eChapters

Abstract

In the present study an integrated remote sensing and Geographical Information System (GIS) based methodology is adopted for demarcating the groundwater prospective zones and identification of artificial recharge sites in the Peddavanka watershed, Andhra Pradesh. In this study Shuttle Radar Topograhic Mission (SRTM) digital elevation model (DEM) with spatial resolution of 90 m and Indian Remote Sensing Satellite (IRS-1A), Linear Imaging Self Scanner (LISS- III) satellite data along with other field and collateral data on geology and well inventory have been utilized to generating the Drainage, Slope, Lineaments and Hydro geomorphology of the study area, which are an essential prerequisites for demarcating water resources and identification of recharge sites. Spatial data integration and analyses are carried out in GIS environment.

Keywords : DEM, Drainage, Hydrogeomorphology, Groundwater prospective zones, Remote sensing, GIS

1.0 Introduction

Groundwater is an important natural resource in arid and semi- arid regions, because most of the places surface water resources are scarce and some places it’s totally absent particularly in hard rock areas. Because in hard rock terrains availability of groundwater is of limited extent and its occurrence is essentially confined to fractured and weathered zones (Saraf and choudhury 1998, Sankar 2002). In these areas groundwater recharge depends on rainfall only. For understanding rainfall recharge mechanism and groundwater management in the watersheds level, first we have to understand the morphometric parameters in the watershed level.

Today a number of countries have moved away from the inorganic or chemicalised farming to organic farming yet it is not possible for India to depend entirely upon this system of farming due to increase in human population. As total availability of plant nutrients from different organic sources is projected to 7.75 MT by 2025 against the total requirement of 37.50 MT for 1504 million populations, so it means if we depend on organic sources there would be deficiency of about 30 million tonnes of nutrients.

Integrated plant nutrition system conceptualized by food and agriculture organization is the maintenance or adjustment of soil fertility and of plant nutrient supply to an optimum level for sustaining the desired productivity through optimization of benefits from all possible sources of plant nutrients viz. organic manures, fertilizers, crop residues, compost or nitrogen fixing crops etc. in an integrated manner.

The introduction of high-yielding varieties of wheat and rice, the development of intensive cereal–cereal rotations (rice–wheat, rice–rice, hybrid maize–wheat), application of high doses of N (above 100 kg N ha^–1 to each crop) without adequate P and K application, increased use of high-analysis fertilizer (urea and diammonium phosphate in place of ammonium sulphate and superphosphate) and ignoring the application of organic manures led to heavy depletion of P, K, S, Zn and other plant nutrients, which led to wide-spread de?ciencies of P, S and Zn in Indian soils. These de?ciencies of nutrients led to focus on integrated nutrient management.

Definitions of IPNM 
Concept of IPNM 
Main objectives of INM 
Components of IPNM

6.1. Introduction

The growing human population presents a number of issues, including food scarcity, malnutrition, inadequate natural resources (land and water) for agricultural production, and environmental deterioration (MacDonald, 2010). Today, viable intensification of agri-production systems can fulfill rising food, nutrition and environmental demands (Pretty and Bharucha, 2014). Production of rice (Oryza sativa L.), a vital element of food security, which feeds nearly half of the world's population, occupies 143 million hectares of land (FAOSTAT, 2023), and uses up to 90% of all irrigation water in Asia (Liu et al., 2018). For instance, it has been determined that systems for producing rice are one of the main industries that wastes water resources and greatly increases greenhouse gas emissions (FAO, 2013). Therefore, it is necessary to look for ways to manage the rice production system more effectively. In paddy rice systems, co cultivating aquatic animals (fish, shrimp, crab, shellfish, and ducks) and rice has been suggested as a way to reduce the risks of environmental pollution/ contamination linked with rice production while simultaneously optimizing the use of land and water resources to provide humans with both grains and meat (Hu et al., 2016; Ahmed and Garnett, 2011). Co-culture systems have drawn more interest recently due to their potential to reduce the stresses associated with environmental and food insecurity.

