eChapters

Abstract

Soil Resource Appraisal helps in characterizing the existing land resources, assessing their suitability for various land uses and then helping the user to prepare a rational land use plan suitable for the integrated development of the area. Application of Remote Sensing (RS) and Geographical Information System (GIS) helps not only in the characterization of the resource base, but also in the generation of various thematic maps and preparation of land use and action plans for effective implementation of various developmental plans at farm level.

Keywords : Soil Resource Appraisal, Land Use Plan, Farm level Developmental Plans

1.0 Introduction

A thorough knowledge and understanding of soils is basic to the development and management of agricultural resources. About 100 years ago, man began systematic study of soils in the field and tried to separate, classify and map their geographic distri­bution. Today we have sufficient knowledge about soils; however the characterisation, classification and mapping of soils is still tedious, time consuming and costly affair. Therefore the need for adopting new and more rapid and accurate methods of mapping soils has resulted in the use of satellite images with image interpretation techniques for soil survey and map­ping.

In India for the country as a whole, soil and other land resources information is available at 1:1 M scale, which was generated from the state level mapping done at 1:250,000 scale under the Soil Resources Mapping Project of India during the period from 1986 to 1995. This information is of general nature and useful for planning on a wide canvas and not particularly useful for farm or watershed level planning in the country. For site-specific needs and for developmental works at watershed level, we need detailed farm level database at 1:10,000 scale. Once this type of information is generated at village/watershed level, this will be useful for identifying site specific constraints, generation of area and site specific agro technologies, land-use options based on soil site suitability assessment, wasteland identification, identification of prime farm lands and farm clusters for zoning and Strategic planning, preparation of farm level development plans by line departments and proactive advice and technology transfer to farmers.

2.1 INTRODUCTION

GIS is a tool that can be used in varied disciplines ranging from civil engineering to business. Remote sensing data is often brought into GIS to have a complete data set in many natural resource application areas. Full capability of GIS is not used widely. Many a times people use GIS only for digitising maps and to do simple query on maps. But it can also be used in modeling, simulation and forecasting, only then one may regard GIS technology is utilised to its full potential. Some GIS softwares handle simple problems for location based services while other complex softwares posses advanced modules to do all types of spatial, network and 3D analysis. Integration of softwares used in natural resources study are being interfaced with GIS to create scenarios.

Remote sensing and GIS technology is useful in the following fields:

Water is a primary source of life and sustains all human activities such as domestic needs, agriculture, industries etc. The allocation and management of water resources is becoming a difficult task due to increasing demands, decreasing supply and diminishing quality. This calls for judicious use of water resources. The geomorphology describes the environment in which the hydrological processes operate. A strong mutual correlation exists between geomorphological variables and hydrological characteristics. Such relationship can be applied to both surface and groundwater regime. Thus the linking of geomorphological parameters with hydrological characteristics of the basin provides a simple way to understand the hydrogeomorphological behaviour of different basins and particularly of un-gauged basins.

During the last few years various applications of hydrogeomorphology with reference to the Geographic Information System (GIS) and various other techniques have emerged as an extensively effective tool for analyzing and prioritizing natural resource management alternatives. Because natural resource management problems are spatial in nature the GIS technology provides a tool for defining the extent of the problem, and facilitates the design and implementation of alternative management strategies. The GIS is used to estimate the hydrogeomorphological parameters of the watershed, which can be used in watershed planning and management at micro and macro levels. The results of the study can be utilized to infer the hydrological features like runoff potential, infiltration and groundwater recharge of the region. Some of the applications of hydrogeomorphological studies are described below:

Abstract

Remote sensing is the art and science of obtaining information about an object or phenomenon without having any contact with the object concerned. Geographical information system is a tool to create, manage, retrieve and query spatial databases. Remote sensing in conjunction with Geographical Information System (GIS) has wide applications in all spheres of life and especially in agriculture. Specifically, this paper reveals some of the applications of remote sensing and GIS in the field of crop condition assessment viz., crop type classification, crop stress detection, crop yield estimation and pest and disease monitoring.

