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Global agricultural research, an essential field in ensuring food security and sustainable development, is heavily influenced by the academic and publishing ecosystems in Europe and North America. This chapter examines the challenges faced by researchers from developing regions, focusing on limited representation in editorial boards, barriers to accessing high-impact journals, and the overarching dominance of Western institutions (Fig. 9.1).
These issues underscore the need for greater inclusivity and recognition of regional expertise to foster a more equitable global research landscape.
9.1. Dominance of Europe and North America in Agricultural Research
Publishing
The dominance of Europe and North America in agricultural research publishing is evident from the distribution of international journals in agriculture and allied disciplines. Approximately 75% of these journals originate from these regions. Breaking this down further:
• Europe accounts for 43.6% of international journals, with leading contributors including the United Kingdom (14.1%), the Netherlands (8.5%), Germany (6.2%), France (4.3%), Italy (3.5%), and other European countries (7.0%).
• North America contributes 31.4%, with the United States alone publishing 28.5% and Canada accounting for 2.9%.

9.1 Introduction
Building upon our foundational understanding of statistical methods in meteorology, Chapter 9, “Advanced Analytic Techniques,” propels us into the forefront of data science applications within the atmospheric sciences. The imperative for these advanced methodologies stems directly from the evolving nature of meteorological data itself. Modern observations, numerical weather prediction (NWP) model outputs, and climate simulations generate an unprecedented volume of information, often arriving with incredible velocity and exhibiting immense variety in format and structure. Traditional statistical approaches, while robust for many tasks, often struggle to efficiently process such vast, high-dimensional datasets, or to fully capture the intricate, often non-linear, relationships inherent in atmospheric processes.
This chapter is dedicated to exploring why and how these sophisticated techniques are not merely supplementary, but increasingly essential. We will delve into how machine learning and deep learning algorithms excel at identifying subtle, complex patterns within massive datasets, enabling breakthroughs in areas like severe weather detection, nowcasting, and the bias correction of model outputs. We will also examine advanced spatial statistics, which provide powerful frameworks for explicitly modeling the intricate geographical dependencies of meteorological variables, crucial for accurate interpolation, downscaling, and understanding regional climate impacts. Furthermore, the chapter will introduce functional data analysis, a method particularly adept at treating continuous meteorological fields (like temperature profiles or pressure maps) as functions, allowing for a more holistic analysis of their evolution and variability. By embracing these advanced analytical techniques, meteorologists and climate scientists can unlock deeper insights, enhance predictive skill, more rigorously quantify uncertainties, and ultimately contribute to a more resilient society in the face of a changing climate. Prepare to expand your analytical toolkit and discover new frontiers in meteorological understanding.

6.1 INTRODUCTION

Besides simple analysis, GIS is useful to do advanced analysis like network analysis, spatial analysis, 3D analysis and modeling. Different softwares are available to perform such analysis and the above mentioned analysis is done in separate modules of the softwares.

6.2 NETWORK ANALYSIS

Network consists of well connected linear features. Roads, railway line, water distribution system, sewer line, streams are some of the examples of networks. These networks consists of nodes and arcs with designated directions and connection with other linear features. Networks are topology based with attributes for the flow of objects like traffic. Road network will be considered for explaining network analysis.

Road map is digitised with nodes and arcs. Signals, accidents are represented as points. Road is represented as line and two or more roads intersect at nodes. Attributes are added to the nodes and lines. Dynamic segmentation model is used for network analysis. It is built on lines of network. In road network analysis, attributes for travel time, one way street, two way street, right turn, impedance are added to the nodes and arcs. A link is the line running between two nodes. Link impedance is the cost of passing through the link. Travel time is different in links in cities due to varying traffic at different locations. Time taken to pass through a known length of link may give the cost but the travel time in a link is not uniform because of varying speed designated in the link as 20 km per hour and 30 km per hour. Hence it is important to consider the length of link and also the travel time. If the speed limit is 20 miles per hour for a length of 2 miles, then the travel time is 6 minutes (2/20x60 minutes). Also tuning in the network is considered. A road may have a maximum of 12 possible turns excluding U turns. There are right turn, left turn, straight way for each road. Also U turn is possible. This turning takes different time depending on the signal. Straight way takes less time but left and right turn may require some more time. If signals is available then signal time should also be taken into account. Fig 6.1 show the possible turns in a road.

