eChapters

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

4.1 INTRODUCTION

Database management system is a computer program to store, search, manipulate retrieve and give the result. Vast amount of spatial data are available and it is necessary to arrange them, store them for retrieval and analysis. Attribute data adds intelligent to GIS. Without database, a GIS will be just a Cad software. Cad softwares do not have database. GIS softwares have their own databases besides it has the potential to link with external database like Oracle, MS Access, MS Excel, Informix, INGRESS, Sybase and dbase. The data available in paper has to be digitally stored in a proper order. Previously the data were stored in a a flat file format where the data was stored without any structure. But to search and retrieve and analyse the database, database structures were developed. There are of 4 types of database structures namely Hierarchical, Network, Relational database and Object orientation.

Tables are made of rows and columns. Rows represent a map feature. Columns represents characteristic of map feature. Row is called a record or a tupule column is a field or an item. Table 4.1 is a table where rows 1, 2 and 3 represent the locations with the soil types. A column soil type represents various soil types.

The content presented in this chapter is exploratory and reflects emerging trends in integrating weather intelligence with decision support systems. The term “weather intelligence” is introduced as a framework, with ongoing research expected to further refine and expand upon these ideas as technologies evolve. In an era where climate variability and extreme weather events increasingly threaten global food systems, the integration of advanced decision support systems (DSS) with what we are now calling weather intelligence has emerged as a transformative paradigm. This chapter explores how food production— encompassing agriculture, fisheries, and animal husbandry—can harness integrated weather intelligence to inform real-time crisis management, strategic planning, and risk mitigation. The discussion is exploratory in many respects, as the concept of weather intelligence represents an innovative framework that challenges traditional forecasting methods and decision support tools (Fig. 9.1). Drawing on multidisciplinary research and practical case studies, we examine the building blocks of decision support systems tailored to food production. We then discuss the integration of weather intelligence into crisis response and strategic planning, highlighting both established risk mitigation approaches and emerging tools designed to anticipate and respond to extreme weather events. By contextualizing these developments within the broader scope of sustainable food production, this chapter offers an in-depth analysis and a forward-looking vision for decision support and response management in a rapidly changing climate.

Given this immense significance, the ability to accurately predict the behavior of the monsoon has always been a paramount concern. The science of monsoon forecasting has evolved significantly over time, transitioning from rudimentary observational methods to sophisticated numerical weather prediction models powered by advanced computing capabilities. This chapter delves into the intricate world of monsoon forecasting, exploring its historical roots, the scientific principles that underpin it, the diverse methodologies employed, and the continuous efforts being made to enhance its accuracy and utility. We will embark on a detailed journey through the complexities of the Indian monsoon system, examining the atmospheric and oceanic factors that drive its variability. From understanding the large-scale circulation patterns to deciphering the influence of regional phenomena like the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), we will unravel the key drivers that forecasters consider. The chapter will then explore the spectrum of forecasting approaches, ranging from statistical models based on historical data to dynamic models that simulate the intricate physical processes of the atmosphere and ocean. We will also discuss the role of satellite technology, ground-based observations, and high-performance computing in generating increasingly detailed and reliable forecasts. Furthermore, this chapter will critically assess the current state of monsoon forecasting, acknowledging both its advancements and its inherent limitations. We will address the challenges in achieving the desired levels of accuracy, particularly at regional and local scales, which are most relevant to farmers. Understanding these limitations is crucial for setting realistic expectations and guiding future research directions. Finally, and perhaps most importantly, we will examine the crucial link between monsoon forecasting and the needs of the agricultural sector. We will revisit the expectations of farmers regarding forecast information – their need for accuracy, timeliness, localization, specificity, and actionable advice. By understanding the gap between current forecasting capabilities and these requirements, we can better appreciate the ongoing efforts to bridge this divide through improved forecasting techniques and effective dissemination strategies. This chapter aims to provide a

Soil Organic Matter

Soil organic matter plays an important role in the maintenance and improvement of soil properties. It is a dynamic material and is one of the major sources of nutrient elements for plants. Soil organic matter is derived to a large extent from residues and remains of the plants together with the small quantities of animal remains excreta, and microbial tissues. Soil organic matter is composed of three major components i.e. plant residues, animal remains and dead remains of microorganisms. Soil organic matter comprises residues of plants and animals and these compounds occur in soil in close combination with inorganic substances. Animals and plant residues are made up of complex carbohydrates, simple sugars, starch, cellulose, hemicellulose, pectins, gums, mucilage, proteins fats, oils, waxes, resins, alcohols, aldehydes, ketones, organic acids, lignin, phenols, tannins, hydrocarbons, alkaloids, pigments, etc.