Keywords : Remote Sensing, GIS, Yield Forecasting, Crop Stress, Pest and Disease Monitoring

1.0 Introduction

Remote sensing is the art and science of obtaining information about an object or phenomenon without there being any physical contact. The information is so collected using a device called a sensor. The sensors are mounted on a platform at an advantageous position. Accordingly the remote sensing is called ground based (sensor is hand held or mounted on mast), air borne (sensor is on a balloon or aero plane) or space borne (sensor is mounted on a satellite). The basic principle in remote sensing is that each and every object has unique reflecting property like human thumb impression and when the solar energy is falling on an object a part of it gets reflected, which carries the information about the object. The reflected light is converted in to reflection percentage of incoming solar radiation. This percentage of reflection can be used to work out the vegetation indices which are surrogate measure of crop condition.

Geographical Information System (GIS) is software system which enable Collection of large volume of data with spatial relation Integration of data from different source is possible Analyze such data for our requirement Present the data in different formats (maps, table and reports)

Applications of Remote Sensing

The era of satellite remote sensing was open in the 1960s when cameras and electronic sensors were mounted on spacecraft. Nowadays there is a big assortment of satellite systems actively recording information about the Earth. A wide variety of imagery is available from satellites. Both active and passive sensors, operating from the microwave to the ultraviolet regions of the electromagnetic spectrum collect a large amount of information about the earth’s surface every day. Each of the systems vary in terms of their spatial, spectral, radiometic and temporal resolution. Those characteristics play an important role in defining which applications the sensor is best suited for. The main benefits of satellite remote sensing are the following:

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

An aquifer is a geologic formation, a part of a formation, or a group of formations that will yield water. The quantity of ground water that an aquifer will yield to wells depends partly on its thickness, extent, continuity, and homogeneity, and partly on its physical properties of permeability and porosity.

Groundwater is stored in subsurface void spaces below the water table. The geologic material that stores, transmits, and yields groundwater to wells and springs are called aquifers. The key word here is yields. To be an aquifer, it must store and transmit water at rates fast enough to supply reasonable amounts to wells and springs. Therefore, not all groundwater is stored in an aquifer. Productive aquifers can be comprised of sand and gravel, sandstone, limestone and dolomite, or basalt flows, whereas fractured igneous or metamorphic rocks are only marginal aquifers.

The classifications of aquifer are given below:

1. Introduction
Studies on soil health and agricultural growth are increasingly using artificial intelligence (AI) and machine learning (ML) techniques. With the help of these technologies, it is possible to analyse vast amounts of data and gain insightful knowledge that will help to boost crop yields and optimise agricultural practises (Neethirajan, 2020). Applications of AI and ML to research crop development and soil health have enormous potential to revolutionise agriculture. In order to increase soil health and crop yield, farmers and researchers can use these technologies to analyse massive datasets, generate precise forecasts, and optimise farming practises. Overall, artificial intelligence (AI) and machine learning (ML) provide useful tools that can support efficient and sustainable agriculture, solving global concerns in food availability and the sustainability of the environment. Unquestionably, technical developments have significantly increased the productivity and efficiency of many industries, including agriculture. The advent of words like “big data,” “data analytics,” “artificial intelligence,” “Internet of Things,” “erosion modelling,” “smart farming,” and “machine learning” are just a few examples of this technological revolution (Almoussawi et al., 2022). Digital soil mapping (DSM) uses computational models to infer regional and temporal shifts of soil types and properties based on soil observations, prior knowledge, and relevant environmental variables in order to create and populate spatial soil information systems. The supply chain for agricultural production is extremely intricate (Lamichhane et al., 2019). The way our food is produced, distributed, and consumed is changing as a result of AI. When planning crop rotations, planting times, water and nutrient management, pest and disease control, optimal harvesting, food marketing, product distribution, food safety, and other agriculture-related tasks throughout every step of the food supply chain, researchers use powered by AI tools to supply advice and expertise. In “Harnessing AI to transform agriculture and inform agricultural research,” Peters et al. (2020) presented a summary of the most recent developments, difficulties, and prospects for AI technology in agriculture. They use the four main elements of the food system—production, distribution, consumption, and uncertainty—to show the possibilities of AI. They come to the conclusion that agricultural businesses are excellent candidates for using AI and other technologies. In “AI down on the farm,” Sudduth et al. (2020), examined a number of case studies in which machine learning (ML) has been used to model various aspects of agricultural production systems and give data that can be utilised to make management decisions at the farm level. These research efforts involve providing data, essential for creating precise and effective irrigation systems and improving tools for suggesting the best nitrogen fertilisation rates for maize. Traditional crop health monitoring techniques need a lot of work and time. Using AI to monitor and detect potential crop problems or nutrient deficits in the soil is an effective method. Applications to analyse plant health trends in agriculture are being created with the aid of deep learning. These AI-powered tools are essential for improving our understanding of soil quality, crop pests, and disease in plants (Virnodkar et al., 2020).

