eChapters

A detention reservoir temporarily stores storm runoff and releases it gradually at a controlled rate, thereby reducing the peak discharge reaching downstream channels and communities. Unlike a conventional conservation reservoir, its principal function is short-term flood moderation rather than permanent water storage. Its performance depends on basin capacity, inlet and outlet arrangements, catchment response, design storm, allowable downstream discharge, and available drawdown time. This chapter explains the concept, purposes, and functioning of detention reservoirs and distinguishes them from ordinary storage reservoirs. It covers their classifications, site requirements, storage estimation, outlet design, routing principles, benefits, limitations, maintenance, and application in rural watersheds, urban drainage systems, and integrated flood-control programmes.

Abstract

The geochemical signature and vulnerability of groundwater of the East Coastal aquifers of  India from Pondicherry to Nagapattinam were determined. The coastal aquifers are fragile and this situation becomes more intense in aquifer systems like that of the Pondicherry to Nagapattinam region. A total of 136 water samples were collected during pre and post monsoon of 2008 and 2009 respectively. Out of which 45 samples for each pre and post monsoon from shallow groundwater and 23 samples for each pre and post monsoon from deeper groundwater.. The samples collected were analysed for major, minor cations and anions. Selected samples were also analysed for stable isotopes (d¹⁸O and dD). The analysed samples were studied for; their utility for different purpose, the nature of the geochemical classification, their variations in ion ratios, extent of sea water intrusion and the percentage of mixing, The study reveals that chief mechanism controlling shallow groundwater is the salts precipitated in the pore spaces during the Pre monsoon were leached and dissolved during the subsequent monsoon apart from sea water intrusion. Southern part of the study area  is more vulnerable than the Northern part of the study area.

Scientific leadership is a cornerstone of progress in agricultural sciences, playing a pivotal role in driving innovation, fostering collaboration, and addressing global challenges such as food security, climate change, and sustainable resource management. Effective scientific leadership ensures that research efforts are strategically aligned with pressing societal needs and that knowledge generated in laboratories and fields translates into tangible benefits for farmers, policymakers, and communities. This chapter delves into the qualities of effective leaders in agricultural sciences, the challenges they face, and strategies to nurture and support scientific leadership in this critical domain. The G20 Chief Science Advisers Roundtable (G20-CSAR), initiated under India’s G20 Presidency, is a flagship program aimed at fostering collaboration and enhancing access to scholarly scientific knowledge. This effort emphasizes the synergy of scientific expertise, ensuring that innovations and advancements are not only accessible but also utilized effectively to address pressing global concerns, such as climate change, public health, sustainable development, and digital transformation. Through this platform, India seeks to promote open science, equitable knowledge-sharing, and stronger international cooperation in research and development.
7.1. Qualities of Effective Leaders in Agricultural Sciences
To excel in agricultural sciences, leaders must embody a diverse range of qualities that enable them to inspire teams, foster innovation, and drive impactful research. These qualities include:
7.1.1. Strong Communication Skills
Effective leaders in agricultural sciences must be adept at communicating complex scientific concepts to diverse audiences. Whether engaging with fellow researchers, policymakers, farmers, or the general public, they must tailor their messages to ensure clarity and relevance. Strong communication skills are also essential for securing research funding, building partnerships, and advocating for science-based policies.

4.1 Introduction

The objective of this chapter is to design a mobile app for agricultural field automation with Blynk app. Mobile app is designed to control the water management in field as per requirement. The complete system comprises of two sections sensor node and mobile app. Sensor node comprises of Node MCU, Power supply, LCD, Relay board, soil sensor and Ultrasonic sensor. The system is designed to establish control and communication with specific agricultural field to take sensory data from soil and ultrasonic level sensor and control the PUMP IN motor and PUMP OUT motor with the help of mobile app.Fig.4.1 shows a block diagram for a system to control specific agricultural field with mobile app and IoT modem.

5.1 Introduction

The chapter describes the development of local web server on the laptop, to control the actuator from the server. Web server is used as remote control for the system. The system is designed to control water pump with respect to the soil sensor values. It comprises of NodeMCU, soil sensor, LCD, two relay boards for pump in and out the water from field.