The organic matter subjected to microbial decay in the soil comes from several sources like plant remains, forest litter, incorporation of plant tissues, animal tissues, and excretory products. The chemistry of organic matter is very complex, and investigations of the transformations and the responsible organisms have therefore been extremely interesting. The organic constituents of the plants are commonly divided into six categories.

Soil organic matter is among the most complex materials existing in nature. In addition to the organic constituents present in undecayed plant and animal tissues, soil organic matter contains living and dead microbial cells, microbially synthesized compounds and an endless array of derivatives of these materials produced as the result of microbial activity. Soil organic matter probably contains most of the naturally occurring organic compounds. Some components of soil organic matter are no doubt distinctive to the soil environment, particularly those involving inorganic-organic complexes.

6.1. Composition of Soil Organic Matter

The composition of soil organic fraction is as follows:

1. Components of organic residues undergoing decomposition

7.1 General Description

Shifting cultivation is a type of land use that dates back to the dawn of agriculture. It's done in places where forest resources are still plentiful. It usually refers to the gradual transformation of primary forests into a mosaic of secondary forests at varying stages of growth, rather than the complete abolition of forests. However, primary forest removal occurs gradually in order to preserve the same fallow time despite population development. As no primary forests remain (or as the remaining primary forests are entirely protected by local customs or conservation legislation), secondary forests fade away, and new techniques arise or are adopted, the fallow period shortens. Natural capital is gradually replaced by man-made productive capital, or the system approaches a state of crisis, depending on whether the limitations to these changes can be levered. In theory, the new land uses allow for more forest protection because they require less territory to provide the same output. In practice, they are triggered or triggered by a series of more radical changes, such as infrastructure development, migrations, financial investments, and new consumer habits, all of which frequently end in large-scale deforestation.

Deep learning has significantly advanced automated plant phenotyping and disease detection by enabling computers to identify complex patterns in large volumes of plant imagery and sensor data. This chapter examines the fundamentals and applications of deep learning architectures in plant science. It discusses convolutional neural networks for image-based plant analysis and advanced architectures including recurrent neural networks, transformers and hybrid models. Data acquisition through imaging and sensor technologies is also considered. The chapter highlights how deep learning can support automated identification of plant characteristics, disease symptoms and crop health conditions. Challenges related to datasets, environmental variability, computational requirements and model generalization are examined alongside emerging research opportunities.

Deep learning emerged as a serious contender in the field from a 10-year explosive computational development. Deep learning is therefore a special form of machine learning, the algorithms of which are based on the structure and function of the human brain.

Machine learning VS Deep learning

Deep learning is arguably the most efficient form of machine learning. This is so important because they know the best way to solve the problem and how to fix everything. The following is a description of deep learning and machine Learning:

2.1. GENERAL DESCRIPTION

Forests covered only 31 per cent of the land in the world in 2011, while land used for agriculture, (cultivable land, perennial crops and pastures), covered 38 per cent (FAO 2011). In the history of humankind, agriculture is a recent invention (Mazoyer and Roudart, 2005). With the transition from the huntergatherer lifestyle to one of agriculture during the Neolithic Revolution, farmers quickly expanded their activity into forest areas. They developed shifting cultivation, an agricultural practice with temporal rotations in forest ecosystems. Shifting cultivation still exists today in tropical regions and is the livelihood basis for hundreds of millions of people (Mertz et al., 2009). The range in the population estimate is extremely wide, because of difficulties in counting smallholders spread out under the tree canopy at the margins of tropical forests, as also due to confusion that exists in differentiating shifting cultivators from other farmers involved in small-scale agriculture (Thrupp et al., 1997).

