eChapters

ABSTRACT

Using a hand auger of 2 m length, upper, middle and lower core sediment samples of seventy nine in number have been collected from the Vaippar river, its tributaries and Vaippar beach in order to understand the role of Vaippar river and its tributaries in making up the Vaippar beach placer deposits. The heavies are found to be concentrated abundantly in fine and very fine size grades of all the samples collected from both the environments. The average heavy mineral concentration varies from 3.13 to 17.40 % in river bed while it is 6.19 – 64.82 % in the beach. In river bed, the pattern of concentration of heavies in upper, middle and lower core samples does not show any characteristic uniform pattern and inferred that it is controlled by the grain size of the parent rock, supply of sediments from the source, geomorphological features of the river and hydrological characters of running water. In the beach, the concentration of heavies are effected mainly due to the winnowing action of waves. However, a phenomenal increase in the heavy mineral concentration of beach samples might be due to the supply of sediments from other source besides the Vaippar river.

ABSTRACT

Mapping and exploration of iron ore deposits in parts of Salem District is carried out based on the detailed field work and the interpretations of IRS-1C PAN data in the scale of 1:12,500 and aerial photographs (NRSA, Scale 1:4,000). The study results that the major rock types of the study area are banded magnetite quartzite, garnetiferous pyroxene granulite, amphibolite, garnetiferous pyroxenite, charnokite, talc tremolite, actionalite and dunite rocks. The Banded Magnetite Quartzite’s (BMQ) occur as enclaves within the hornblende gneisses at several parts of the study area. Topographically, the rocks constitute small low-lying linear ridges at some places it is found to be closely associated with garnet pyroxene granulites. A remarkable feature about the mode of occurrence of magnetite quartzite is its linear nature and the physical form, which could be described as a “Band”. All the bands possess an ENE-WSW strike and the dip is found to be steep (above 70 degrees) towards north. The rock in the hills of Godumalai, Vellalagundam, and Singipuram are highly deformed and contact with garnetiferous pyroxene granulite and hornblende gneisses. In hand specimen, the magnetite quartzite rock exhibits fine banding. In general no variation in grain size or thickness of banding could be seen megascopically. However, occasional increase in size of magnetite might be possible as seen from the coarser magnetite pebbles strewn in the channels of the area. There are two types of folds observed namely F1 and F2. The axial plane trace of the F1 varies in trend from WNW-ESE to WSW – ENE. The joints are traced in the directions of NNW-SSE to NNE – SSW and WNW – ESE to ENE-WSW. The interpretation of lineaments shows their trend directions of NNE-SSW, NE-SWand NNW-SSE. The above said work has carried out through detailed field work and interpretations using by IRS-1C PAN data in the scale of 1:12,500 and aerial photographs of NRSA, in the scale of 1:4,000 for the study area.

ABSTRACT

The upper Agniar and Vellar river basins principally consist of three major rock typesnamely pink granite, gneiss and charnockite. The major geochemical pattern of these igneous rocks when studied indicate that charnockites have a trend of low Mg2+ and high Ca2+ + Na O to high K+ end, whereas the same is vice – versa for hornblende biotite gneiss and also for granite gneiss. The pink granite has an intermediate trend between the charnockite and hornblende biotite gneiss. The value of SiO2 in all the rock types was found to increase with the increase of K2O, Na2O and Al2O3. Further, a decrease in the values of MgO, Fe2O3, TiO2 and CaO was noticed with an increase in SiO2. On the basis of the distribution pattern of these elements, the origin of the igneous suites in the above two basins was inferred.

ABSTRACT

The disposal of industrial effluents in rivers causes groundwater contamination. It is necessary to assess how these contaminants are likely to migrate in the groundwater system. In the present study, an attempt is made to study the migration of contaminants in the groundwater of Palar River basin in Tamil Nadu. Simulation of the contaminants was carried out by using numerical methods. The finite difference computer code MODFLOW (Modular 3-d finite difference flow) and MT3D (Mass transport 3-d) with Groundwater Modelling System (GMS) as pre and post processor was used to simulate the groundwater flow and transport in this study. The simulation was done in transient state for a time period of one year. It predicted the migration of plume of chlorides in groundwater. The changes in the distance and concentration rate of chlorides are identified for a particular period. Modelling provides a useful basis for contaminant transport studies, as well as in the evaluation of the hydrogeologic parameters relevant in the context of the transport problem.

ABSTRACT

Trace Elements were analysed from water samples collected from bore wells from block 2, 5, 14, 26 and 29 at Neyveli Township. Lead Concentration has crossed the limit of ISI standard prescribed for drinking which is 0.100 ppm. Lead concentration in the order of 0.082 ppm at block 5 and 0.11 ppm in block 29 were observed. Cadmium concentration has also crossed the limit of ISI (0.010 ppm) for drinking purpose, which is 0.02 ppm in block 26. Chromium concentration has also crossed the permissible limit of ISI standards (0.05 ppm) for drinking, which is 0.098 ppm in Block 14.

The above elements when compared with ISI standards for drinking water have shown a slight increase in concentration of lead, cadmium & chromium. Concentration of lead in Neyveli basin may be attributed to basement geochemical sources influenced by physical and chemical weathering. High concentration of chromium and cadmium in the aquifer may be due to anthropogenic activity probably from effluents of closed briqueting & carbonization plant, which is due south of block 14.

ABSTRACT

The study narrates the importance of occurrence of groundwater in the crystalline terrain and its yield in different lithologies. In general, yield of well in the crystalline terrain of the study area is low. Average or mean yields for groups of wells in various regions are most commonly between 7 to 20 gpm: The smallest yields are encountered in granulitic terrain and this may be due to fracture closing rapidly with depth. The jointed and weathered geneses yield small but steady supply of water at shallow depth. Chemical quality of the groundwater from this terrain is generally satisfactory for most purposes.