Abstract

Higher temperature and altered rainfall patterns accompanied by extreme weather events are being considered indications of climate change. These factors impact vegetation growth in natural forest, open scrub and in agriculture land. Vegetation index based on satellite data was used to analyse agricultural vulnerability in India to understand impact of rainfall variability. Time-series Normalized Difference Vegetation Index (NDVI) data products obtained from AVHRR (1982 to 2006) and MODIS (2000-2011) were analyzed to assess spatial and temporal variability. Coefficient of variation of maximum NDVI was assessed for the study period and results were corroborated with Standard Precipitation Index (SPI) instead of actual rainfall. Resolution of AVHRR time-series data facilitated study at regional-scale while MODIS datasets with 250m pixel resolution helped in identifying vulnerability at district-level. Study indicated that over 241 Mha areas in the country may not be vulnerable to rainfall variability induced climate change while over 81.3 Mha in arid, semi-arid and dry sub- humid regions may be vulnerable. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severally vulnerable while 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Study indicated that length of cropping season was decreasing due to delay in start of season. The paper also presents analysis of variations in SPI and resultant NDVI in typical drought and flood years in the country.

Keywords: MODIS, AVHRR, NDVI, Rainf ed Agricul ture, Vulnerability, Climate Change

Introduction

Climate change and human activities have emerged as significant factors affecting water resources in diverse regions (Kumar et al. 2022; Haddeland et al. 2014). The escalation in human water consumption for domestic and industrial purposes enhanced by the impacts of climate change has resulted in an overall decline in water availability (Liu et al. 2017; Paul et al. 2021). Rising temperatures and altered precipitation patterns directly influence water availability for ecosystems and agriculture potentially leading to greater evapotranspiration rates, intensified rainfall events, and prolonged droughts (Sharannya et al. 2021; Myers et al. 2023). Furthermore, urbanization, deforestation, and agricultural practices alter landscapes, affecting water resources in these areas.

Water shortages pose threats to ecosystem health, and socio-economic development and disrupt agricultural production (Piao et al. 2010). Thus, ensuring sustainable and efficient water usage necessitates accounting for water usage and quantifying it in a changing environment. To comprehensively assess the significance of water resources in the environment, Falkenmark (1995) introduced the concept of blue water (BW) and green water (GW) water, particularly for managing water resources in water-stressed regions. Green water refers to the portion of precipitation that infiltrates into the unsaturated soil layer and is subsequently utilized by plants through transpiration and soil evaporation. In fact, “blue water” represents surface and groundwater flow in rivers, lakes, and aquifers (Abate et al 2024; Sharannya et al. 2021). While numerous studies have investigated the spatial and temporal variation of BW and GW using the Soil and Water Assessment Tool (SWAT) (Feng et al. 2021; Li et al. 2009; Lyu et al. 2019), fewer publications have focused on assessing these variations and uncertainties at the basin scale under varying climate scenarios (Sharma et al. 2023). Hydrological models such as SWAT, serve as valuable tools for simulating past and future hydrological changes in the watersheds using projected climate data. SWAT specifically can simulate both blue and green flows at the watershed scale.

Abstract

Land development in urbanized watersheds poses a potential of increasing storm runoff rates, and therefore increasing the risk of flooding in the downstream areas of a watershed. To investigate the impact of change in land use practices on the watershed hydrology, an hydrological analysis was conducted on Little Kitten Creek watershed located near Manhattan city of Riley county, Kansas, USA. Prior to development, land use types in the watershed included woodland, pasture, and fallow land. However, rapid development has occurred in the watershed in the last ten years. Post development land use practices include commercial development, residential housing, and conservation (woodland and grasslands). Data collected and analyzed included digital elevation model (DEM) of study site, stream network and soil data using ArcGIS (ESRI, 2006) and Arc-Hydro Software (Maidment, 2000). Watershed characteristics such as slopes, channel length, circularity ratios, and drainage density were also computed. The delineated watershed boundary, stream segments and soil data layers were over laid to produce soil maps. Time of concentration (Tc) was determined for the pre and post development conditions in the watershed using seven different methods. The time of concentration values computed using Kerby, Federal Aviation Agency, Kinematic Wave and NRCS (SCS) methods found more reasonable compared to the remaining three methods used.

