eChapters

Introduction

To address the escalating food demands of the global population, a transformation in the agricultural sector is imperative (Velten et al., 2015; Lampridi et al., 2019). The current overdependence on finite resources is leading to their overexploitation, largely due to irresponsible usage (Velten et al., 2015; Lampridi et al., 2019). By adopting a site-specific approach, farmers can apply inputs only where they are necessary in the field (Velten et al., 2015; Lampridi et al., 2019). This method can enhance output, decrease labor costs, and mitigate the environmental damage caused by excessive input use (Velten et al., 2015; Lampridi et al., 2019).

Lithological interpretation of igneous rocks involves the identification and description of the different types of rocks that form as a result of the cooling and solidification of magma or lava. Igneous rocks can be classified based on their mineral composition, texture, and the way they formed. Here are some of the common types of igneous rocks and their characteristics:

1. Basalt

Basalt is a dark-colored, fine-grained rock that forms from the rapid cooling of lava. It is typically composed of minerals such as pyroxene, plagioclase feldspar, and olivine.

Basalt is a common extrusive igneous rock formed from the rapid cooling of basaltic lava. It is typically dark in color, ranging from black to dark gray, and has a fine-grained texture. Basalt is composed mainly of plagioclase and pyroxene minerals, with smaller amounts of olivine, magnetite, and other minerals.

Basalt is found in many parts of the world, including the oceanic crust, volcanic islands, and some continental regions. It is also used as a construction material, such as in road paving, because of its durability and resistance to weathering. Additionally, basalt fiber is used in various applications, including reinforcing materials in composites, insulation, and other industrial uses.

Basalt in India: Basalt is a type of igneous rock that is commonly found in India. It is a dark-colored, fine-grained rock that is rich in iron and magnesium minerals.

Abstract

The Car Nicobar Island is one of the several islands in Indian Ocean which was severally damaged by the Great tsunami on 26 December 2004. The great tsunami event caused massive devastation, in the form of loss of life and property in south and south East Asia. The objectives of the study are to assess the changes in the coastal features of the Car Nicobar Island using various indicators such as the tsunami height, run- up elevation, water flow direction, erosion, sand deposition and coastal subsidence. Andaman &Nicobar Islands are the summit of the submarine mountain range lying along the great tectonic suture zone extending from Eastern Himalaya to Sumatra and Lesser Sunda via Myanmar border and Arakan hills. The IRS P-6 satellite data has been found to be very useful in the present tsunami assessment studies. The digital analysis of the satellite data was done using the image processing software (ERDAS IMAGINE 8.4) and DTM to quantify the tsunami height (15 m), run up elevation (2-19 m), inundation (295-1203 m) and coastal responses. The settlements and coconut plantations have been severely damaged in the island, which were assessed using GIS analysis of the pre and post-tsunami satellite data.

Keywords: Tsunami, Wave Height, Run-up Elevation, Inundation, Coastal Responses, Remote Sensing, GIS.

Abstract

The objective of the study is to build a framework of Geo-spatial Information System for effective decision making by empowering all the stake holders with spatial and non-spatial data. The Interface of this information system is developed as a deployable windows application using Map Objects in Visual Basic programming language with MS Access database. The practical utility of the proposed information system was explained with a case study in Aquaculture. This geo-spatial information system provides required information through maps based on spatial and attribute quarries. These dynamic maps help in taking effective decisions.