ABSTRACT

The chemical characteristics of the groundwater in and around the Valapadi taluk of Salem district have been studied to evaluate the suitability of water for domestic and irrigation uses. Twenty three groundwater samples representing the shallow and deep aquifer of the area were collected and analyzed for pH, TDS, Ca, Mg, Na, K, HCO3, CO3, SO4 and Cl. It is observed that the quality of the groundwater is suitable for domestic use with some exception. The observed high quantity of Cl, SO4 and Mg at some places require immediate attention and detailed study. The value of EC and SAR indicate good to medium q of water for irrigation.

ABSTRACT

Hydrogeochemistry of a region is a reflection of hydrodynamic process, composition of lithology and physical constraints. The water chemistry is the resultant of all biogeochemical processes and reactions, which have acted on water from the moment it has condensed in atmosphere till the time it is discharged. A case study has been carried out in a varied lithological terrain with Archean, Alluvium and Tertiary rocks to unravel the hydrogeochemical process. A geochemical model relays the distribution of mineral species in the groundwater. Geochemical modeling WATEQ4F has been carried out for pre-monsoon and monsoon period of various litho units in Gadilam river basin, Tamilnadu to find out the disequilibrium indices of carbonate (calcite, dolomite, magnesite and aragonite) and sulfate minerals (anhydrite and gypsum). The result of the model reveals that carbonate minerals show the over saturation state in monsoon period and the Sulfate minerals exhibit under saturation state in both the seasons.

ABSTRACT

The sources of fluoride in groundwater are minerals like fluorite, apatite, mica, amphiboles,cryolite and fluor.spar. The undue concentration of fluoride greater than 1 mg/l has befallen a major toxicological and geoenvironmental issue in India. Excess of fluoride in groundwater causes skeletal and dental fluorisis. The study area forms part of Salem district with records of endemic fluorisis. To decipher process and chemistry of fluoride, a total of 46 groundwater samples were collected from major litho units of the study area (Peninsular gneiss and Charnockite) and analyzed for fluoride using standard procedures for two seasons viz. pre and post monsoon. In pre-monsoon, maximum and minimum fluoride in Peninsular gneiss and charnockite were ranging from 2.85 to 0.4 mg/l and 4.02 to 0.37 mg/l respectively. The coresspnding figures in post monsoon ranges from 2.84 to 0.32 mg/l and 1.91to 0.37 mg/l respectively. Higher concentration was noted in charnockite folowed by peninsular gneiss in pre monsoon and the effect of dilution was well noted in post monsoon season. Statistical analysis undercovers the relationship of fluoride minerals with other ions. Modeling results represent the leaching of fluoride minerals and their variation along with seasons.

ABSTRACT

The present work relates to geological and commercial aspects of pyroxene syenite (Green Onyx) occuring in and around Sivamalai of Periyar District in Tamil Nadu. The alkaline complex of Sivamalai comprises syenites-, nepheline syenite and their variants. Pyroxene syenite is possibly the oldest within the syenite complex occurring as minor massifs, elongated linear bodies, -arcuate islands and enclaves within feldspar syenite are observed in the hillocks North East of Sivamalai, namely Chennimalai and its southern extensions. On the commercial point of view, the pyroxene syenite in the study area occupies nearly 30 square -kilometres area and can be categorised as Kalleri Block, Neykkarampalayam Block and Sivamalai Block. The observed petrographic and petrochemical characters of the alkaline rocks of the study area favours magmatic origin for these rocks. A detailed geological study and random market survey of individual syenite bodies proved the true worthiness of the material Green-Onyx as a dimensional stone with substantial resources and market potential.

Evapotranspiration is the combination of two separate processes named evaporation and transpiration. Evaporation accounts for the movement of water to the air from sources such as the waterbodies and soil. Transpiration accounts for the movement of water within a plant and the subsequent loss of water as vapour through stomata in its leaves. So evapotranspiration is a bio-physical process.

Evaporation

The change of state of water from liquid to the vapour and its diffusion into the atmosphere is referred to as evaporation. During evaporation, water molecules absorb energy which gives them the motion required to escape from the surface of liquid and become a gas. Since a certain amount of energy is consumed in the process of evaporation, the remaining liquid is cooled by an equivalent amount. Thus, the process of evaporation is always accompanied by a cooling effect.

As evaporation proceeds, the surrounding air becomes gradually saturated with water vapour and the process will slow down and might stop if the wet air is not transferred to the atmosphere. The replacement of the saturated air with drier air depends greatly on wind speed. Hence, solar radiation, air temperature, air humidity and wind speed are meteorological parameters to be considered when assessing the evaporation process.

In agrometeorology, evaporation is defined as the maximum possible loss of moisture from a wet, horizontal, flat surface exposed to weather parameters, which exist in the vicinity of plants. Evaporation is an essential part of the water cycle. It is the primary pathway through which water moves from the liquid state back into the water cycle as atmospheric water vapour. The oceans, seas, lakes and rivers provide nearly 90 per cent of the moisture in the atmosphere via evaporation, with the remaining 10 per cent being contributed by plant transpiration. A very small amount of water vapour enters the atmosphere through sublimation, the process by which water changes from a solid (ice or snow) state to a gaseous one, bypassing the liquid phase.

Weather and climate have great influence on farm animal production. The environment in which an animal lives is referred to as its habitat. A habitat includes both biotic (living) and abiotic (nonliving) components of the animals’ environment. Abiotic components of an animal’s environment include temperature, humidity, oxygen, wind, soil composition, day length and elevation. Knowledge of how potential environmental stressors (ambient temperature, humidity, thermal radiation, air speed, etc.) can directly and adversely affect animal performance, health and wellbeing is also required for a successful animal farming. The indirect consequences of weather include their impact on feed quality and availability.