The world is at a critical juncture where the impacts of climate change are increasingly felt across various sectors, with agriculture being one of the most vulnerable. The agricultural sector, which is a cornerstone of many economies, especially in developing countries, faces unprecedented challenges due to rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events. These climatic changes not only affect crop yields and quality but also have broader implications for food security, economic stability, and sustainable development. In this context, the development and dissemination of operational agroclimatological advisories ((OAA) have emerged as a vital strategy for mitigating the impacts of climate variability on agriculture.
5.1. The Imperative for Agroclimatological Advisories
Agroclimatological advisories are specialized services designed to provide farmers, policymakers, and other stakeholders with timely and accurate information on optimal agricultural practices in response to prevailing and forecasted climatic conditions. These advisories are grounded in the integration of climatological data, agricultural research, and local knowledge, aiming to enhance agricultural productivity, reduce risks associated with climate variability, and promote sustainable agricultural practices (Fig. 5.1).
The relevance and necessity of these advisories are underscored by several critical factors:
• Climate Change Impacts: The agricultural sector is both a contributor to and a victim of climate change. Agroclimatological advisories can guide farmers in adopting practices that reduce the vulnerability of their crops to changing climatic conditions.
• Food Security: With the global population projected to reach 9.7 billion by 2050, enhancing agricultural productivity and resilience is crucial for ensuring food security. Tailored advisories can help farmers make informed decisions that potentially increase yields and reduce losses.
• Economic Stability: Agriculture is a significant source of income and employment in many countries. By minimizing risks associated with climate variability, agroclimatological advisories can contribute to economic stability and poverty reduction.

Composting means decomposing organic wastes. It is a safe, non-polluting and microbiological method for disposal and recycle of organic wastes by converting those to balanced plant food materials, commonly known as compost, the organic fertilizer. In other words, the mineralization and partial humifi cation of organic materials by the action of microorganisms under suitable condition is called composting (Figure 6.1). The organic wastes undergo different physical and chemical changes with the help of microbes like bacteria, fungi, actinomycetes, etc. During composting the population of pathogenic microorganism reduces while that of benefi cial microorganism increases.

Introduction

Globally, the SOC pool of 1,550 Pg (1 Pg = 1015 g) to 1-m depth is about three times that of biotic and twice that of atmospheric pools (Post et al. 1982, Watson et al. 2000). In arid and semi-arid ecosystems, SOC contents show high degree of spatial variability due to patchiness of vegetation (Wiesmeier et al. 2009). The arid ecosystem might play a key role in mitigation of climate change effects through sequestering carbon in soils (United Nations 2011). Therefore, a suitable land use system having high C sequestration potential for arid ecosystem needs to be identified and promoted. However, the prevailing conditions of arid ecosystems normally does not support dense vegetation. Therefore, low carbon stock is expected in both above-ground vegetation and below-ground soil. Fruit trees are an integral part of arid farming system and played a significant role to improve the nutritional security and employment opportunity of farmers but also have potential role to increase carbon storage both in biomass and soil. To quickly assess the soil carbon restoration programmes, it is important to know how much soil carbon is stored in an agricultural farm through adoption of different land management practices. Therefore, reliable estimates of SOC pools are essential to establish the soil carbon sequestration programmes at different landscape scales.

Introduction

A complex spatial soil properties pattern is directly controlled by soil internal factors and anthropogenic impacts.Hartemink et al. (2001) elaborate a strong decline in mineralogy, morphology and genesis of soil research than strong increase in pedometrics applications during 1967 to 2001. Hartemink and McBratney (2008) identified this change as “soil science renaissance”. Basically, the soil science deeply rooted in agriculture, geology, and chemistry but the paradigm has been shifted from classification and inventorization to quantification of soil patterns in spatial as well as temporal patterns and their impact on the ecosystem health and hydrological cycle. The environmental keen approach explains that soil is in the center of the ecosystem and highly interacting with the biotic and abiotic factors of the ecosystem and a complex pattern and process has been co-evolved with the time.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Introduction

Disaster is defined as a serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses, which exceed the ability of the affected community or society to cope using its own resources (ISDR 2002).

Disaster in Global context - The impact of natural disasters in terms of human and economic losses has risen in recent years, society in general has become more vulnerable to natural disasters. Those usually most affected by natural and other disasters are the poor and socially disadvantaged groups in developing countries as they are least equipped to cope with them (IFRC 2001).