5.1 Participatory Approaches to Watershed Management

Community Engagement Strategies: Exploring methods for involving local communities in decision-making processes, project planning, and implementation, including participatory rural appraisal (PRA), stakeholder consultations, and community meetings. Exploring methods for involving local communities in decision-making processes, project planning, and implementation is essential for promoting ownership, sustainability, and effectiveness of development initiatives within watershed areas. Here are key community engagement strategies:

a. Participatory Rural Appraisal (PRA): PRA is a participatory approach that involves community members in assessing their own needs, priorities, resources, and capacities. PRA techniques, such as transect walks, social mapping, seasonal calendars, and wealth ranking, enable communities to identify and analyze local issues, strengths, and opportunities. PRA fosters dialogue, builds trust, and empowers communities to take ownership of development processes.

b. Stakeholder Consultations: Conduct stakeholder consultations to engage a diverse range of actors, including community members, local leaders, government officials, civil society organizations, and other stakeholders. Consultations provide opportunities for stakeholders to share their perspectives, knowledge, and concerns, and contribute to decision-making and project design. Stakeholder consultations ensure inclusivity, transparency, and legitimacy in development processes.

c. Community Meetings: Organize community meetings, village assemblies, or public forums to facilitate dialogue, consensus-building, and decision-making among community members. Community meetings provide platforms for discussing development priorities, reviewing project progress, and resolving conflicts collaboratively. They foster social cohesion, collective action, and community mobilization around shared goals and aspirations.

1.1 Introduction to Integrated Watershed Management

Definition and Scope of Integrated Watershed Management: Integrated Watershed Management (IWM) refers to a comprehensive approach to managing the resources and ecological processes within a watershed area in a coordinated and integrated manner. Integrated watershed management represents a holistic approach to managing natural resources within a watershed, with the aim of achieving sustainable development while maintaining ecosystem health and resilience. It involves collaboration among various stakeholders and integrates scientific knowledge with traditional wisdom to address complex environmental challenges. This section aims to provide a clear understanding of what integrated watershed management entails, including its scope and key components.

a. Definition of Integrated Watershed Management: Integrated Watershed Management is a holistic and multi-disciplinary approach that considers the entire watershed as a unit for planning and managing natural resources. It involves the coordinated management of land, water, vegetation, and other resources within a watershed to achieve sustainable development goals while preserving ecological integrity.

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.

Computational crop-disease detection uses machine learning, neural networks and image-processing techniques to provide timely and accurate diagnoses. This chapter presents a complete framework covering agricultural intelligence, disease categories, image acquisition, preprocessing, model training and classification. It examines datasets collected in fields or through drones and considers deployment through mobile devices, cloud platforms and other digital systems. Evaluation measures are introduced for assessing model accuracy and reliability. The chapter also considers limitations caused by insufficient or poor-quality data and variation in field conditions. Future directions include multimodal data integration and explainable models that provide transparent recommendations for sustainable crop protection and improved food security.

Introduction

In recent decades, integrating trees with crops for food and wood production has received considerable attention in both tropical (Garrity et al., 2010) and temperate regions (Palma et al., 2007). The growing of trees in combination with the agricultural crop is a land management practice that farmers have been practicing since the ancient period. Seasonal agricultural/horticulture crops, woody perennial trees (multipurpose), and/or animals are major components of various agroforestry systems. According to Sanchez (1995), the science of agroforestry revolves around four aspects: competitiveness, complexity, sustainability, and profitability, and all of these factors must be balanced in order to produce beneficial outcomes. In fact, agroforestry is significantly assisting in meeting the United Nations Millennium Development Goals (MDGs) such as poverty and hunger eradication, improved health, nutrition, and education for people, gender equality, and environmental sustainability, particularly in developing countries (Garrity, 2004, 2006).