Birds and mammals are warmblooded (endotherms or homeotherms), possessing a thermoregulatory system that maintains a stable body temperature and uniform internal environment (homeostasis) by converting food energy into heat. All metabolic processes generate substantial heat and much of this is retained within the body of homeotherms by insulating layers of fat, feathers or fur. Dairy cattle, like other warmblooded animals, function most efficiently in environments where they can maintain their body temperature at around 38° C. Tissue and cellular metabolism and the underlying biochemical reactions that sustain life and productive functions need body temperature to be maintained within very narrow limits. Relatively small increases in body temperature result in the breakdown of body protein and a significant depression in production.

Climate change, particularly global warming has complex impacts on domestic animal production system affecting feed supply, challenging thermoregulatory mechanism resulting thermal stress, emerging new diseases due to change in epidemiology of diseases and causing many other indirect impacts. So, climate change will have farreaching consequences for dairy and meat production, especially in vulnerable parts of the world where it is vital for nutrition and livelihoods. Due to poor availability of good quality feed and fodders in India, animals are generally maintained on poor quality grasses available in the pastures or are stallfed, mainly on crop residues. India is already deficit in feed and fodder. These shortages would be further aggravated by the adverse effects of global warming on fodder crop production.

Climate, soil, input and management factors influence the crop yield. Weather is the most important natural input that affects the agricultural production and productivity. The agricultural activities starting from the land preparation to harvest and post-harvest storage are greatly influenced by the weather. The effects of weather on crops may be direct such as those of precipitation, solar radiation, humidity, temperature, etc., or indirect such as those of pest, diseases, weed competition, etc. Thus, the success or failure of a crop is determined by the prevailing weather conditions. Although influence of weather conditions on yield is well established, the quantitative assessment is not always straight forward and simple. The quantitative assessment of the effect of weather on crops is an important application of agrometeorology. The relationship between weather and crop production has been understood through crop weather modelling and study on this aspect through a systematic approach started a century ago. The aim of developing crop weather models is to ensure better utilisation of resources and hence a more environmental friendly and sustainable agriculture.

A model is a schematic representation of the conception of a system or an act of mimicry or a set of equations, which represents the behaviour of a system. Its purpose is usually to aid in explaining, understanding or improving performance of a system. Agricultural models are mathematical equations that represent the reactions occurring within the plant and the interactions between the plant and its environment. However, it is a difficult task to represent any system completely in mathematical terms because of the complexity of the system. Universal models do not exist for the agricultural systems and agricultural models are built for specific purposes and the level of complexity is accordingly adopted. Different models are developed for different subsystems and several models may be built to simulate a particular crop or a particular aspect of the production system. Crop weather models may be defined as a simplified representation of the complex relationships between weather or climate on one hand and crop performance such as growth, yield or yield components, on the other hand by using mathematical and/or statistical techniques

11.1 Biodiesel Chemistry

Biodiesel is a mixture of methyl esters of long chain fatty acids like lauric, palmitic, stearic, oleic and so on. It is produced by the transesterification of animal fats and vegetable oils – all of which belong to a group of organic esters called triglycerides. Typical examples are rape/canola oil, soyabean oil, sunflower oil, palm oil and its derivatives, etc. from vegetable sources, beef and sheep tallow and poultry oil from animal sources and also from used cooking oil. The chemistry is basically the same irrespective of the feedstock. The chemistry of biodiesel is given in Fig. 11.1.

15.1 Fuel Cell

Fuel cell is a electrochemical device that convert chemical energy of a reaction directly into electrical energy. The basic physical structure of a fuel cell consists of an electrolyte layer in contact with a porous anode and cathode on either side. A schematic representation of a fuel cell with the reactant or product gases and the ion conduction flow directions through the cell is shown in Fig. 15.1

Soil is a mass of large variability and thus its sampling is the most challenging problem for any soil analyst. Practically, soil sampling is a two step process which includes (i) collection of soil from homogeneous unit and (ii) processing which in turn includes drying, grinding, sieving, mixing, partitioning and storing.

1.1. Sources of Error in Soil Chemical Analysis

Different sources of error involved starting from soil sampling to chemical analysis of a sample can be divided into three main groups:

1. Sampling error : This error results from variability among the different samples drawn from same soil unit with respect to the objective of sampling.
2. Sub-sampling error : This error is due to variability introduced among sub samples of the same soil sample.
3. Analytical error : This error introduced due to variability from one chemical determination to another on same sub sample.

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.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

Introduction

Over the past few decades, soil resources have witnessed many changes driven by demographic pressure and climate change. It is necessary to have up to date soil information to monitor the state of soils health with time. Soil resource inventory provides information about the potentialities and limitation of soils for its proper utilization. Soil survey offers an accurate and scientific inventory of different soils, their properties and extent. An in-depth knowledge of the type of soils and their spatial distribution is a prerequisite for developing rational land use planning. Remote sensing is the science of obtaining information about the earth’s surface without actually being in contact with it. On the other hand, proximal sensing refers to remotely sensed measurements that are taken at the field or laboratory level. Both these processes involve an interaction between incident radiation and the targets of interest. Fundamentally, remote sensing data products are a representation of the functional response of objects to energy sources. These are typically spectra, which are acquired as a discrete series of reflectance measurements taken at different wavelength intervals, or bands. In the shorter wavelengths of the electromagnetic spectrum (visible part), features can be detected by virtue of reflected solar energy, while in the longer wavelengths (microwave, thermal parts), sensing of emitted energy works. Conventional soil sampling, and subsequent laboratory analysis are generally time consuming and costly. In this context, remote sensing plays a crucial role for studying soil resources. The term remote sensing is generally used for airborne and space borne acquisitions, whereas proximal sensing refers to ground-based laboratory and field measurements. Advances in the quantitative disciplines like remote sensing and proximal sensing have laid the foundations for a spatial exploration of soil-system dynamics within a landscape context (Pennock and Veldkamp 2006).