These computed time of concentration values were observed to be shorter for the post-development condition when compared to the pre-development condition of the watershed. The reduced time of concentration in the post development condition is attributed to increase in percentage of impervious areas due to increased residential development in the watershed; therefore increased rates of runoff in the watershed are expected.

Keywords: Rezoning, Time of Concentration, DEM, Kinematic Wave, Drainage density, Circularity ratio.

1. Introduction
Soil hydrothermal regime refers to the distribution and movement of water and heat within the soil. It describes the patterns of soil moisture and temperature variation over time and space. Understanding the hydrothermal regime is important in agriculture, ecology, and engineering, as it affects various soil processes, plant growth, and the behaviour of soil-related structures. The hydrothermal regime of soil is influenced by several factors, including climate, topography, vegetation, soil properties, and land use practices. The movement of water within the soil is driven by precipitation, irrigation, evaporation, and plant uptake (Aggarwal et al., 2009). It can be affected by soil physical properties such as soil texture, structure, porosity, and permeability. Water moves through the soil in response to hydraulic gradients, which are determined by the difference in soil water potential. Soil temperature is influenced by solar radiation, air temperature, and heat fluxes at the soil surface. It varies with depth and can be affected by factors such as soil color, moisture content, vegetation cover, and the thermal properties of the soil. The hydrothermal regime of soil has significant implications for plant growth and development. It affects processes such as seed germination, root growth, nutrient availability, and microbial activity. It also plays a crucial role in soil erosion, groundwater recharge, and the movement of pollutants within the soil profile. Soil’s thermal properties are pivotal in determining the microclimate, which subsequently influences critical stages like seed germination, the growth of seedlings, and the successful established of a crop stand. These properties are vital for modelling the movement of energy, water, and nutrients in soil. Soil thermal regimes are shaped by static properties such as texture and mineralogy and dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties, significant to significant change during various farming operations can be adjusted with suitable soil and crop management strategies. Soil moisture content has a significant effect on the soil thermal conductivity. The relationship between soil moisture and thermal conductivity

Abstract

This paper is the result of strong belief that if earth observation and geospatial techniques could be applied to a village to plan its natural resources in sustainable manner, the potential of this technique for common man is harnessed maximum. So analysis of one village natural environment, predicting its impact on humans, identifying certain possible inhabitable conditions due to existing nature & suggesting ways to utilize these conditions for sustainable human's and nature's existence  is the motive behind this work.

Astro-meteorology is considered as one of the branches of Astronomy along with Astrophysics, Astrobiology and Astrometry. Astronomy is the science that deals with the material universe beyond the earth’s atmosphere. It studies the nature and constitution of Celestial (heavenly) bodies and the laws which govern them including the effect they produce upon one another. It seeks to unravel the probable past history of heavenly bodies and their development. There exists a confusion between the branches of Astronomy viz., Astrophysics and Astro-meteorology with the term Astrology. Astrophysics uses principles and laws of physics to explain how Celestial bodies form and function or in other words it will help to ascertain the nature of the astronomical objects, rather than their positions or motions in space. However, the Astrology and Astro-meteorology are the subjects deal in future prediction and often considered as pseudo-science which uses position and movement of stars and planets for that purpose. Astrology links these to predict the human behaviour, while that of Astro-meteorology links the same to predict the future weather.

From ancient days, these predictions are based on one’s wisdom and knowledge in astronomy and often these astrologers are astronomers who keenly watch the stars and planets movement and guide their rulers and farmers with their guesstimates. Till date it is being lore, mainly passed to followers of this group and the methodology is not documented clearly and properly. There are many incidences of falsified predictions of astrology as reported by many reviewers elsewhere and we are not discussing the same here, owing to our interest in weather prediction i.e., astro-meteorology only. However, we have to recognize the works of the ancient astrologers who helped in developing the astronomical science.

Basically, there are two approaches in astrology viz., geocentric and heliocentric as used by the practitioner of astrology worldwide. As the name implies the Geocentric astrology is based on the calculation of planetary position as seen by the observer from the earth and thus earth is centre, while Heliocentric astrology bases its interpretation upon position within solar system with reference to the sun as centre. Though in true sense, the sun is the centre, the effect of motion as manifest on the earth is the basis of most astrological interpretation. Hence, vast majority of astrologers employs the geocentric calculations of the planetary positions for their predictions.