1.0 Introduction

Computer based information systems proved to be very efficient and effective tools for managing data and information to aid in decision making . FAO (2000) proposed agricultural knowledge and information system for rural development (AKIS/RD) to link rural people and institutions to promote mutual learning and generate , share and utilize agricultural technology , knowledge and information. This serves as vehicle for sharing ideas and principles with various stake holders addressing the problems and seeking solutions to rural development. Although the rural development planning is top-down prescriptive approach , in reality the decisions are made by the stake holders based on their perspectives, knowledge and priorities. Problems particularly related to environmental impacts are location specific and contextual. Geo-spatial technologies may serve as better vehicle to store and share all kinds of location specific data and information. The state of mapping and management of geospatial data changed during the recent years with the diffusion of modern geospatial information technologies such as GIS(Geographic information system), GPS(Global positioning system) and image analytical system for remote sensing(RS) data. Adinarayana et al., (2008) proposed a geographical information and communication technology (Geo-ICT) based information system called Grama Vikas( rural development) to assist the user in analyzing the rural information for rural development, planning . During the recent years there is a paradigm shift in GIS applications for rural development particularly in developing countries, by way of integrating top-down approach with bottom-up approach by the stake holders who stand to be affected by the decisions based on these technologies. Emerging concepts include “ community - integrated GIS”, “GIS in participatory research” and ‘participatory GIS”. (Abbot et al., 1998)

Abstract

The East Coast of Tamil Nadu exhibits a spectacular network of deltas formed by the easterly flowing river systems. The study of raw and digitally enhanced Landsat TM and IRS data shows, that these deltas were formed due to multivariate dynamic processes of fluvial, marine and aeolian actions. The wide fan shaped delta observed northwest and north of Madras city spanning out over an area of 700 sq.km is a feature, standing testimony to the PaleoCauvery river systems.

The regional slope and the overall arcuate arrangement of the crescent shaped lakes in the area and the tentacles of paleochannels show, that it is overall a fan shaped delta and its successive growth towards the sea might have been due to the constant progradation of lobes by continuous reworking of preceding lobes. The depressions in between successive concentric lobes have become the lakes. The absence of any beach ridges in such palaeo deltaic regime suggests that, it would be a sub aerial delta only. The overall arcuate shape of the shoreline in between Madras city in the south and Pulicat lake in the north is due to this fan delta. This feature once again symbolises the active river dominated delta. The Palar delta is a ‘cuspate’ one. The Ponnaiyar river has developed a linear delta, along a narrow depression found in between Tertiary uplands. While the avulsion phenomenon indicates the ancient shoreline near Villupuram, the east-west trending micro sandy uplands within the delta suggest, that it is a tide dominated delta.

Abstract

Soil quality assessment has been recognized as an important step towards understanding the long term effects of land management practices within agricultural watersheds. Potential soil quality issues include continued high rates of erosion, losses of organic matter, reductions in soil fertility and productivity, as well as chemical and heavy metal contamination. This case study addressed the spatial variability of soil properties and its quality at the watershed level using geostatistical methods. Soil samples from the 0- to 20-cm depth were collected on a 100- by 100-m grid across an 88-ha watershed at Sakaliseripalli village in the Nalgonda District in Andhra Pradesh State, India. Geostatistical analysis showed that most of the soil parameters were moderately spatially dependent. An assessment framework, that included a minimum data set, linear scoring technique, and additive indices, was used to evaluate the soil quality index (SQI). Through principal component analysis, cation exchange capacity, exchangeable Na percentage, DTPA-extractable Zn, available P, available water, and dehydrogenase activity have been emerged as the most important indicators for evaluating soil quality. A kriged map of SQI was prepared for the watershed. The SQI was found to be higher in irrigated systems than under rainfed conditions. In the study, potential soil loss calculated using the Universal Soil Loss Equation and crop yield were identified as the quantifiable management goals; the results indicated that soils having higher soil quality indices were more productive and less erosion prone.

Keyword : Principal Component analysis, SQI, Watershed, Geostatistics, Potential soil loss

Introduction

Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

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.