Introduction

Soil is a dynamic natural body, characterized by high degree of spatial variability due to combined effect of physical, chemical or biological processes that operate with different intensities at different scales (Goovaerts 1998). Reports have shown that there is large variability in soil properties, crop yield, disease, weed etc., not only in large-sized fields (Godwin and Miller 2003,Vrindts et al. 2005), but also in small-sized fields (Bhattacharya et al. 2008). Therefore, knowledge of hydro-physical properties of soils is crucial for management practices for all kinds of agricultural practices for efficient utilization of input resources. Plant growth and development is highly related to soil hydro-physical properties, which not only depend on soil intrinsic textural and chemical characteristics (Hamza and Anderson 2005) but also on soil management.

Soil physical properties which change with the variation in soil structure include bulk density, total porosity, pore geometry (size distribution, shape, continuity and tortuosity), penetration resistance and aggregate stability, consequently affecting soil hydraulic properties such as water retention characteristics, plant available water capacity, infiltration capacity and saturated hydraulic conductivity. These soil properties are dependent on seasonal climatic conditions, management practices, crop development and biological activity (Reynolds et al. 2007). The hydro-physical properties of soils, i.e., water retention and water permeability in both saturated and unsaturated zones not only shape soil water balance but also decide the conditions for plant growth, development and yield. They also determine water availability for the plant root system and the transfer of water with chemical compounds dissolved in it into deeper layers.

Introduction

The life support systems of a country and socioeconomic development of its people largely depend on soils. The soils are the most valuable natural resources for the society. It caters the basic needs of mankind by producing food, fibre and timber. It is therefore essential to know the distribution and extent of different soils and their qualities, potentialities and constraints for sustainable utilization of this valuable natural resource (Shyampura and Sehgal 1995). Information on distribution, potential and constraints of major forest soils are essential to design most appropriate soil management systems in order to increase productivity of forests as forests also offer excellent potential for poverty reduction and rural economic growth in India. However, unlike agriculture lands forest soils usually draw less attention and are ignored in spite of the fact that they nourish the lungs of the Earth. Soil quality is the most important factor in forest management decisions. Soils will determine productivity of a particular forest and management strategy. Knowing about forest soils can serve as a basis for forest management decisions, including land acquisition, species selection for planting, site preparation requirements, watershed development, fertilization prescriptions, stand density/ composition, and harvest timing, as well as decisions affecting land ownership and use.

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

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.

Green energy can be classified as any form of energy resource that results in zero greenhouse gas emissions and zero toxic byproducts. These constitute the alternate source of energy such as Solar, Bio, Hydrogen, Wind, Water etc. Renewable Energy is energy that is not depleted or expen- ded. Previously the pressure to reduce CO2 emissions dictated the energy sourcing. With recent findings from experimental evidence, global warming may not be attributable to CO₂ emissions in relation to other over-weighing causes. However the pressures to develop renewable sources continue since the cost of Oil is increasing in many countries. It is desirable and a necessity to develop renewable non-toxic energy resources. The global economy and welfare of all earth depends on it even if CO₂ is not the cause of global warming (GW). There are some factors not in the control of even the most technologically advanced society. These are solar activity such as sun spots which have huge and tremendous influence on the Earth’s climate. However we should strive not to cause any undue imbalances in the proportion matter and composition of Earth’s atmosphere. Although CO2 is a small contributor to GW in comparison to H₂O, we still have responsibility to maintain the right balance of the gases in the atmosphere by not causing undue changes to the Carbon, Nitrogen and Oxygen cycles! We are far way off from a Oil-free technology. Most transportation is still petrol driven.

To set up a soil-testing laboratory the minimum requirements are as follows:

8.1 Laboratory floor area

Minimum four rooms are needed. They are as follows:

1. Office room, floor size: 18’x 12’

This room is used for registration of the soil samples received, recording recommendations along with soil test results in laboratory book and in report sheet, their dispatch, accounts etc.

Coastal areas are defined as the interface or transition areas between land and sea, including large inland lakes. Coastal areas are diverse in function and form, dynamic and do not lend themselves well to definition by strict spatial boundaries.

Economic importance of coastal areas

Many of the world’s major cities are located in coastal areas, and a large portion of economic activities are concentrated in these cities. The coastal zone is an area of convergence of activities in urban centres, such as shipping in major ports, and wastes generated from domestic sources and by major industrial facilities. Many of the world’s most productive agricultural areas are located in river deltas and coastal plains, which contribute for national economic growth.

Life on Earth is dependent on microorganisms for many essential services. Microbes are intimate partners in global agriculture by way of mobilization of nutrients, antagonism against pathogens and exploitation of plant- microbe interactions and optimization of plant microbiome allows farmers to apply less chemical fertilizers and pesticides, and improving plant growth and yield for sustainable development.

India has 2.4% of land area of world and 8.1% global species diversity. India has 7500 km long coast line in nine states and three union territories. About 14% of Indian population (17 crores) lives in coastal area. In 10.78 million ha of coastal ecosystem, rice is the major coastal crop. Coastal rice accounts for 15% of total area and production of India. Coconut, cashew, areca nut, spices, plantation and fruit crops are the other important coastal crops. The east coast in four states (Tamil nadu, Andhra Pradesh, Orissa, West Bengal) and one union territory (Pondicherry) has sandy coast and plains with medium to high rainfall (100-250 cm), and hot humid climate. It has marshy lands and mangroves. The west coast in five states (Kerala, Karnataka, Goa, Maharashtra, Gujarat) and two union territories (Diu and Daman, Dadar and Nagarhaeli) has fertile sandy plains with high rainfall (250-300 cm) and hot humid climate suitable for horticulture and plantation crops. Western Ghats near west coast is a hot spot with high biodiversity. ICAR established the Central Coastal Agriculture Research Institute (CCARI) at Goa in 2015 to carry out research on coastal agricultural crops.

