eChapters

Introduction

Precision Farming (PF) revolves around the idea that any agricultural land contains wide spatial variations in soil types, nutrient availability, and other important factors. If these variations are not taken into account, then there may be a loss in productivity. Precision farming is one of the most scientific and modern approaches to sustainable agriculture that has gained momentum in 21^st century. Consider the situation: ‘A farmer goes to his/her field with his/her Global Positioning System (GPS) guided tractor. The GPS senses the exact location of the tractor within the field. It sends the signal to a computer on the tractor that has a Geographical Information System (GIS), storing the soil nutrient requirements map in it. GIS, in consultation with a Decision Support System, decides what is the exact requirement of the fertilizer for a given location and then commands a variable rate fertilizer applicator, attached with the tractor, to apply the exact dose at that precise location. All this is done within a second, before the tractor moves further.’ This may sound like an excerpt from a scientific fiction, but this is what precision farming means to the large growers of the United States or European countries and day is not far off when it will be feasible for large Indian farmers as well.

PF refers to a developing agricultural management system that promotes variable management practices within a field according to site or soil conditions. Within a short span of time, this concept has been described with various names viz. farming by soil; farming soil, not fields; farming by the foot; spatially prescriptive farming; computer aided farming; farming by computer; farming by satellite; high-tech sustainable agriculture; soil-specific crop management; site-specific farming; and site-specific crop management.

Key words : Wasteland Reclamation, Poverty Indicators, Food Insecurity, Alternate Livelihood Systems, Rural Infrastructure Index.

Introduction

The countries of Asia and the Pacific region accounts for nearly two-thirds of the chronically poor and undernourished in the world. FAO estimates indicate that, by 2010, Asia will still account for about one-half of the world’s malnourished population (FAO 2000). In Asia, poverty has mainly been rural phenomenon and nearly three-fourth of the poor live in rural areas, with large majority of them dependent on natural resources for employment and income. South Asia, which had a poverty incidence of 43 percent (or about 520 million people), contributed about 40 percent of the world’s poor. Development of natural resources thus offers a potentially enormous means of poverty reduction.

In developing countries poverty and environmental deterioration are often viable in proximity to each other, and have led many to infer that a two-way causality exists between human and environmental degradation. Environmental degradation usually occurs when production and consumption activities of growing population irreversibly weaken nature’s recycling capabilities. These economic activities are also attributed to the development of markets, the advent of modern technology, and the spatial integration of inaccessible areas to market systems. In many developing countries, however, environmental degradation such as soil erosion, deforestation and pollution are most visible around poor settlements, leading some policy makers to highlight the direct links between poverty and the environment.

One has to recognize that both poverty and the environment are descriptions of states of human and natural resource attributes and cannot be reduced to simple one-dimensional cause-effect relationships. Apart from conceptual difficulties in modeling linkages, another handicap has been the absence of adequate or reliable data sets on poverty and environment characteristics. The challenge for operational research is exploring how circumstantial evidence and inductive logic can be used to explain the nature of interactions between the two states. Based on nationwide wasteland statistics vis-à-vis state and district wise poverty estimates available with the concerned agencies, this study is one such attempt, and evaluates the incidence of poverty with the backdrop of natural resources degradation and role of institutional interventions with illustrations from India.

Abstract

The Pudukkottai district located in the southeastern tip of Tamil Nadu, India provides a complex geological setting with three litho-tectonic blocks membered by the hard Crystalline rocks of Archaeozoic-Proterozoic Era in the west,Mio-Pliocene Sandstone/Cuddalore Sandstone in the centre and Quaternary sediments in the east. While the western and central blocks are separated by a well-defined NNE-SSW trending fault, the central Mio-Pliocene Sandstone block is cut into two parts by a N-S fault with western static block and the eastern uplifted counterpart. Besides the above, the various drainage anomalies viz; Annular, Deflected and Eyed drainages and compressed meanders interpreted using the raw and digitally enhanced satellite multispectral data show that the N-S oriented faults with oscillations from NNW-SSE to NNE-SSW, NE-SW and E-W orientations are tectonically active in the area. The integration of these active faults with known geothermal springs and the maxima axes drawn from the contours deducted from the temperature of groundwater measured during the predawn hours from the wells revealed that the N-S and NE-SW faults are the probable locales for geothermal resources in the study area. Thus the study demonstrates a newer methodology for locating the geothermal prospects.