What is a Farming System?
A farming system is a complex and interconnected set of components, activities, and interactions that occur on a farm. It’s a holistic view of agriculture that considers not only the crops grown but also the livestock raised, the resources used, the environment, and the socio-economic factors that influence the farmer’s decisions. It aims to optimize productivity and sustainability. A farming system is a population of individual farm systems that have broadly similar resource endowments, enterprise patterns, household livelihoods, constraints, and for whom similar development strategies and interventions would be appropriate. It is a complex interwoven mesh of soil, plant, animal, people and other resources surrounding by a distinct boundary jointly managed by farm households. A farming system refers to a complex, interrelated matrix of soil, water, crops, animals, labor, and other resources managed by farm households to increase productivity and ensure food security. It goes beyond crop production alone—it is a holistic concept that includes all on-farm and off-farm activities contributing to a family’s livelihood. Farming-based livelihood systems emphasize the role of agriculture and allied sectors in supporting rural life and sustainability.
• It considers the interactions between different components of the farm (crops, livestock, soil, water, etc.) and how they are managed by the farm household.
• The aim is to optimize resource use, increase productivity, and improve the sustainability of agricultural production.

6.1 general description

Between 13,000 and 3,000 BC, Neolithic period, mountain people in Asia, Africa, and Latin America have practiced shifting cultivation (Mazoyer and Roudart 2006). A cultivation phase, consisting of removing forest and crop production for 1 to 3 years, is followed by a fallow phase, in which agriculture is discontinued to allow soil nutrients to recoup (Conklin 1961). Conklin (1961) distinguished between total and partial shifting cultivation. Pioneer shifting cultivation, in which primary or apex forest is cleared annually, and replaced by shifting cultivation, which entails clearing mostly secondary forests, are both essential forms of cultivation that are primarily practised for farmers' subsistence. Partial shifting cultivation, on the other hand, is done as a supplemental or temporary activity (Conklin 1961). Throughout history, indigenous peoples have used fire to manage their land and natural resources. Pastoralists and hunters-gatherers in Africa's savannas, for example, have used fire to preserve the ecosystem's productivity for livestock and game for millennia (McClanahan and Young 1996). In pre-colonial times, indigenous peoples in North America also significantly altered the landscape, establishing and preserving pastures and open forest savannas through regulated burning (Pyne 1982, cited in: Schneider 2000).

Herbicides degrade over time. The persists of herbicides is dependent on several factors including light, temperature, and substrate moisture. Several processes are responsible for herbicide degradation and include the following:

    1.    Photodegradation: The decomposition of herbicides caused by duration or intensity of sunlight is called as Photodegradation. Photodegradation occurs when ultraviolet (UV) light breaks chemical bonds of the herbicide active ingredient. Secondary molecules resulting from the cleavage of the parent molecule are generally less effective in providing weed control. Herbicides such as Monuron, Trifluralin, PCP, Chloramben etc are prone to photodegradation. So these herbicides should be applied in evening or incorporated in to the soil just after application in order of avoiding photodegradation.

Biological activity or index of a soil is the function of number of organisms present in soil and their physiological efficiency. The rate of respiration can be used as an index of the biological activity of soil as it reflects the physiological efficiency of the organisms. Monitoring of dehydrogenase, which are respiratory enzymes and integral part of all soil organisms, will give a measure of biological activity of soil at a given time.

During respiration biological oxidation of reduced compounds occur which is catalyzed by dehydrogenase. In the process TTC gets reduced to a pink coloured compound Triphenyl formazan (TPF) which can be quantitatively extracted by methanol and measured colorimeterically. The soil dehydrogenase activity in the current experiment was measured as per the methods of Cassida, (1977).

13.1. Introduction

The Delphi technique is a systematic, interactive forecasting method that involves gathering expert opinions to make informed decisions, predict future trends, or address complex problems. The core idea behind this technique is to use the knowledge and insights of experts in a structured manner to produce well-informed and reliable outcomes. By combining expert judgment with iterative feedback, the Delphi method allows for deeper analysis, greater accuracy, and a better understanding of complex issues that cannot be easily solved through traditional data analysis or quantitative methods (Ismail and Taliep, 2023).

Originally designed for defence-related forecasting, the Delphi technique has since evolved into a versatile tool used across a wide range of fields, such as business, healthcare, education, public policy, environmental science, and social research. Its versatility stems from its ability to gather expert opinions in a manner that allows for rigorous analysis of complex issues, without the constraints of traditional group meetings or face-to-face discussions.

Introduction

Food security problem has multiple dimensions that range from ensuring the food supply at global, country and local levels to create its effective demand round the year. The ultimate goal of an effective food security policy is to provide individuals with adequate dietary requirements through availability and accessibility of food, which are necessary conditions for nutritional well being. The level of food security existing in a country, however is a complex interactions, operating at different levels from the macro to the micro level. The components of the system would include international trade and macro policies, the agricultural sector, the market economy, consumption pattern, the micro economy or household income, urban-rural differentiation and gender issues, etc.