Extreme weather events, such as hurricanes, droughts, and floods, have become increasingly frequent and intense in recent years, posing significant threats to agricultural productivity, food security, and rural livelihoods. The impacts of extreme weather events can be devastating, resulting in crop failures, livestock deaths, and damage to infrastructure and property. Traditional approaches to dealing with extreme weather events have often focused on top-down, expertdriven solutions, where external experts provide advice and guidance to farmers and rural communities on how to prepare for and respond to extreme weather events. However, these approaches have often been criticized for being ineffective, unsustainable, and neglecting the needs and perspectives of local communities.
In recent years, there has been a growing recognition of the importance of interactive community approaches for dealing with extreme weather events. These approaches involve working closely with local communities to identify their needs, priorities, and capacities for dealing with extreme weather events. They also involve empowering local communities to take ownership of their own disaster risk reduction and management efforts, through the provision of training, resources, and support.
A critical component of these interactive community approaches is the development and implementation of Early Warning Systems (EWS). EWS are designed to provide timely and accurate warnings to communities about impending extreme weather events, allowing them to take necessary precautions to minimize losses and protect their lives and livelihoods (Fig.8.1).

Historical perspective: Planting of single crops or mono-cultural agriculture—is a recent invention of the industrial agricultural complex (Hirst, 2019). While unequivocal archaeological evidence is difficult to come by, it’s believed that most agricultural field systems in the past involved some form of mixed cropping. That’s because of even botanical evidence of plant residues such as starches or phytoliths of multiple crops are discovered in an ancient fields (Hirst, 2019). Mixed cropping, also known as polyculture, or co-cultivation, is a type of agriculture that involves planting two or more crop plants simultaneously in the same field so that they grow together. The primary reason for prehistoric multi or mixed cropping probably had more to do with the needs of the farmer’s family and perhaps to avoid crop failure. It’s possible that certain plants adapted to multi-cropping over time disappeared as a result of the domestication process (Hirst 2016).

7.1 INTRODUCTION

Web technology revolutionised information technology and GIS of no exception. Analysis of maps available in internet through remote personal computer is called as web GIS. The term web is GIS synonymous with the term internet GIS. Most of the software vendors in the field of GIS have developed their GIS compatible to internet. The advantage of internet GIS is no need to own a GIS software in the personal computer. Maps available in a web site can be analysed for GIS functions as one could do with GIS in standalone personal computer. Applications of web GIS is not limited. Most of the governmental agencies, business people and others put their data in their server and the data could be analysed spatially for the users requirement. Internet mapping is simple, cost effective and reach vast audience.

Internet GIS is useful in field like civil engineering, government organisations, forestry, environment, goescience, utility planning etc.

The Internet of Things supports continuous monitoring of soil, crops, weather and other field conditions through distributed sensors and connected devices. This chapter proposes an edge-cloud architecture designed to address scalability, heterogeneous data, communication latency and energy requirements. Edge nodes conduct local processing and anomaly detection, while cloud platforms provide storage and run advanced machine-learning models. Applications include crop-status evaluation, irrigation scheduling, pest and disease detection and yield forecasting. Explainable artificial intelligence improves transparency by helping users understand model recommendations. The integrated architecture can reduce response time compared with conventional centralized systems and provides a foundation for scalable, efficient and responsive next-generation smart-farming operations.

Introduction

Famous French postmodern philosopher Jean Baudrillard commented on information once. “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise.” This seemingly paradox observation fits contemporary Internet age perfectly. Internet is a vast repository of information of all kinds and is bursting on its seams. The seeker needs to search with monumental grit and determination to have a full handle on the range of information available for any particular search. A search string for GIS returns more than 1185000 results, in a mere 0.26 seconds. Recommending a comprehensive resource list is not possible as new material gets continually added by way of additions and updates. However for a beginner the list given below will be handy. The flip side is GIS has innumerable resources and a broad range of content. Software vendors, organizations, individuals, universities and companies have provided a wealth of information. The readers are exhorted to make use of the materials to improve their understanding of the subject.