Abstract

The Tamil Nadu is one of the leading states in groundwater development. Since the surface water resources have been fully exploited for irrigation, the groundwater is massively mined for agriculture, domestic and industrial purposes. The paper provides a synthesis of whole groundwater related issues including basin wise groundwater conditions, the aquifer types, geogenic water quality and quality deterioration due to anthropogenic activities, need for recharge, the utility of Remote Sensing & GIS etc. The paper has come out with set of recommendations as the concluding remarks.

Introduction

Geomorphology is the study of earth’s landscapes over time and space, and involves an analysis of both its surface features as well as the processes responsible. Historically, the subject evolved through monumental studies on supposed evolution of the landscape, based on intuitive knowledge of the researchers on landscape response to endogenic and exogenic forces, as well as visions of possible changes over a time scale of centuries to millennia or more. Too much emphasis on the study of possible landscape evolution in an era of little technological back-up facilities meant that the discourses were largely philosophical in nature, with fewer measurable parameters to substantiate the concepts or the reasoning, although reflecting deep understanding of the researchers involved. As older concepts began to be confronted with newer ones, and applied aspects started receiving greater attention, a need was felt for quantification of the features and processes. This was more so for an understanding of the fluvial landscape, although aeolian and other process-based studies also gradually incorporated quantitative analysis as a means of understanding the landscape. Despite the awareness, lack of adequate equipment hindered the progress of quantification in the pre-World War II period.

Abstract

Natural resources and agricultural production processes vary spatially. Capturing the spatial variations in data and knowledge of natural resources and production processes has been a major challenge for efficient and sustainable management of agricultural production systems. Geographical Information Systems (GIS) provide an effective platform for storing and mapping spatial data and modeling the relevant processes. Huge volumes of geospatial data on specific themes such as soils, water, climate, and other natural resources, are being collected by public and private agencies. However, currently it is difficult for most users to access the data and integrate them into a GIS platform to derive useful information and knowledge for specific applications. There is a need to create geospatial data and knowledge systems that can provide efficient, on-demand and remote access of spatial data to users to enable development of a variety of GIS applications for agricultural and natural resources management. The design of a prototype geospatial library to provide web-based access to geospatial data, services, and learning and knowledge resources that can support agricultural and natural resources management is presented. The design is scalable and sufficiently general to allow a range of agricultural and natural resources management applications across different spatial scales.

Keywords: Agriculture, GIS, Geospatial library, Knowledge Management, Natural Resources

Introduction

High degree of spatial variability in soil properties is observed at different scales. This variability is generally originated from the combined effect of physical, chemical or biological processes operating with different intensities at different scales (Goovaerts 1998). Knowledge of this spatial variationof soil properties is important in several land management applications e.g. variable rate application of nutrient and water in agricultural production system, land suitability classifications, modeling water balance components at landscape scales, precision farming etc. The concept of ‘management zone’ was also evolved in response to this large variability with the main purpose in efficient utilization of agricultural inputs with respect to spatial variation of soils and its properties (Franzluebbers et al. 1996, Atherton et al. 1999, Malhi et al. 2001). Therefore, an appropriate understanding of spatial variation of soil properties is essential. The most important way to gather knowledge in this aspect is to prepare soil maps through spatial interpolation of point-based measurements of soil properties. Here, a case study is discussed where soil water retention at two critical levels e.g. field capacity (FC) and permanent wilting point (PWP) was spatially characterized through geostatistical approaches. Because these two soil moisture contents guide farmers to apply right amount of irrigation water at right time. The study was carried out at the experimental farm of ICAR-Indian Agricultural Research Institute, New Delhi, India (28°37′-28°39′ N, 77°8′ 30″-77°10′30″ E, 217-241 m above mean sea level). A detailed discussion of the study is available in Santra et al. (2008)