Introduction
In the past, the approach to disaster management was relief-focused and restricted to helping the area affected by the disaster. involves a continuous and integrated process of organizing, coordinating, and putting into action measures that are required or advantageous for preventing danger or threats, mitigating risk or its severity or consequences, building capacity through research and knowledge management, being ready to deal with any disaster, responding quickly to any situation that poses a threat to the community, determining the extent or severity of effects, evacuation, rescue and relief efforts, rehabilitation, and reconstruction.
Phases and Elements in Disaster Management
I. Pre-disaster phase: There are three components to the pre-disaster phase: preparedness, mitigation, and prevention.
II. The aftermath phase, encompassing response, recuperation, and restoration. All these components are connected by a structure of institutions and legislation.

Introduction
There are always going to be natural disasters like cyclones, earthquakes, and floods. They are a component of our surroundings. Nonetheless, the amount of damage caused by natural disasters can be reduced if there is a reliable warning system in place
and the impacted population is ready. Thus, while reactive mechanisms constituted the majority of disaster management in the past, a change towards a more proactive, mitigation-based strategy has been gradually observed in the last several years.
The field of emergency and disaster management focuses on mitigating and preventing potential hazards. It is a field that deals with anticipating catastrophes, responding to them (e.g., emergency evacuation, quarantine, mass decontamination, etc.), and helping to rebuild society following natural or man-made disasters. Generally speaking, emergency management is the ongoing process through which all people— individuals, organizations, and communities—manage risks in an effort to lessen the effect of catastrophes that may arise from those risks. The perception of danger by individuals who are exposed influences the actions that are taken.
Definition
The word “disaster” comes from the French word “disaster.”
A disaster is a state brought on by natural factors in which there is a significant disruption of services and infrastructure that has an adverse effect on the environment, the economy, and human life.

A disaster is any event natural or manmade which creates an intense negative impact on people goods and services and or the environment and exceeds the affected community internal capability to respond prompting the need to seek outside assistance. In other words a disaster is a serious disturbance of a community or society interfere human resources losses and impacts on economically and environmentally or disaster means simply an event has unfortunate consequences on only on human but also on whole environment. Disaster can be two types’ Natural disaster and manmade disaster.

Introduction

A disaster is the result of an immediate situation or the result of a long, set process, which disrupts normal human life in its established social, traditional and economic system. This is due to the destruction of environment which is caused by extraordinary natural destructive phenomena or human-induced hazards resulting in human hardship and suffering beyond recovery unless external aid is brought in.

ABSTRACT

This work presents the results of multispectral analysis of TM (Thematic Mapper) sensor images on board the satellite LANDSAT 5 for discrimination of selected features of a Nainarmalai region of part of Namakkal District, Tamil Nadu using hyperspectral tools. The spectral classification was carried out using hyperspectral tools namely, a) spectral reduction by the Minimum Noise Fraction (MNF) transformation, b) spatial reduction by the Pixel Purity Index (PPI) and c) manual identification of the end members using the N-dimensional visualizer. The results of study shows that the kinds of features such as magnetite quartzite, sand, harvested land and vegetation area are discriminated well (as given in the figure) and show a strong correlation with landscape.

Keywords: Landsat TM Multispectral Images, MNF transformation, PPI, endmembers, Reflectance spectrum, ENVI software.

1. INTRODUCTION

With a multispectral analysis of an images collected by radiometers and detectors embarked on a satellite, one can make: “spectroscopy by satellite imagery”. Imaging spectrometers or “multispectral sensors” are remote sensing instruments that combine the spatial presentation of an imaging sensor with the analytical capabilities of a spectrometer. With a radiometer such as AVIRIS ”Airborne Visible/Infrared Imaging Spectrometer” where the number of spectral bands exceeds the 200, and with a spectral resolution of the order of 10 nm or narrow, it can produce a complete spectrum for each pixel of the image. The case of this radiometer enters on the “Hyperspectral” field. In the case of our study, we are using the radiometer TM (Thematic Mapper), which is on board for the satellite Landsat 5, and which only has 7 multispectral bands, with a spectral resolution of the order of 100 nm. Like finale result, one using the spectroscopy by satellite imagery, we can identify the spectra of various materials which are on various terrestrial surfaces.