ABSTRACT

Coastal resources represents highly productive ecosystems and are under pressure on account of increased anthropogenic activity on the coast. There is an urgent need to protect coastal resources aiming at sustainable development. Management of coastal resources requires spatial information on land and water resources, processes, water quality in a repetitive basis. Availability of high resolution and very high resolution data made the spatial tools indispensable in planning and management of resources such as mangroves and coral reefs. Monitoring mangrove degradation and aquaculture development, land use pattern, site selection for developmental activities adhering to coastal regulation zone notification requires highly accurate spatial information that can be extracted from IRS, IKONOS data. Extensive applications of remote sensing were hindered by cost, lack of familiarity and expertise in the developing countries. A national frame work must be established involving all available spatial datasets to build up the technical capabilities and ensure sustained support.

The advent of advanced technologies and methodologies has revolutionized the field of crop disease forecasting, enabling researchers and practitioners to develop more accurate, reliable, and efficient forecasting systems. Conventional methods of disease forecasting, such as weather-based forecasting and statistical modeling, have been widely used for predicting crop disease outbreaks. However, these methods have several limitations, including reliance on historical weather data, limited spatial and temporal resolution, and lack of mechanistic understanding of disease development. In recent years, advanced methods of crop disease forecasting have emerged, leveraging cutting-edge technologies and methodologies to improve the accuracy, reliability, and efficiency of disease forecasting systems. These advanced methods include machine learning algorithms, artificial neural networks, geographic information systems (GIS), remote sensing technologies, and precision agriculture techniques. These methods have been shown to be highly effective in predicting crop disease outbreaks, and have the potential to revolutionize the field of crop disease management. Machine learning algorithms, for example, can be trained on large datasets of weather, crop, and disease data to identify complex patterns and relationships that may not be apparent through conventional statistical analysis. Artificial neural networks can be used to develop predictive models that can learn from experience and adapt to changing conditions. GIS and remote sensing technologies can be used to analyze and integrate spatial data on weather, crop, and disease patterns, enabling researchers and practitioners to identify areas of high disease risk and target interventions more effectively. Precision agriculture techniques, such as precision irrigation and fertilization, can be used to optimize crop growth and reduce disease susceptibility. These advanced methods of crop disease forecasting have the potential to transform the field of crop disease management, enabling researchers and practitioners to develop more effective and efficient disease management strategies. This chapter will provide an overview of these advanced methods, highlighting their potential applications and benefits in crop disease forecasting.
11.1 Remote Sensing Technology
Crop disease forecasting using remote sensing involves the use of satellite or aerial imagery to detect and predict crop disease outbreaks. The principles