Introduction

Soil organic carbon (SOC) content plays a key role in maintaining soil fertility in agricultural farm. At present, there has been great interest to reduce the atmospheric CO₂ level through a chain of increasing vegetation carbon pools first and then to store them as soil carbon pool. 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. Average SOC content is considered in most soil carbon pool calculations in spite of its large spatial variation in landscape, and thus leads to inaccurate estimate of SOC stock for an area. Therefore, reliable estimates of SOC pools and their spatial variability are essential to establish the soil carbon sequestration programmes at different landscape scales. Here, a case example of assessing the spatial variation at a typical agricultural farm is discussed. The experimental field was located at Badoda village of Jaisalmer district, Rajasthan covering an area of about 76.12 ha. The farm lies between 265^o2′30″-26°53′30″ N and 71°12′15″-71°13′15″ E (Fig. 24.1) and 20 km away from Jaisalmer city in eastward direction. Elevation of the farm ranged from 232 m to 248 m above mean sea level with an overall slope from south to north. Among total area of the farm, 85.38% area was under cultivation in four main blocks: block A, B, C, and D whereas rest area was under grassland.

Abstract

Any naturally occurring phenomena varies both in space and time. This variability is a result of various natural process and hence deterministic. Geostatistics is a tool to help us to characterize spatial variability and uncertainty resulting from imperfect characterization of the variability. Geostatistics involves the theory of regionalized variables, which dates back to the early fifties and includes concepts of random function and stationarity. Geostatistical mapping can be defined as analytical production of maps by using field observations, explanatory information, and a computer program that calculates values at locations of interest. There area number of spatial prediction models depending on the amount of statisticsinvolved in the analysis. Kriging is a generic name adopted by the geostatisticians for a family of generalized least- squares regression algorithms and various kriging algorithms viz., punctual kriging, block kriging, co-kriging and universal kriging are explained in this paper. Applications of geostatistics can be found in many varied disciplines ranging from the geology to soil science, hydrology, meteorology, environmental sciences, agriculture and even structural engineering and some of its applications are summarized in this paper.

Keywords :Geostatistics, spatial variability, kriging, model

Abstract

The sustainable management of plant genetic resources is of great concern as increasing population and rapid technological strides are putting enormous pressure on this country’s nutritional and food security. Management of plant genetic resources at national level generates and uses enormous data and analysis of these data is crucial to the effectiveness of PGR management process and can add significantly to the value of genetic resources. A GIS is a database management system that can simultaneously handle data representing spatial objects and their attribute data. GIS allows us to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. It can play an important role in the management of large and complex PGR datasets. The article analyses the potential uses of GIS in plant genetic resources management in India covering various activities such as germplasm exploration and collection, characterization and evaluation, plant health management, documentation and conservation.

Key words : plant genetic resources, germplasm, conservation, documentation, DIVA-GIS,

Abstract

The study area falls in the southern part of the Indian Peninsular comprising hard crystalline rocks of Archaeozoic and Proterozoic Era. In the present study, the GIS based 3D visualizations of gravity, magnetic, resistivity and topographic datasets were made and therefrom the basement lineaments, shallow subsurface lineaments and surface lineaments/faults were interpreted. These lineaments were classified as category-1 i.e. exclusively surface lineaments, category-2 i.e. surface lineaments having connectivity with shallow subsurface lineaments and category-3 i.e. surface lineaments having connectivity with shallow subsurface lineaments and basement lineaments. These three classified lineaments were analyzed in conjunction with known mineral occurrences and historical seismicity of the study area in GIS environment. The study revealed that the category-3 NNE-SSW to NE-SW lineaments have greater control over the mineral occurrences and the N-S, NNE-SSW and NE-SW faults/lineaments control the seismicities in the study area.