Soil salinity in coastal regions is not only a soil type character, but also due to low quality irrigation water. The plant associated microorganisms “Plant microbiome” play a significant role in conferring resistance to soil salinity. The endophytic bacteria- rhizobium, rhizobacteria- azospirillum, azotobacter, phosphobacteria, PGPR bacteria, cyanobacteria, symbiotic fungi- ecto and endo mycorrhiza, alleviate the impact of soil salinity. Minimal microbiome for plants is the key determinant of plant health and productivity. The plant- microbe interactions are of three types: (i) Positive: Beneficial organisms- nitrogen fixers, phosphobacteria, mycorrhiza; (ii) Negative: Harmful organisms - plant pathogens; (iii) Neutral: Transient organisms. Millions of microbes inhabit plants, form complex ecological communities to improve plant growth, crop quality and productivity.

Introduction

Horticultural crops comprising of fruits, vegetables, flowers, medicinal and aromatic plants, and mushrooms play an important role in meeting the livelihood, nutritional security and socio-economic development of farmers. India’s horticulture production (287 mt) surpassed food grain output (265 mt) fifth year in a row in 2016-17 (First Advance Estimates, 2016-17). Horticultural crops together contribute around 30.4% to the agricultural GDP from nearly 14% of the total cropped area, and contribute nearly 37% to the export of the agricultural commodity from the country (Anonymous, 2015). Fruits and vegetables are an important part of the human diet and a major source of biologically active substances such as vitamins and secondary metabolites. India is the second largest producer of fruits and vegetables in the world, with a combined production of 260.7 million metric tons (91.7 million metric tons of fruits and 169 million metric tons of vegetables) during 2016-17.

Rapid growth in horticultural production has been accompanied by heavy use of pesticides thus causing major concern over health effects. Heavy pesticide use occurs, in part, because numerous pests attack horticultural crops, including the fruit itself, reducing market value and yield on high-value crops. Pesticide use raises safety concerns for agricultural workers who apply pesticides. Concern is particularly high in flower production because of heavy spraying in enclosed conditions. Potential food safety risks from pesticide residues are also a significant issue for importers of fresh fruits and vegetables and a market-risk factor for exporters who may have shipments detained or rejected if residues exceed allowable limits (George et al., 2003).

The increased use of chemical pesticides on horticultural crops has raised a number of economic, ecological and health concern. Economic concerns arise from the over reliance and use of chemical pesticides which increase the costs of production. Indiscriminate use of pesticides has resulted in ecological problems of common pests developing resistance, elimination of natural enemies and other beneficial arthropods, and environmental pollution. Human health concerns focus on risks from shortcomings in protective clothing, large deviations from recommended doses/situations, and excessive run-off into the soil and water sources. These concerns are exacerbated by poorly regulated internal markets for pesticides that have fostered usage of banned or outdated products; creating a situation that if not stopped will negatively impact on horticultural exports to countries with more stringent regulatory requirements for fresh crop produce. Meeting these food safety requirements has become a major challenge for the fresh produce export sector of many countries. To ensure and maintain export compliance, grower and consumer safety, and environmental integrity; farmers, government and development partners are developing programmes designed to improve pesticides usage, regulation and management on horticultural crops (Karungi et al., 2011). In this chapter, major important horticultural crops grown in India have been selected to illustrate trends in pesticide usage and regulation, and the development and application of alternative pest management practices through farmer participation and training.

Introduction

Vegetables are important constituents of Indian agriculture and play important role in nutritional security due to their short duration, high yield, nutritional richness, economic viability and ability to generate on-farm and off-farm employment. Vegetables form an integral part of staple diet in India where 42% population is vegetarian and vegetables are sources of daily requirement of nutrients, vitamins and minerals. Our country is blessed with diverse agro-climates with distinct seasons, making it possible to grow wide array of vegetables. The role of vegetable cultivation in the country’s nutritional security, poverty alleviation and employment generation is becoming increasingly important. There are vast opportunities available in export sector, which need to be harnessed to their fullest ability. Vegetable crops have high yield potential and calories per unit area compared to that of cereals in the shortest time. Additionally, vegetable cultivation creates employment in rural sector, as it is a labour intensive farming practice. Vegetables are good source of ready farm income, besides being rich in health building substances, especially protective nutrients (Kalia, 2010 and Meena et al., 2016).

Introduction

Multiplication and perpetuation of plants through sexual and asexual means at desired quantum and quality is one of the important aspects of successful horticultural development. For successful propagation, stock production and growth of plants some facilities in the nursery, field or greenhouses are required for mass multiplication of the saplings. Horticultural crops included annuals, biennial and perennial crops so careful planning is require for the quality and genuinely. We known the various methods of propagation i.e., seed, cuttings or grafting and budding can be used for faster and true to type sapling production (Aldriance and Brison, 2000). Plantlets propagation mainly depends on season in open conditions, but for round the year material multiplication require hi-tech propagation structures like greenhouses, cold frames low polyethylene tunnels, sun tunnels, shade houses (lath houses), tissue culture facilities, hot frames and net house (Singh et al., 2003; Ratha Krishna et al., 2014). Field management for mass multiplication of saplings require partial modification and refined in nursery beds preparation (raised bed in high rainfall area and sunken bed in arid region), mother bed preparation (bed with mixture of media, where generally seeds are sowing in enclosed area and all viable and vigours seedlings at early stage may be shifted in poly bags for further growth) and poly bag nursery (saplings of horticultural crops are produced in poly bags of 150-200 gauge and different sizes i.e., 25 x10 cm (ber, khejri), 15 x10 cm (phalsa and pomegranate, karonda) and 30 x17 cm (kinnow, mosambi, aonla, sapota, mango) etc. Propagated plants should be healthy, vigorous and free from disease, weed, insect-pests as well as grafted plants should be equal in size of scion – rootstock, proper in shoot: root ratio and appropriate in morphological standard of scion according to varieties and species.