The age-old struggle between crops and diseases has been a perpetual challenge for farmers and agricultural stakeholders worldwide. Weather patterns play a pivotal role in the development and spread of crop diseases, making it essential to integrate weather wisdom into crop disease management strategies. By analyzing historical and real-time weather data, farmers and agricultural experts can anticipate, prepare for, and respond to disease outbreaks more effectively, minimizing crop losses and ensuring food security. Effective crop disease management based on weather wisdom involves a three-pronged approach. It begins with caution, where historical climate data and disease surveillance reports are analyzed to identify potential correlations between weather patterns and disease outbreaks. This information helps farmers and agricultural experts to recognize early warning signs of disease susceptibility, such as temperature fluctuations, precipitation patterns, and humidity levels. For instance, excessive rainfall can lead to an increased risk of fungal diseases, while drought conditions can exacerbate bacterial diseases (Fig. 13.1). The confirmation phase involves closely monitoring real-time weather forecasts and disease surveillance data to detect early warning signs of an impending disease outbreak. Advanced technologies, such as satellite imaging and sensor-based monitoring systems, provide valuable insights into crop health and disease development. By combining these data streams, farmers and agricultural experts can confirm the presence of disease-causing pathogens and take targeted action to prevent their spread. Finally, a call to action is triggered, prompting farmers and agricultural stakeholders to implement integrated disease management strategies. These may include cultural practices, such as crop rotation and sanitation, biological controls, such as introducing beneficial microorganisms, and chemical controls, such as targeted fungicide applications. By leveraging weather wisdom, farmers can optimize their disease management strategies, reducing the reliance on chemical pesticides and minimizing environmental impact. By embracing weather wisdom, crop disease management can be transformed from a reactive to a proactive approach. Farmers and agricultural stakeholders can stay one step ahead of disease-causing pathogens, protecting their crops and ensuring a stable food supply. As we explore the intersection of weather wisdom and crop disease management, it becomes clear that this integrated approach holds tremendous potential for revolutionizing the way we predict, prevent, and respond to crop diseases.
13.1 Caution
Approaches to Caution Farmers on Crop Diseases, Information Generation, and Communication using Mobile Technology To caution farmers on crop diseases, it is essential to adopt a multi-faceted approach that leverages mobile technology to generate and disseminate critical information. This approach should focus on empowering farmers with timely and accurate information, enabling them to take proactive measures to prevent and manage crop diseases. Firstly, a robust disease surveillance system should be established to monitor and track crop disease outbreaks in real-time. This can be achieved through a network of field scouts, extension agents, and research institutions that report disease incidence and severity to a central database. Mobile-based applications can be used to facilitate data collection, transmission, and analysis, ensuring that critical information is captured and disseminated promptly. Once disease outbreaks are detected, mobile technology can be harnessed to generate and disseminate alerts to farmers. This can be achieved through Short Message Service (SMS) or voice messages, which can be sent to farmers’ mobile phones, warning them of potential disease risks and providing advice on management strategies. Mobile-based applications can also be used to push disease alerts, advisories, and management recommendations to farmers, ensuring that they receive critical information in a timely and accessible manner. To enhance the effectiveness of these alerts, it is essential to ensure that they are tailored to the specific needs and contexts of farmers. This can be achieved

Age old practice of photography was taken with still camera in parallel direction, not aerially scan or snapping which really present wider prospective of any object or places in rearranging or planning skilfully in two dimensional working. Earlier aeroplane played a important role in over head condition to capture snaps but again accuracy and angle of camera limited the works because these photographs were diagonally or trigonally not matched the reality of ground.

Introduction

Although both maps and aerial photos present a “bird’s-eye” view of the earth, aerial photographs are NOT maps. Maps are orthogonal representations of the earth’s surface, meaning that they are directionally and geometrically accurate (at least within the limitations imposed by projecting a 3-dimensional object onto 2 dimensions). Aerial photos, on the other hand, display a high degree of radial distortion. That is, the topography is distorted, and until corrections are made for the distortion, measurements made from a photograph are not accurate. Nevertheless, aerial photographs are a powerful tool for studying the earth’s environment. 

Because most GISs can correct for radial distortion, aerial photographs are an excellent data source for many types of projects, especially those that require spatial data from the same location at periodic intervals over a length of time. Typical applications include land use surveys and habitat analysis. 

This unit discusses benefits of aerial photography, applications, the different types of photography, and the integration of aerial photographs into GISs.

Agriculture addresses the essential food wellspring of the world, and it has been confronting serious difficulties because of the rising interest for food items, sanitation, and security worries as well as calls for ecological insurance, water conservation, and maintainability. This improvement is anticipated to go on since the total populace is assessed to reach 9.7 billion by 2050. Since farming comprises the most noticeable illustration of water utilization around the world, it is normal that food interest and water utilization will decisively increment within a reasonable timeframe. Moreover, the rising utilization of composts and pesticides combined with the increase of cultivating exercises could prompt future ecological difficulties. Essentially, arable land is restricted, and the quantity of farmers is diminishing around the world. These difficulties highlight the requirement for imaginative and practical cultivating arrangements.
Consolidating novel innovations has been distinguished as a promising answer for address these difficulties. Brilliant cultivating and Precision farming have arisen because of such discussions. The previous is an overall idea for information communication technologies (ICT) and other state of the art developments in cultivating exercises to build productivity and viability. The last option centers around site-explicit administration in which the land is separated into homogeneous parts, and each part gets the specific measure of agricultural contribution for crop yield enhancement through original advances. Unmistakable advances that certainly stand out in this field incorporate Wireless Sensor Networks (WSNs), the Internet of Things (IoT), artificial intelligence (AI) techniques, including machine learning and deep learning.
Notwithstanding the previously mentioned innovations, remote detecting has been viewed as a mechanical device with high potential to work on savvy and Precision farming. Satellites, human-maintained airplane, and robots are well known remote-detecting advances. Drones, popularly known as Unmanned