Introduction

In agriculture, plant diseases have always been a major problem, as they cause crop quality to decrease, resulting in yield. Plant disease has a range of effects from minor symptoms to serious damages in whole crop areas, which lead to high financial costs and significantly impact the agricultural economy, particularly in developing countries dependent on a single crop or on a few crops.

Various approaches have been developed to detect disease in order to avoid significant losses. Causal agents have been precisely defined by methods developed in molecular biology and immunology. However, for many farmers these techniques are inaccessible and require detailed knowledge of the field or substantial time and energy to be used. According to the United Nations Food and Agriculture Organization, most farms in the world are small and family-owned in developed countries. Such families provide food for a significant proportion of the population of the planet. However, hunger and food insecurity are not rare and there is restricted access to markets and services. For the above reasons, a great deal of work has been undertaken in order to establish methods that are sufficiently reliable and available to most farmers. Where Plant disease is an essential factor contributing to a major decline in plant production quality and quantity. Plant disease identification and classification are a significant role in improving plant production and economic development.

Abstract

Climate change is a long-term change in the statistical distribution of weather patterns over periods of time that range from decades to millions of years. It may be a change in the average weather conditions or a change in the distribution of weather events with respect to an average. Climate change may be limited to a specific region, or may occur across the whole Earth.Since, time immemorial human beings have attempted to predict the disease occurrence informally by mere observing the changes in pattern of weather parameters. But the present scenario has been very much altered with the incessant and irrational use of natural resources exposing the atmosphere to the enormous encumbrance. Excessive CO₂ emissions, deforestation and injudicious use of chemicals in agricultural fields have ultimately led to the climate change, which needs an absolute engrossment. Climate change has also invited the emergence and resurgence of many of the pest and diseases posing a grave challenge to manage them. So there is a need for immediate action through Government policies and scientific community to combat the situation. Disease forecasting models may be one of the rescuing options to mitigate the problems of agriculture.

Abstract

The disease is an output of the interaction among host, pathogen, and the environment over a period of time. Given that a susceptible host and an aggressive pathogen are available, weather determines the intensity of the disease over time. To capture the dynamics of the interactions among these biophysical variables, efforts have been made in the past to understand the variables per se and their interactions through modeling approaches.  The important weather parameters are temperature; rainfall/dew; leaf wetness period, wind, bright sunshine hours all of which are in turn influenced by soil properties. The important parameters of host that can influence disease development are susceptibility; growth stage and form; structure and density of the plant population and overall health. For pathogen, to cause disease, it should be present in the vicinity, pathogenic, adaptable with reproductive fitness and possess efficiency of survival and dispersal. 

Introduction 
As we have journeyed through the preceding chapters, meticulously examining the foundational understanding of crop-weather interactions, the sophisticated development of crop simulation models and decision support systems, the invaluable contributions of indigenous technical knowledge, and the systematic formulation of precise and customized agroadvisories, we now arrive at the decisive stage of this entire endeavor of dissemination of Agromet Advisories. The profound scientific insights and meticulously crafted recommendations, no matter how accurate or relevant, remain inert unless they effectively reach the hands, ears, or screens of the farmers who need them most, in a format they can readily understand and apply. This chapter, therefore, is dedicated to unraveling the complex, yet essential, process of ensuring that crucial agrometeorological information successfully traverses the last mile to translate into tangible impact on agricultural practices and outcomes across diverse landscapes. The challenge of effective dissemination extends far beyond a simple broadcast; it necessitates the establishment of a robust, dynamic, and multichannel communication ecosystem. This ecosystem must be meticulously designed to account for the rich tapestry of socio-economic, technological, and literacy disparities prevalent within farming communities. Historically, traditional methods such as the communal gathering around a radio for a farmer‘s bulletin or face-to-face discussions during village meetings formed the bedrock of knowledge transfer, leveraging established social networks and widely accessible, albeit low-tech, communication tools. However, the relentless march of the digital revolution has fundamentally reshaped this landscape, unveiling an unprecedented potential for expansive reach, enhanced personalization, and rapid delivery of information, thereby opening a myriad of new avenues for disseminating critical advisories with remarkable speed and specificity.