Introduction

Under arid and semi-arid regions of the world, major problems are salinity, alkalinity and calcareousness, which seems further aggravate with changing climate scenario. Besides that most of the developing countries lies in this zone with flanging population resultant shrinking down of land resource and good quality of irrigation water. Globally area under saline/sodic soils is about 953mha covering 8% of the land surface. Resent estimates indicate that 6.7 mha area in India is affected by soil salinity and alkalinity and about 25% of ground waters are sodic and/or saline in the country. There are two important approaches for utilizing saline, alkali or calcareous environment. The first one is to modify the environment so as to suit the plant or modifying the plant or substitute the plant species so as to suit the environment. First approach has been used more extensively because it enables the plants to respond better not only to water but also to other production inputs and also not far sustainable. However, second option is cost effective and most sustainable.

Accumulation of excessive salt in root zone leaves adverse effect on general agricultural crops and or crop failures sometimes, while crop diversification using seed spices are the option for such conditions. Most of the seed spices particularly cumin, fennel, dill, fenugreek, ajwain, celery, anise, caraway and coriander (and their cultivars) have some degree of salinity and alkalinity tolerance. However, among the seed spices, cumin is having high degree of salinity tolerance. In case of sodic or calcareous environment, fennel is most tolerant followed by dill as well as these crops are the remedy for such degraded environment by bringing down the sodicity and calcareousness. In some cases, germination, growth, yield and quality of seed spices improved by saline sodic environment. Besides eco-friendly use of these soils, phytoremediation of saline, sodic and calcareous soils by seed spices seems most sustainable.

Introduction

The hot arid regions of India are spread over 32 million ha (0.32 million Sq.Km.) in the state of Rajasthan (61%), Gujarat (20%), Andhra Pradesh (7%), Punjab (5%), Haryana (4%), Karanataka (3%), and Maharashtra(0.4%) which are characterized by hostile agro-climatic conditions and fragile eco-system. Out of total hot arid area of the country (India), more than 60% area falls under the state of Rajasthan which covers 12 districts viz., Jaisalmer, Barmer, Bikaner, Churu, Sikar, Jhunhjunu, Naguar, Jodhpur, Pali, Jalor, Sriganganagar, and Hanumangarh of the state. In general, the hot arid region of Rajasthan receives very low rainfall, varies from 100 mm annun^-1in north-western district of Jaisalmer to 450 mm per annum in the eastern boundaries of arid district of Rajasthan. The potential evapo-transpiration varies from 2063 mm in Jaisalmer to 1503 mm in Sikar districts of Rajasthan. The rainfall is very erratic and often experienced prolong drought. The ground water table is very deep and often brackish in nature. The extremes of temperature having severe winter during December-January with temperature as low as -4 °C and very hot summer during May-June with temperature as high as 48-50 °C is another important characteristic of the hot arid regions. The solar radiation is very high. Atmospheric vapour pressure deficit reaches as high as 30 mb during summer (May-June). The wind velocity in June is very high which leads to heavy wind erosion and formation of sand dunes. Some time strong sand storms of very high speed (60-0 km h^-1) with huge amount of sand particles also occurs. The soils are coarse textured with low silt, clay and humus content and water holding capacity is very low and infiltration rate is very high. The soil fertility is very poor having low organic carbon (0.02-0.06%).

Introduction

The Intergovernmental Panel on Climate Change (IPCC) assessment reports (IPCC, 2007) suggests that consequently occurring climate extremes are likely to increase the frequency and intensity in the future. In the Indian context, events such as landfall of rare of rarest most intense pre-monsoon Tropical cyclone Fani (2019), back to back landfall of very intense tropical cyclones such as Phalini (2013) and Hudbud (2014), Cyclone Komen (2015), Extreme rainfall events of Kerala (2018 and 2019), Heavy rainfall events of over Northeastern states of India (2019), Cloud burst events of Uttarakhand (2013), Rainfall over Himachal Pradesh (2019), Mumbai Rain fall (2015) and extreme heat wave conditions of 1998, 2015 and many similar type of extreme events are not usual. However, at present there is no unanimity among scientists to exclusively and directly link such extreme weather events to climate change, however based on number of studies and observation data sets it is agreed that the impact of climate change is evident (beyond the natural variability) in many part of the globe including India. In 2019 monsoon season at least 1351 deaths happened to ?oods, heavy rainfall and landslides compared to 1550 for year 2018. In this manuscript, recent research findings related to extreme weather events in the context India are discussed in the following section-2, need for high resolution and long term data sets are emphasized in section-3, followed by recommendations as part of future preparedness, that will not only enable us to address these issues in a robust scientific manner but also provide us strong resilient society to face these extreme weather events in an optimized and effective manner to minimize the loss of lives, properties and socio-economic conditions of citizens.