Agricultural drought management is a critical aspect of ensuring food security and sustainable agricultural practices, particularly in regions prone to water scarcity. Droughts, characterized by prolonged periods of abnormally low rainfall, can have devastating effects on crop yields, livestock productivity, and the livelihoods of farmers. Effective drought management strategies are essential for mitigating these impacts and enhancing the resilience of agricultural systems. Agricultural drought occurs when the soil moisture is insufficient to meet the needs of crops, leading to reduced growth, lower yields, and increased plant stress. This type of drought can be particularly challenging because it directly affects food production and the agricultural economy. Unlike meteorological droughts, which are defined by the degree of dryness compared to normal conditions over a specific period, agricultural droughts focus on the impact of drought on crops and agricultural productivity.

Water is the lifeblood of agriculture, fundamentally dictating the success or failure of crop production worldwide. While sufficient and timely rainfall, or adequate irrigation, ensures healthy plant growth and bountiful harvests, an imbalance in the water supply can quickly escalate into a crisis. Among the various forms of drought—meteorological, hydrological, and socio-economic - agricultural drought stands as the most direct and immediate threat to food security and farm livelihoods. It represents a critical shortfall in the moisture necessary to sustain crop growth, leading to reduced yields, compromised crop quality, and often, complete crop failure (Fig. 9.1). effective mitigation and adaptation strategies. This chapter will delve into the precise definition of agricultural drought, differentiate it from other drought types, and critically examine its various forms based on the timing of water deficit during a crop’s life cycle. By dissecting the characteristics of early season, mid-season, terminal, and apparent permanent droughts, we aim to provide a comprehensive understanding of how water scarcity at different stages impacts crop physiology, productivity, and the broader agricultural landscape.

Agricultural Meteorology, also known as Agrometeorology, is a fascinating scientific field that explores the intricate relationship between weather, climate, and farming. It essentially bridges the gap between understanding the atmosphere and the practicalities of growing crops, raising livestock, managing forests, and even fisheries. The core idea is to figure out how environmental factors like light, temperature, water, and wind impact every stage of agricultural production, from initial planning to harvest and marketing. 1.2 Scope of Agricultural Meteorology At its heart, agrometeorology investigates how various weather elements— temperature, rainfall, humidity, sunlight, wind, and even atmospheric pressure and CO2 levels—directly and indirectly affect agricultural systems. This includes how weather influences crop growth and development, impacting crucial processes like photosynthesis, respiration, and

In an era defined by increasing climate variability and the urgent need for sustainable food production, the science of Agricultural Meteorology has risen to unprecedented importance. This final chapter, “Agricultural Meteorology: A Science for Global and National Food Security (in the context of climate change),” synthesizes the principles and applications we have explored, highlighting their critical role in ensuring food security at both global and national levels, particularly within the challenging framework of a changing climate.
We have seen how weather and climate intricately influence every aspect of agriculture, from planting to harvest, and how extreme events can decimate livelihoods and threaten food supplies.1 Now, we will focus on how the interdisciplinary field of agricultural meteorology – the study of the interactions between meteorological and hydrological factors and agriculture in the widest sense – provides the knowledge and tools necessary to navigate these challenges.
This chapter will underscore how understanding and applying agrometeorological principles, including crop-weather relationships, climate risk assessment, and the use of decision support systems, are essential for developing climate-resilient agricultural practices. We will explore how this science aids in optimizing resource management, predicting and mitigating the impacts of weather-related hazards, and ultimately, in building sustainable and secure food systems for the future, both globally and specifically within the Indian context.