Abstract

Runoff from rainfall is an important component of the hydrological cycle. Estimation of runoff is critical for the design of hydrological structures and drainage systems in watersheds. Different soils, land use, and water management practices affect runoff differently. In real watersheds, land use, soils and weather conditions vary spatially over the geographical area of the watershed leading to spatial variations in runoff. Also, most watersheds form a part of larger drainage basin or a large watershed consisting of several such subwatersheds. Each subwatershed is hydrologically connected to the other subwatersheds of the basin. A comprehensive GIS based decision support system (DSS) for estimation of runoff that includes the spatial variations in rainfall and natural resources is presented. The DSS is developed as a deployable application by integrating independent GIS layers of watershed features created in ArcGIS with MapObjects in VB, for a case study watershed - the KK3 watershed in Mahbubnagar district of Andhra Pradesh, India. The DSS generates thematic maps of spatial variations in runoff on individual rainy days for the subwatersheds.

Keywords : GIS, GUI, Land use, Rainfall, Remote Sensing, Runoff, Soil, Sub-watershed, Watershed 228 Geospatial Technologies for Natural Resources Management

INTRODUCTION

Coastal zones are always dynamic and active, because of the multivariate morpho tectonic and morpho dynamic processes, viz: tectonic, fluvial, marine and aeolian which act in varying degrees and duration along the coasts. As these geomorphic / morpho dynamic processes act in varying permutations and combinations in different segments of the coasts, the land - ocean interactive phenomenon vary greatly in time and space, which in turn frequently and dynamically alter the coastal environment, and cause fragility to the coastal ecosystems. Further, as the coastal zones act as the junction between the Continents and the oceans, even mild earth movements exhibit exemplified expressions in land - water distribution pattern along the coasts. The green house effect and the much apprehended sea level rise and the disasters like cyclones also contribute in their own way in accelerating such land - water distribution / land - ocean interactive phenomenon.

Abstract

The emergence of Remote Sensing and GIS technologies has substantially eased the problems of groundwater targeting in hard rock aquifer systems. Proper utilization of these can provide more vital information in managing such scarce resource. In this context, a new technique of GIS based visualization of groundwater levels of different periods (1985- 1990 -1995) was attempted for a part of Western Ghats hill-plain region of Tamil Nadu and various groundwater features viz: ridges, valleys, domes and basins were interpreted. The integration of such groundwater features of different periods and their analysis with structural features provided some newer information on geosystem processes and their interface dynamics with hydrosystems, from which, certain strategies were suggested for better groundwater management in hard rock areas.

Topology building

Boolean operations

Query and model building

Customization

Introduction

At human scales, the world consists of objects, events, processes, and a background environment. Cognition is about knowledge: its acquisition, storage and retrieval, manipulation, and use by humans and other intelligent creatures. Cognition includes sensation and perception, thinking, imagery, reasoning and problem-solving, memory, learning, and language. Cognitive structures and process are part of the mind, which emerges from a brain and nervous system inside of a body that exists in a social and physical world. Spatial cognition deals with the cognition of spatial properties of the world, including location, size, distance, direction, shape, pattern, movement, and inter-object relations. GIS design will be largely influenced by spatial cognition.

8.1 INTRODUCTION

To implement a good GIS project, a well structured project design and management are essential. Well planned project will give fruitful result within the stipulated time. Project not completed in time will not incur more cost and also the time delay will add negative value to the project. Hence all the elements in project planning and implementation should be considered to have a desired result. Some of the issues are also present in GIS and they will be rectified in the next decade.

8.2 PROBLEM IDENTIFICATION

Any project is coming into reality due to necessity. If water scarcity is more in a hard rock region with sufficient rainfall, it is important to augment groundwater resource using rain water harvesting structures. It is important to build water harvesting structures like check dam, percolation pond in suitable locations so that groundwater recharge will be more. Hence a problem is identified and the project work may be carried out by a government or private organisation and the result is given to the government to implement the project. Likewise many area may require a project in GIS environment for planning. Once a project is created any development taking place can be added and GIS database is updated.

3.1 INTRODUCTION

Geographic Information System handles large volumes of data. It is important to know the nature of data, instruments from which the data is collected and represented in computers. GIS data is either spatial or aspatial. Data is an important component in GIS. Digital data are available for some projects. but for many projects, input data may not be available. Now-a-days, data is supplied by many government, private organizations and also available on internet. If data for a particular project is not available, then the user has to collect the data from satellite imageries, GPS and other instruments and maps from various sources.