Augmented designs are useful when a large number of standard varieties or treatments are available for replication but new or promising materials are available only in limited quantities. This chapter introduces augmented designs as modifications of standard experimental designs in which additional, usually unreplicated, entries are incorporated alongside replicated standard treatments. Applications include testing new varieties, seedlings, medicinal plants, growth regulators, seedling carriers, and progeny under forestry and agricultural conditions. The chapter explains the principles of augmented designs and their applications in experimental research. It also discusses augmented Completely Randomized Design, including its layout, analysis of variance, and comparison of replicated varieties and unreplicated seedlings.

Traditionally, remote sensing encompasses a wide array of technologies and methods used to gather information about objects, areas, or phenomena without direct physical contact. This includes devices like seismographs, fathometers, radar systems, and more. These tools enable us to observe and measure various aspects of the environment from a distance.

However, in contemporary contexts, when we talk about remote sensing in a more specialized sense, we often refer to the use of satellite and aerial-based systems to capture and analyze electromagnetic radiation reflected or emitted from the Earth's surface. This form of remote sensing has become incredibly valuable in fields such as environmental monitoring, urban planning, agriculture, forestry, and disaster management, among others.

Modern remote sensing techniques leverage sensors capable of detecting specific wavelengths of electromagnetic radiation, such as visible light, infrared, and microwave radiation. By analyzing the patterns and intensity of this radiation, researchers can derive valuable information about the Earth's surface features, including vegetation cover, soil moisture, land use, and changes over time.

Soil sampling is an integral and essential component of soil fertility evaluation and nutrient management research. The effectiveness of soil sampling is a prerequisite for soil testing to achieve its goals for efficient nutrient management and plant nutrition for improved yield and quality. The underlying basis for soil sampling is that a soil sample taken represents the “population” which may be a plot, field or a watershed. It further implies that nutrient status of the representative soil sample(s) determined in a laboratory would reflect nutrient status of a plot, field or watershed and is of interest for correcting nutrient disorders in the field or watershed.

The most important factor that influences the effectiveness of soil sampling is soil heterogeneity. However, in a relatively homogenous group of fields or plots, a small number of samples may be sufficient to represent the population to a more heterogenous group of fields that would require more number of samples to represent the soil population. Whenever, soil samples to be taken in field following points needs to be kept in mind.

Biodiversity provides the building blocks for sustainable food, health and livelihood security systems. It is the feedstock for both the biotechnology industry and a climate-resilient farming system. Because of its importance for human well-being and survival, a Convention on Biological Diversity (CBD) was adopted at the UN Conference on Environment and Development held at Rio de Janeiro in 1992. The Convention’s triple goals are conservation, sustainable use, and equitable sharing of benefits. CBD defines biological diversity as follows:

Variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems.

Demographic explosion, environment pollution, habitat destruction, enlarging ecological footprint, widespread hunger and unsustainable lifestyles, and potential adverse changes in climate all threaten the future of human food, water, health and livelihood security systems. 2010 appears to mark the beginning of uncertain weather patterns and extreme climate behaviour. Events like temperature rise, drought, flood, coastal storms and rise in sea level are likely to present new challenges to the public, professionals and policy makers. Biodiversity has so far served as the feedstock for sustainable food and health security and can play a similar role in the development of climate-resilient farming and livelihood systems. Biodiversity is also the feedstock for the biotechnology industry. Unfortunately, genetic erosion and species extinction are now occurring at an accelerated pace due to habitat destruction, alien species invasion and spread of agricultural systems characterised by genetic homogeneity. Genetic homogeneity increases genetic vulnerability to biotic and abiotic stresses. To generate widespread interest in biodiversity conservation, the UN General Assembly has declared 2010 as the International Year of Biodiversity.

The country is at long last becoming sensitive to the serious consequences of the growing rural-urban divide in terms of investment, infrastructure and opportunities for income and employment. The rural-urban divide also leads to an expanding rich-poor divide. Since crop and animal husbandry, fisheries, forestry, and agro-processing are the main sources of rural livelihoods, the current agrarian crisis is adding to the problems of hunger, poverty and unemployment. According to the Union Planning Commission, we are off-track in achieving most of the UN Millennium Development Goals. A major cause for the growing rich-poor divide both between and within nations is unequal access to modern technology. Technology helps to achieve a paradigm shift from unskilled to skilled work and thereby move large numbers of the rural poor from the primary to the secondary and tertiary sectors of economic activity. If technology has been a major factor in promoting economic and social divides in the past, the challenge now lies in enlisting technology as an ally in the movement for economic, gender and social equity.