1. Introduction
Fish and fisheries remains to be an important sector for Indian economy providing food, nutrition as well as employment to a large share of the population. Initiating with a production of merely 7.7 lakh ton of fishes in 1951-52, the country was on its way of increasing the production steadily and has now attained the phenomenal success of producing 162.5 lakh ton during 2021-22. Of this total amount, 41.3 lakh ton has been obtained from marine fisheries and 121.2 lakh ton from inland resources (Anon, 2022). For the inland fisheries production, the contribution of fish culture sector was much lower than that from capture fisheries for several decades during the initial phases. However, the production of culture fisheries maintained a consistently increasing trend and has now surpassed in magnitude the production from capture fisheries sector (Fig.1). With the potential areas for fish culture being gradually used up for different purposes, maintaining such high production rates in coming days will be possible only through optimum management of various factors which govern the productivity of the ponds. Among such factors, one important aspect is nourishing the quality of pond soil and water.

1. Introduction
Soil salinity is a global threat which affects crop production and poses challenges to sustainable development in various regions worldwide. Worldwide, salinity and sodicity impact 20% of cultivated land and 33% of irrigated land, respectively (Kumar and Sharma, 2020). Global map of salt–affected soil (SAS) indicated that 0.424 billion hectares of upper soil layer (0–0.3 m) and 0.833 billion hectares of subsoil layer (0.30–1 m) are salt-affected (FAO, 2021) and by 2050, salinization will degrade 50% of agricultural land (Wang et al., 2003). In India, SAS currently occupy 2.1% of total geographical area. Among the SAS, 2.96 million ha is saline and 3.77 million ha is sodic (Narjary et al., 2024). Due to salinity and sodicity, yearly 16.8 million megagram of agricultural produce are wasted in India, costing of ? 230.2 billion (Sharma et al., 2015).
Salt–affected soils are a significant concern in several regions of India. These soils contain high level of sodium salts (NaCl, Na2SO4, NaHCO3, Na2CO3) which adversely affect soil fertility and crop productivity. Here are some areas in India known to have salt–affected soils:
1. Indo–Gangetic Plain: The Indo–Gangetic Plain, spanning across states like Punjab, Haryana, Uttar Pradesh, Bihar, and parts of West Bengal, is prone to salinity issues. Over–irrigation, poor drainage, the presence of salt containing parent materials in the lithology and use of saline irrigation have contributed to soil salinity.
2. Coastal Regions: Coastal regions in India, such as parts of Gujarat, Maharashtra, Tamil Nadu, Andhra Pradesh, Karela, Odisha and Andaman and Nicobar Islands experience salinity problems due to sea water intrusion and the presence of coastal saline soils. These soils are affected by high levels of sodium chloride and other salts, making them unsuitable for cultivation of most arable crops.
3. Arid and Semi–Arid Regions: Arid and semi–arid regions, including areas in Rajasthan, Gujarat, and parts of Madhya Pradesh, face the risk of soil salinization due to low rainfall, high evaporation rates, and poor– quality irrigation water. Waterlogging in these regions can also lead to soil salinization.
4. Irrigation Command Areas: Certain irrigation command areas in different states, including Punjab, Haryana, and Uttar Pradesh, have witnessed salinity problems as a result of improper water management, excessive irrigation, and inadequate drainage infrastructure.

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.

Introduction

Agriculture contributes to 29.93% of India's GDP with a share of 27% of the world's agriculture production. India is ranked 6th in the world. In Rajasthan significant portion of economy of is agrarian. The agriculture sector of the state accounts for 22.5% share of agriculture in GSDP which is estimated to 18.39% in 2009-10. About 80% of the total population resides in rural area and largely dependent on agriculture as the source of their livelihood. The hot arid region of western Rajasthan occupies 61% of the total geographical area and 39.51% population of the state. During the last four decades the burgeoning human and livestock population and increased societal demands for land based products led to  significant expansion and intensification of agriculture by putting even marginal lands under plough, excessive exploitation  of ground water and importing of surface water for irrigation.

