help to monitor and automate the activities in greenhouse. The objective of this chapter is to discuss the development of sensor nodes to monitor the growth of flower plants through XBee and internet of things. The system comprises of three sections- sensor nodes, local server and main server. Sensor nodes communicate to local server with XBee and then local server communicates the same data to main server through IoT.Fig.11.1 shows the generalized block diagram, sensor node is to be implemented on each flower pot and communicating to local and main server.

Water system is considered an Arti?cial usage of the water on the dirt utilizing various strategies like siphons, cylinders and showers. Typically, the requirement for water system shows up where the precipitation is unpredictable, in dry times, or where lack of hydration is customary. An excessive number of water system frameworks are accessible with various sorts as indicated by the Climate of the dirt. The water utilized in water system has numerous assets, like underground water, through wells or springs; the surface water, from lakes, streams; or other a few sources, for instance, the treated wastewater or desalinated seawater. In this way, farmers need to save and safeguard their Agriculture water sources by limiting the capability of diseases. Since with any groundwater extraction, clients of water system water should be mindful so as not to deplete groundwater out with a rate more prominent than it is being recovered. There are two strategies of current water system systems, the conventional water system procedures and the Smart water system philosophies. Water system is like surface water system, trickle water system and sprinkler water system. Later on, a few extreme and confounded issues will be met by inundated agriculture. An illustration of a signi?cant issue is the low efficiency of the water assets for water system. An important safe guess is that over 40% of the diverted water system water is spent before at the level of the ranch, either through profound permeation or at the surface spillover. Nonetheless, frequently these misfortunes address botched open doors for water, as they keep water from showing up at the downstream redirections. One of the most noticeable coming issues is the expansion of various water prerequisites assets, for example, manufactural and metropolitan requirements. These are utilized to give water assets a higher worth, so as a result, researchers favor concentrating entirely on rehearses with a high inefficient rate. s worldwide populace is expanding quickly, rural efficiency in drylands needs improvement to fulfill food security needs. Subsequently, taking on Precision water system approaches is a suitable choice to more readily use the accessible water assets and further develop water efficiency in drylands. Water shortage has become one of the basic issues and undermines the supportable improvement

Plant breeding is a revered custom that proceeds with the most common way of creating further developed plant cultivars, which traces all the way back to the beginning of agriculture . People started to observe levels of greatness among the plants in their fields not long after the underlying regulations of cereal grains, from which they put away the seeds of the best to develop new harvests. Early plant-reproducing advancements were the trailblazers to such simple choice methodologies. Individuals from the whole way across the world investigated and developed almost 7000 assortments of food plants during Breeding 1.0, which started quite a while back. Breeding stage 2.0 began in the late nineteenth and mid 20th hundreds of years when an inbreeding discouragement was found. During this time, many advances in plant breeding were made in the study of reproducing itself, like duplicated field preliminaries, controlled intersections, factual examinations, formal exploratory plans, crossover breeding, family-based appraisals of reproducing values, and exact yield estimation at scale (e.g., with Muldrow joins). Breeding 3.0 came to around a long time back, when sub-atomic markers and genomic information started to enhance phenotypic information. Breeding 4.0 is quickly drawing nearer, with immense omics information and the fast advancement of informatics innovations. Starting from the start of the plant breeding cycle, beginning from crop training, new methodologies interceded by various mechanical upheavals are being advanced in the science consistently to build the speed, exactness, and Precision in plant reproducing. In the previous 10 years, this science has proactively brought the green upheaval by creating semi-predominate, supplement responsive, and crossover cultivars. In any case, taking into account the populace development, diminishing arable land, and Climate changes, these have requested more exact, high throughput moves toward that can intervene to foster harvest cultivars at a more prominent speed, with higher exactness, and accuracy. In such manner, the field of Artificial brainpower has as of late arisen, which has been recommended to have unprecedented potential to help with breeding Climate versatile Precisionyields. Climate strong harvests keep up with or increment crop yields

The fate of smoke from a wild land fire depends in large part on the airflow carrying it away from the fire which can involve a complex interaction of eddies that may occur near the ground or expand beyond the atmospheric mixed layer (Fig.11.1). The buoyancy of smoke and ambient atmospheric conditions determines how high and how quickly the smoke rises, and thus where it travels.
The smoke plume distribution is affected by number of factors as indicated below:
• Elevation: The smoke plumes at higher elevations disperse the smoke particles more vertically while plumes from lover elevations disperse more horizontally.
• Temperature: The large difference between the smoke and surrounding air affects the Plume dynamics. The buoyancy force increases the vertical velocity of the particles, while heat diffusion increases surrounding air temperature.
• Moisture: The presence of moisture can modify the distribution of smoke and chemistry of smoke particles.
• Topography: Topography can influence emissions that remain within the mixing zone.
• Atmospheric stability: Atmospheric stability can affect the distribution of smoke.