Introduction

Climate change is one of the biggest environmental threats to food production, water availability, forest biodiversity and livelihoods. Warming of climate system of the earth is a unanimously accepted reality and probably one of the most prominent challenges for scientists, development workers, policy makers and other relevant stakeholders regarding development and sustainability in international and national arena during past several years. Intergovernmental panel on climate change (IPPC) has described climate change as any anthropogenic or naturally occurring alteration in the climate over time. It is widely believed that developing countries such as India will be impacted more severely than developed countries. Climate change is emerging as a big threat to sustainable development of agriculture including animal husbandry and to the livelihood of people. Extreme events such as cold waves, heat waves, ?oods and high intensity single day rainfall events are on increasing trend during the last decade (Dikshit and Birthal, 2010). It is probable that by 2030 the impacts of climate change will be noticeable, although not dramatic and will be largely manifested through changes in pasture growth and quality, and greater inter-annual variability in pasture production. Livestock contribute to climate change by emitting methane through enteric fermentation. But they are also affected by climate change it directly and indirectly, hence affecting their economic and social contributions. Various studies suggest that the changes in pasture growth, composition and production will be dependent on the actual combination of CO₂, temperature and rainfall conditions. Livestock productions under both intensive and extensive systems are subjected to so many stresses, where they undergo different types of physiological adjustments to cope up with stressful conditions. Productions are drastically reduced during stress and require managemental interventions both in terms of optimum nutrition and health care (Soren, 2013).

Introduction

Birds lack sweat glands; hence non-evaporative cooling mechanism (radiation, conduction and convection) is the major route of heat dissipation within thermoneutral range. Beyond this TNZ, majority of heat loss occurs as insensible heat loss i.e., Panting. Panting is one of the visible responses in poultry during exposure to heat stress associated with considerable energy expenditure. This specialized form of respiration dissipates heat by evaporative cooling at the surface of the mouth and respiratory passage. Panting increase, the loss of CO2 from the lungs, this leads to a reduction in the partial pressure of CO2 and also reduces bicarbonate levels in blood plasma. In turn, the lowered concentration of hydrogen ions leads to rise in plasma pH, a condition generally referred to alkalosis (Daghir, 2009). Birds also respond by reducing their feed intake and thus metabolizable energy (ME) intake to reduce thermo genesis as an adoptive mechanism. If panting fails to prevent their body temperatures from rising, the birds become listless, comatose and soon die due to respiratory, circulatory or electrolyte imbalances (Saif et al., 2003).

Introduction

The Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC, 2013) emphasized that warming of the climatic system is unequivocal and that anthropogenic warming will continue for centuries to come due to timescales associated with climate processes and feedbacks. While stresses caused by climate change are likely to negatively impact the agricultural and livestock sector and escalate existing small-scale farming challenges; agricultural and livestock productivity and sustainability have to improve to meet the needs of the growing human population (DEA, 2013; Zamykal and Everingham, 2009). Climate change is seen as a major threat to the survival of many species, ecosystems and the sustainability of poultry production systems in many parts of the world. Nevertheless, global demand for poultry products is expected to increase, as a result of the growing human population, and its growing af?uence. The extreme climatic conditions impose various stresses on poultry birds which adversely affect their growth, production and reproduction.

While climate change is a global phenomenon, its negative impacts are more severely felt by poor people in developing countries who rely mostly on the natural resource base for their livelihoods. Moreover, rural poor communities rely greatly for their survival on agriculture and poultry which belong to the most climate-sensitive economic sectors. Climate change is expected to intensify existing problems and create new combinations of risks and shift hazard zones. The incidences of draughts, snow-storms and blizzard like events have risen. The situation is made worst due to factors such as widespread poverty, inequitable land distribution, limited access to capital and technology inadequate infrastructure, long term weather forecasts and inadequate research and extension. Intensive poultry production has much less impact on global warming than organic or free-range production (Van der Sluis, 2007). Seo and Mendelsohn (2006) reported that small farms of poultry are better able to adapt to warming. However, organic egg production needs more energy than non-organic and increases most environmental burdens. The most disadvantageous, from environmental point of view, is litter-free breeding of birds which causes great amounts of liquid manure.

Introduction

There is an ongoing debate on global warming worldwide as it is seriously impacting climate change. The global temperature has been increased by 0.74±0.18°C during the last century and the climate model projections summarized in the Intergovernmental Panel on Climate Change (IPCC) report indicate that the global surface temperature will probably rise a further 1.1 to 6.4°C during the twenty-first century (IPCC, 2007). High ambient temperature is one of the most important stressors to poultry, which has a direct relationship on profitability of chicken meat and egg production. A seasonal problem in many parts of the world, high environmental temperature (35-43^oC in tropical countries) causes economic losses through reducing feed intake while decreasing nutrient utilization, live weight gain, egg production, egg quality and feed efficiency in poultry (Corzo et al., 2003; Panda et al., 2007a; 2008). The ideal temperature for broilers is 10-22^oC for optimum body weight and 15-27^oC for feed efficiency (NRC, 1994). Layers will produce egg constantly in the temperature range of 10-30^oC. Above 30^oC, performance in terms of growth and egg production declines.

Introduction

Statistics generally refers to information about an activity or a process that is expressed in quantitate as well as qualitative traits. The quantitative research process is well structured whereas the qualitative one is fairly unstructured. It depends upon how a piece of information have been collected and analyzed. A good researchers need to have both types of skill. This article highlights about the operational procedure, about methods, procedures and techniques that are used in analysis of climate related data.

Parameter vs Statistic

When a data is collected with survey of all the individuals (like in a census) and analysed for certain values, it is called a parameter. But when, we guess the same value with a random sample and interpret for whole population it is called a statistic. Environmental parameters are: mean, variance, correlation, regression, heritability, repeatability etc. Again, there are two types of variables: quantitative and qualitative. Quantitative variables can take any value in a specified range. So, they are of continuous type. But qualitative variables are very few types ( 2 ,3 or 4 types) and they are of discrete type. Body weight is an example of quantitative variable whereas, comb type is a qualitative variable.