Abstract

Customized application development can deliver/fulfill the specialized and specific needs of the users as well as it provides user- friendly environment to the users. There have been applications developed in the past but now applications developed and coupled with the advantage of GIS technologies for various data-handling, spatial analyses and visualization under variable inputs and spatial scales is an innovative approach developed during recent years.The present work encompasses Spatial Agricultural Information and Decision Support System having five modules - Agro-socio-economic-information, Bio- Physical Information, Crop’s Information & Management, and Agriculture DSS Module. This system, developed in a GIS framework, allows managers and planners to rapidly assess land attributes and link these attributes with maps and related infrastructure. Hence, it can facilitate efficient use of existing resource information. The primary database of the software has a Database Update feature which can be utilized by the user using the software to integrate own data into the primary database of the software. With this integration, the software adapts to new data entered and will enable this data to be visualized in GIS platform. The software has been designed keeping in view the requirements of the departments like Department of Agriculture, Department of Land Records etc. In comparison with the commercial GIS applications, the commercial applications are not cost effective if the primary requirement is visualization enabling decision making and basic spatial analysis. The most features available on such software remain un-utilized or they cannot be used without having GIS knowledge. This application is suitable in such scenarios, providing a simple customizable platform for GIS visualization. This software application fits in and serves the line departments and in a way promoting the GIS in the mountainous terrain as it requires very basic or no GIS skill set to operate.

The climate, the past weather event that existed over larger region and covering long years of more than 30 years is very important for the emergence and successful sustenance of an organism including human kind. Normally scientists talk about environment and this includes not only the soils but also the climate of the particular region. Based on this, during 1989, the Planning Commission of India divided the entire India in to 15 agro-climatic zones based on physiography and climate. This division is only for planning purposes. Subsequently under NARP, the Indian Council of Agricultural Research (ICAR), New Delhi in consultation with the Planning Commission, requested all State Agricultural Universities (SAU) to divide their State in to different sub climatic zones considering soils, temperature and other weather parameters preciously with in the major agroclimatic zone of India in which the concerned State falls. Accordingly, each SAU has divided their State into different agro-climatic sub-zones and this resulted in 129 agro-climatic sub zones in India and limited to 127 and these all fall within 15 agro climatic zones of India as done by the Planning Commission of India.

The National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Nagpur not satisfied with this division, considering soils, temperature, rainfall, vegetation and all factors responsible for ecosystem (bio-climate, length of growing period, moisture index) divided the whole India in to 20 agro-eco regions by 1992 and revised preciously again by 2015 (Mandal et al., 2016) and the revised approach is being considered as scientific tool and taken for micro-level planning in India now and those are discussed at this chapter.

Introduction
Having meticulously explored the foundations of crop-weather interactions, the development and validation of crop growth models, the wisdom embedded in Indigenous Technical Knowledge, and the systematic organization of Crop Weather Calendars, we now arrive at the culmination of this knowledge synthesis: the Agroadvisory Based on Weather Forecast. This chapter delves into the critical process of transforming raw meteorological data and scientific understanding into actionable, timely, and location-specific advice for the farming community. An agroadvisory, at its core, is a targeted recommendation provided to farmers, guiding them on various agricultural operations by integrating current weather conditions, short- to medium-range weather forecasts, and the specific needs of their crops at different growth stages. It represents the final, practical output of complex agro-meteorological research and forecasting efforts, designed to empower farmers to make adaptive decisions that can significantly impact their yield, resource efficiency, and resilience against climatic shocks. In the upcoming sections, we will comprehensively examine the multi-faceted nature of weather-based agroadvisories. Our exploration will detail:
• The Conceptual Framework: Understanding the underlying principles and objectives of these advisories, emphasizing their shift from reactive responses to proactive planning.
• Essential Components of an Agroadvisory: Breaking down the key pieces of information typically included, such as weather forecasts (temperature, rainfall, wind, humidity), crop-specific phenological stages, potential risks (pests, diseases, extreme weather), and precise recommendations for farm operations (sowing, irrigation, fertilization, pest control, harvesting).