Food insecurity is a complex issue having several dimensions such as, poverty, unemployment, famine, gender discrimination, inequity, starvation, food and nutritional practices, for human growth, natural calamities and so on. Poverty has been recognized globally both as a cause and consequence of food insecurity. In the mid 1980s, 72 per cent of the world’s poor and 66 per cent of the world’s extremely poor lived in the Asia-Pacific region. In this region, 272 million people would suffer from chronic malnutrition during the next decade i.e. in the year 2010. In Asia-Pacific region, India alone has 204 million undernourished people, which is quite alarming when compared with neighbouring countries like China, which has only 164 million undernourished people despite being much populous than India. World Health Organization (WHO) report (1998) points out that health expectancy is more important than life expectancy. The impact of poverty and malnutrition on health has widened the gap between the rich and the poor.

Abstract

Population of Rajasthan as per Census 2011 is 68,621,012 persons which is much higher than census 2001 that records 56,507,188. It includes 12 districts of Rajasthan. Western Rajasthan covers about 61% area of the total geographical area and 39.51% population of the total population of the Rajasthan. The growth rate of population is much higher than that of the Rajasthan State. In the census 1981 Western Rajasthan had registered a phenomenal increase of 35.48 per cent over 1971 population. Regional pattern of growth rate in western Rajasthan shows that extreme hot arid region district (Barmer) is on top having 32.55 per cent growth rate whereas Indira Gandhi Canal Region (Ganganagar) district has least population growth of 10.06 per cent only. In 1981, the average urban growth rate had been 54.52 per cent. In 2001, the rural density of population was 83 persons per sq. km.

Introduction

The various reactions of nitrogen (N₂) cycle, transform one form of the element to another. Mineralization liberates nitrogen in inorganic form, immobilization converts its back to an unavailable state, while nitrification changes the nitrogen from a reduced to oxidized state. Certain transformations of nitrogen lead to a net loss of element from the soil through volatilization. For the purpose of crop production, nitrogen volatilization has a deleterious influence because it depletes part of soils reserve of an essential nutrient. The sequence of steps that results in gaseous loss is called denitrification. So denitrification refers to the reduction of nitrates and nitrites to gaseous nitrogen products, principally di-nitrogen and nitrous oxide, coupled to energy production through oxidative phosphorylation.

10.1 Introduction

Biomass fuels are a potential source of renewable energy. One of the major barriers to their widespread use is that biomass has a lower energy content than traditional fossils fuels, which means that more fuel is required to get the same amount of energy. When combined — low energy content with low density -the volume of biomass handled increases enormously. Compaction or densification is one way to increase the energy density and overcome handling difficulties.

Densification means compaction of loose material or to increase density of loose biomass so that its volumetric efficiency can be increased. Densification essentially involves two parts; the compaction under pressure of loose material to reduce its volume and to agglomerate the material so that the product remains in the compressed state. The resulting solid may be a briquette, a pellet and a cube. It will be briquettes if roughly, it has a diameter greater than 30 mm. Smaller sizes are normally termed pellets though the distinction is arbitrary. The process of producing pellets is also different from the typical briquetting processes. The densified product can be developed in the cubical shapes as well.

If the material is compacted with low to moderate pressure (0.2-5 MPa), then the space between particles is reduced. Further, increasing the pressure will, at a certain stage particular to each material, collapse the cell walls of the cellulose constituent, thus approaching the physical, or dry mass and more, density of the material. The pressure required to achieve such high densities are typically 100 MPa plus. This process of compaction is entirely depending on to the pressure exerted on the material and its physical characteristics including its moisture content.

3.1. Determination of Bulk Density of Soil

3.1.1. Determination of Bulk Density of an Undisturbed Soil

Bulk density of soil is expressed as the ratio of the mass (weight) of soil particle to their total volume including the pore space between the soil particles. Alternately, it is the weight of unit volume of dry soil. It is usually expressed in the unit of gram per cubic centimeter (g/cc) or mega gram per cubic meter (Mg/m³). In fine textured soil bulk density varies from 1.00 to 1.60 g/cc, whereas in coarse textured soil from 1.20 to 1.80 g/cc.