Spatial Data

All locations in the earth surface are spatial data. Location of a city, location of a river, location of a lake are represented in earth by their latitude or longitude in global coordinate system or as x,y coordinate in Cartesian coordinate system. Spatial data consists of location, shape and size in earth. A village boundary is represented as a polygon in map. The village is having certain shape of irregular polygon with definite area. As all these are mappable , they are called as spatial data. But name of the village, revenue of a village and description about a village and called as non-spatial or aspatial data.

Abstract

Geographical Information Systems (GIS) application in plant genetic resource management allow capture, manipulate, process and display of georeferenced data integrated with genetic resource database for operations such as query and statistical analysis, visualization and geographic representation. GIS based several spatial tools have been developed that model species’ ecological niches finding a relationship between crop or variety with a set of environmental parameters and useful for planning exploration, evaluation trials, in situ and ex situ conservation sites. GIS technologies have direct practical application in elucidation of genetic, ecological and geographic patterns in the distribution of crops and wild relatives; mapping of richness, diversity, distribution of useful traits and, location of areas with complementary diversity; gene bank information management and strategies for future explorations and gap analysis; selection of suitable accessions/sites for ex situ evaluation; searching germplasm for abiotic stress tolerance and developing core collections. With the increasing availability of high- speed computers and software development in the area of GIS tools specific to PGR management increasing trend in application of these is expected. Apart from ecogeographic survey for locating diversity and germplasm collection and design, management and monitoring of in situ reserves, introduction of new varieties, and development of core collection, locating gene donors for biotic and abiotic stresses are the areas to be benefited with the GIS application.

Keywords: GIS, genetic resource, distribution, diversity, core collection, gap analysis, mapping

Introduction

Rainfall is one of the climatic variables that varies very highly spatially and temporally. Understanding this micro-level variation is a crucial indicator for planning future water requirements and preparing for management strategies. Numerous researchers have delved into the distribution, variability, and trends of rainfall across diverse spatial scales, ranging from global to regional and basin levels. Nair et al. (2014) analyzed the rainfall trend and seasonality index for Kerala state in India over the last 100 years. The rainfall trend was found to be decreasing significantly in almost all the regions of the state. Nischitha et al. (2013) provide insights into the spatial and temporal variability of daily monsoon rainfall in the Tunga and Bhadra River basins in Karnataka state. Paradkar et al. (2019) evaluated the pattern of weekly rainfall by using statistical parameters such as mean, standard deviation, skewness, and kurtosis for five stations in Pratapgarh district.

Geographical Information System (GIS) proved to be an effective tool in handling information and analyzing rainfall patterns, providing visual representations, and facilitating the decision-making process (Kadir et al. (2016) and Rao et al. (2017)). Anand et al. (2020) analyzed the long-term spatial and temporal trends of rainfall in the Lower Bhavani basin in Tamil Nadu, India using GIS and statistical methods. Earls et al. (2007) compared different interpolation methods such as Inverse Distance Weighting, Spline, and Kriging (ordinary and universal) to generate a 30 m representation of the rainfall data. Spatial interpolation techniques perform differently and their evaluation should not rely solely on quantitative assessment. Wu & Hung (2016) compared the performance of three spatial interpolation techniques i.e., IDW, kriging, and spline using quantitative assessment, 2D visualization, and 3D visualization. IDW and kriging performed similarly in terms of quantitative assessment, while spline performed worse. IDW and spline performed similarly in 2D visualization, while kriging showed different behavior. Kriging was observed to be an inexact interpolation in 3D visualization, while spline tended to create extreme values along the edges of the study area. Some studies discovered that the accuracy of Kriging in the study area is higher than that of IDW or Thiessen interpolations (Earls et al. 2007; Song et al. 2008; Chen et al. 2010). GIS applications have the capability to integrate spatial data and non-spatial data like rainfall and to visualize, model, and analyze how rainfall distribution varies across space and over time, providing insights into climate change, water resource management, agricultural planning, and disaster management. The present study aims to analyze annual rainfall data pertaining to Kerala state in the GIS environment.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.