Introduction

Quantum Geographical Information System (QGIS) is an open source GIS environment. The software package is available freely in web https://www.qgis.org/en/site/forusers/download.html) and can be installed in a variety of computer platform e.g. Windows, Linux, Mac OS X, Android etc. Latest information on QGIS is available inhttp://www.qgis.org/en/site/. Basically, the QGIS GUI is divided into five areas: (i) Menu Bar, (ii) Tool Bar (iii) Map Legend, (iv) Map View and (v) Status Bar. QGIS allows users to define a global and project-wise coordinate reference system (CRS) for layers without a pre-defined CRS. It also allows the user to define custom coordinate reference systems and supports on-the-fly (OTF) projection of vector and raster layers. All of these features allow the user to display layers with different CRSs and have them overlay properly. QGIS has support for approximately 2,700 known CRSs. Definitions for each CRS are stored in a SQLite database that is installed with QGIS. The CRSs available in QGIS are based on those defined by the European Petroleum Search Group (EPSG) and the Institute Geographique National de France (IGNF) and are largely abstracted from the spatial reference tables used in Geospatial Data Abstraction Library (GDAL). For Post GIS layers, QGIS use the spatial reference identifier that was specified when the layer was created. For data supported by OGR, QGIS relies on the presence of a recognized means of specifying the CRS. In the following sections, few basic GIS analysis using example spatial data from arid western India is discussed. However, readers are advised to go through different tools and functions available in QGIS after installation of the system in personal computers.

Introduction

Water preceded all forms of life on earth and is believed to be the very origin of all organic life forms. For ages water was forming oceans, separating landmasses and shaping the terrain. It further influenced the emerging atmosphere of the young planet. The first organism formed and began to evolve in the primeval oceans. Mankind entered into the scene several million years later. The history is rather too long and infinitely complex. Variety of models with different degrees of certitude and consensus attempt to cogently explain what could have happened. But in spite of all the differences on the course of evolution, there is a universal consensus that water played a decisive role in the changes.

Across the globe, mankind managed to establish different civilizations with dissimilar cultural and material advancements much later. But then invariably all civilizations considered water holy and water bodies were sacred places. Holy wells, springs, rivers, cenotaphs were held in awe all through human history. The mysterious properties of water are studied from time immemorial. And water indeed is a strange compound! Availability of water for consumption and agriculture has determined the zenith and nadir of surviving and extinct civilizations.

Ancient Indian temples as part of design, have a prominent sacred well or a pond with an associated purana/myth narrating the mystical properties of the theertha / waterbody. It is still is a living tradition. In the west wishing wells survive from remote antiquity.

Computational intelligence is enabling agriculture to evolve into a data-driven, adaptive and increasingly autonomous system. This chapter introduces its concepts, scope and role in smart agriculture by presenting farms as interconnected cyber-physical-biological environments. It discusses artificial intelligence, data analytics and decision-support systems and explains the progression from traditional farming to precision and smart agriculture. IoT, remote sensing, cloud platforms and digital agricultural ecosystems are examined as enabling technologies. The chapter also considers socioeconomic, environmental and scalability challenges. It establishes the conceptual and architectural foundation required to understand how computational intelligence can improve productivity, sustainability, resilience and decision-making in next-generation agricultural systems.

Introduction

A geographic information system or geographical information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics. What goes beyond a GIS is a spatial data infrastructure, a concept that has no such restrictive boundaries. In a general sense, the term describes any information system that integrates stores, edits, analyzes, shares, and displays geographic information. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. Geographic information science is the science underlying geographic concepts, applications, and systems.

GIS is a broad term that can refer to a number of different technologies, processes, and methods. It is attached to many operations and has many applications related to engineering, planning, management, transport/logistics, insurance, telecommunications, and business. For that reason, GIS and location intelligence applications can be the foundation for many location-enabled services that rely on analysis and visualization. GIS can relate unrelated information by using location as the key index variable. Locations or extents in the Earth space–time may be recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a “real” physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.