Abstract

Remotely sensed data have become the primary realistic source for natural resources and biomass estimation, since last 30 decades. A summary of earlier research on remote sensing and GIS based biomass estimation approaches and a discussion of existing issues prompting biomass estimation are very valuable for further improving biomass estimation performance with the integration of geographical information systems. This paper discusses the recently most accurately alternative research methods to integration of remote sensing and GIS applications for identification, characterization and analysis of biophysical characters. The remote sensed data and geographical information systems using the accurate assessment of natural resource and management for the enhancement of biomass production and estimation. There is not any other option for the perfectly biophysical and natural resources estimation in small to large area. The earlier research has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest and mixed traditional cropping systems and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research expertise is needed to focus on the integration of remote sensing, GIS& GPS, the use of multisource data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties caused by different stages of the biomass estimation technique and applying strength of mind to reduce these uncertainties are critical.

Keywords : Integrating, Remote Sensing, Bio-physical, Resources, Environment

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

Analysis of Variance (ANOVA) is an important statistical technique for separating variation attributable to different sources in an experiment. The chapter explains how total variation can be partitioned into controlled and uncontrolled components and how experimental error is used to assess treatment differences. The F-test provides the basis for testing the homogeneity of several treatment means simultaneously. The chapter discusses the assumptions underlying ANOVA, including independence, normality, additivity, and homogeneity of variance. It also introduces fixed- and random-effects models and explains their relevance to experimental analysis. Forestry and agricultural examples demonstrate the application of ANOVA for interpreting treatment and experimental effects.

Key words : Integrated Watershed Management (IWM), GIS, DTM, Watershed Delination, Water Quality Modeling, RS, DSS, Ground Water Information System (GWIS).

Introduction

The availability of land and water resources has a major effect on the economic and ecological aspects of life everywhere around the world. As a result, the need to develop better tools for efficient land and water resources management has become very vital. For quicker, reliable and precise results, it is necessary to use the geospatial tools and techniques such as Global Positioning Systems (GPS), Geographic Information Systems (GIS) and Remote Sensing (RS) image interpretation and analysis techniques to manage the natural resources. The application of these tools and techniques not only facilitates timely and repetitive analysis of all the topological features, but also assists in development of Decision Support Systems (DSS) to generate alternative scenarios for optimal use of the natural resources in the long term. The optimal use of these resources through integrated management of land water and vegetation leads to enhanced production and productivity of food, fodder, fiber, fuel, fish, flower and forest resources. Moreover, the holistic management of the resources to produce these outputs is plausible and manageable within the watershed systems, which are hydro geological units from which the runoff resulting from precipitation flows past through a single outlet to downstream locations. The systematic management of land, water, vegetation and livestock resources within the watershed systems is termed as Integrated Watershed Management (IWM). Broadly, IWM can be defined as a multidisciplinary, holistic way of protecting and managing a watershed’s natural resources to enhance biomass production in an eco-friendly manner. The watershed is viewed as a hydro-geological complex and dynamic ecosystem in which natural and anthropogenic processes occur and interact resulting in generation of runoff at the watershed outlet.

Abstract

This study was conducted at Directorate of Cashew Research (DCR) Puttur, D.K., Karnataka during 2007-12. In this study, a methodology was standardized to find the area under cashew, barren areas and other areas for expanding cashew cultivation with remote sensing. It was found that the images pertaining to the month of November was best for classifying cashew since the colour of cashew leaves changes during that period. Also, the LISS III images were found to be good for identifying cashew. It was not possible to differentiate between cashew seedling area and cashew graft area using LISS III or LISS IV images. A spatial database of soil moisture was prepared for the irrigation scheduling in cashew and a spatial database of major nutrients was prepared for the site specific nutrient management in cashew gardens. A spatial database of land slope and stream orders was prepared using GIS and suitable soil and water conservation interventions were suggested for the selected area.

Keywords: Cashew, GIS, remote sensing