ABSTRACT

Hyperspectral data are generally noisier compared to broadband multispectral data. Therefore, it is desirable to smoothen the reflectance spectra. Smoothing needs to preserve an absorption feature while simultaneously removing noise. It is very essential to optimize the smoothing filter to minimize disturbances to the statistical properties of the original data. This study was carried out to see the effect of Fast-Fourier Transform (FFT) based smoothing technique on the statistical properties of vegetation spectral response and to see the effect of the filter size. The data used for the present study is the reflectance spectra data obtained from space borne EO-1 onboard Hyperion sensor. Filter sizes were varied between 3 and 15 with the increment of 2, i.e. 3, 5, 7..........15. Paired t-test was carried out between the original and the smoothed data (for the selective bands) for all the filter sizes in order to see the extent of distortion with changing filter sizes. Our study revealed that the distortion in the spectra increases with the increase in the filter size. Beyond filter size 11, the number of locations within the spectra where the smooth spectra were statistically different from its original counterpart increased to 14 and reaches 21 at the filter size 15.

The research setting provides specific details about the location where a study is conducted. This study area typically encompasses a particular geographical region, such as a state, district, block, or gram panchayat, chosen according to the research requirements. The researcher must have a thorough understanding of the locality, including its communication methods, in order to effectively access all parts of the region for data collection.

In social science research, a clear comprehension of the characteristics, attitudes, and behaviours of the individuals within the study area is essential. Without this understanding, accurately conceptualizing, perceiving, and interpreting data becomes challenging. To obtain a comprehensive understanding of the implications and behavioural intricacies of the local people representing a broader community in the referenced study area, researchers must have a solid grasp of these factors. Therefore, this chapter critically analyses the socio-demographic background of the residents in a rural setting.

The term “research setting” denotes the context in which research inputs and elements are absorbed, interacted with, and mutually contribute to the system’s overall performance.

The research setting is crucial because it shapes and influences the interactions of various factors and components. Therefore, conducting a study on how farmers perceive persuasive issues and other topics requires a local context that takes into account the unique natural environment, demographics, crop ecology, institutional framework, and socio-cultural factors. For example, when writing a paper about the social behaviour of chimpanzees, it is important to include details about the research setting. This may include information such as where the chimpanzees were studied (in the wild or in captivity), the number of chimpanzees that were observed, whether they were part of the same social group, the geographic location, the time of the study, the season/weather conditions, the availability of resources such as food, water, and shelter, and any environmental factors that were present.

Sustainable Development A social cause is a problem that influences a considerable number of individuals within a society. There are many environmental issues the India like pollution, waste management sustainable development, rain water harvesting etc.

4.1 Urban Issues

(i) Water Problem

The world’s population is increasingly found in the cities. Now a days, throughout the developing world, urbanization trends are gaining speed and are irreversible. From a technical standpoint, it is easier to provide water and sanitation services to people living closer together in urban settings than in dispersed rural communities. However, the costs of meeting the needs are much higher per capita, and are growing. The health risks posed by the lack of sanitation increase exponentially as densities increase and as people share drinking water and sanitation resources.

The urban environmental sanitation crisis in developing countries is taking a large health, economic, and environmental toll on all city residents. Willingness to pay for basic water and sanitation services is often high in peri- urban neighbourhoods, provided that services are appropriate, effective, and affordable. The use of a strategic sanitation approach should helps to build capacity within implementing agencies and enhances the ability of communities to make sustainable sanitation improvements.

(ii) Energy Problem

Energy is basic to development. Modern energy services are powerful engine of economic and social opportunity: no country has managed to develop much beyond a subsistence economy without ensuring at least minimum access to energy services for a broad section of its population. It is not surprising to find, therefore, that the billion who live in developing countries attach a high priority to energy services. On average, these people spend nearly 12% of their income on energy. At the same time, the provision of energy services especially through the combustion of fossil fuels and biomass can create adverse environmental effects. In rich countries, much attention is directed to the regional and global consequences of fuel combustion, because many of the local effects have been controlled at considerable expense over the past half- century. In developing countries, the local environmental problems associated with energy use remain matters of concern that are as, or even more, urgent than they were in industrialized countries 50 or 100 years ago. Further, it is the poor who suffer most severely from such problems, because it is they who are forced to rely upon the most inefficient and polluting sources of energy services for lack of access to better alternatives.