The dynamic layer surrounding the earth above its surface containing various gases, moisture, aerosols, etc. is called atmosphere. Atmosphere can be defined as the gaseous envelope surrounding the earth. It is retained by earth’s gravity.

The atmosphere protects life on earth by absorbing ultraviolet solar radiation, warming the surface through heat retention and reducing temperature extremes between day and night. The common name given to the atmospheric gases used in breathing and photosynthesis is air. The atmosphere has a mass of about 5.15 × 10¹⁸ kg, three quarters of which is within about 11 km of the surface. The atmosphere becomes thinner and thinner with increasing altitude, with no definite boundary between the atmosphere and outer space. The imaginary ‘Karman line’ lies at an altitude of 100 km above the earth’s sea level, and commonly represents the boundary between the earth’s atmosphere and outer space. The study of earth’s atmosphere and its processes is called atmospheric science or ‘aerology’.

A big layer of air called the atmosphere surrounds the earth. The air within this layer moves from place to place when it warms up or cools down. This moving air is known as wind. In other words, air in motion is called wind. Winds move moisture and heat around the world and also produce much of our weather.

The strength of the wind can vary enormously. Sometimes air moves slowly and the wind is barely noticeable. When the weather is clear there may be a gentle breeze. When the wind is still very light, but it can be felt and there may be rustlings by leaves. At other times, the air can move very quickly and become a gale or hurricane, blowing down trees and damaging electric poles and buildings.

Winds are part of a global air circulation system that acts to balance temperature and pressure around the world. Different parts of the world receive different amounts of heat from the sun. This differential heating in turn results in differences in temperature and air pressure around the world, which drives the world’s winds.

As equatorial areas are heated most, the air above them warms and rises as it becomes lighter than the surrounding air, causing an area of low pressure. In cooler areas, the air sinks because it is heavier and results in an area of high pressure. Winds will blow as air is squashed out by the sinking cold air and drawn in under the rising warm air. Any difference in temperature like this will always cause a difference in air pressure, and therefore winds will blow. These movements result in a global wind pattern, with air moving between different areas around the world and also at different heights in the atmosphere. Colder air from the poles tends to sink and move towards the equator closer to the surface of the earth. In contrast, warm air from the equator rises and moves towards the poles high in the atmosphere because it is lighter.

Abstract

Any country’s socio-economic development essentially depends on land and water resources. Resource depletion takes place when there is an overstretching due the exploding population, calling for prudent management of the resources. Application of remote sensing and Geographical Information System (GIS) tools in the management of natural resources at different levels including watersheds is quite well known. In addition, applied geoinformatics are also used in research on management of these delicate resources also besides characterization. There is a continuous advent in technology in both hardware and software necessitating updation of application scientist/manager. Once an exclusive tool of elite institutions and government agencies, GISnow became an indispensible part of our day-to-day life. As the urge for information intensifies, new applications of GIS continue to evolve. A concerted effort is needed to develop an information system when vast expanses of natural resources are to be managed and a combination of information tools like remote sensing, global positioning system and GIS always updates the database effectively. However, the cost factor should not hinder the applications at micro level. This necessity calls for attention to popularize free and open sources of software (FOSS) and data (particularly digital elevation models (DEM) and satellite imagery) to put the techniques into use by enthusiasts at village level such that a database at a regional level can be built through participatory GIS. It is in this direc tion the av ailab le li terat ure on appl ied geoinformatics coupled with use of FOSS is reviewed and presented in this article to give an idea as to how an open ended information system could be developed to help the management.

Keywords: Natural resources, management, Remote sensing, GIS, FOSS, DEM

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

Abstract

The weather or climate is a natural resource and is an important basic input in agricultural planning and strategies. Every plant process, related with growth, development and yield of a crop and each of inseason and off-season farm operations (according to World Meteorological Organization) is more than 10 per cent and can reach up to 50 per cent in smaller areas and not a surprise even if it reaches to 100 per cent on micro scale. Amongst the various weather elements, temperatures, radiation and rainfall play major role in deciding the crop growth and yield levels. Precipitation is one of the important weather factors being responsible for atmospheric and soil moisture and therefore has more agricultural importance, especially in rainfed agriculture.

Keywords: Remote sensing, weather, agroadvisory, GIS

1.0 Introduction

India receives more than 70 per cent of its annual rainfall during South West monsoon season. Indian summer monsoon rainfall shows large intra - seasonal and intra-annual variability (Parthasarathy, 1960; Alexander et al., 1978; Mooley and Parthasarathy, 1984) and also changes in space and time, intensity and distribution. Borgaonkar and Pant (2001) have reported that Indian monsoon rainfall has shown trend less nature since 17^th century based on proxy data with large inter-annual fluctuations. IPCC (2001) has reported increase in rainfall over tropical and higher latitudes and decrease in rainfall over subtropics and mid latitudes.

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

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

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

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.

Particle-size distribution is one of the stable soil characteristics, being little modified by agricultural management practices. The particle-size distribution expresses the proportion of various size particles in a soil, especially sand, silt and clay that determine soil texture (Hillel, 2004). Soil texture is closely related to soil physical properties such as water retention and release characteristics, surface area, swelling and shrinking, soil strength and tillage properties. There are two common methods being employed for particle-size (<2.0 mm) analysis namely international pipette method and the hydrometer method. Both the methods are based on Stokes’ law which establishes a relationship between particle size and the rate of sedimentation. Thus, particles are assessed by their settling velocities from suspension in a water solution that can be used to quantify particle size. Size range of soil particles (sand, silt and clay) according to two most adopted systems i.e. U.S. Department of Agriculture (USDA) and International Union of Soil Science (IUSS) formerly known as International Society of Soil Science (ISSS) are given below: