Introduction

The seismic method is an active form of geophysical surveying that uses elastic waves to investigate the subsurface. The waves are created by a source and propagate through the subsurface before being recorded by detectors that measure deformation of the ground (Fig. 6.1). The deformation of the ground as a function of time since the waves were created comprises a time series, which is called a seismic trace. The passing of a seismic wave appears as a deflection of the trace, referred to as an arrival or an event. The path of the waves from source to detector is controlled by the elastic properties of the material through which they travel. Discontinuities in the elastic properties deflect and divide the seismic waves so that the detectors record a series of waves that have taken different paths through the subsurface. It is by identifying these different arrivals and analysing their travel times and amplitudes that the nature of the subsurface can be inferred.

Most seismic surveys use sources and detectors located on, or near, the Earth’s surface. Surface seismic surveys are of two types. Surveys that particularly exploit waves reflected at elastic discontinuities are known as seismic reflection surveys, whilst those based on waves deflected so as to travel parallel to the discontinuities are known as seismic refraction surveys. Of all the geophysical methods, seismic reflection surveys provide the most detailed information about the subsurface, albeit at the greatest cost (see Fig. 1.3). Reflection survey data are displayed in a pseudosection/pseudovolume form where the travel times of the seismic waves provide an approximate indication of depth; but accurate time-to-depth conversion is difficult. Refraction surveys provide less detailed information, detecting only major changes in elastic properties, although the positions of these boundaries can usually be accurately determined.

INTRODUCTION

An understanding of the processes, principles and history of garden design is important for anyone involved in garden and landscape planning. It also increases enjoyment from simply visiting gardens.

A well-designed garden or landscape will fulfil its function at all levels. It will be aesthetically pleasing, inspiring, and yet practical and easy to use, such as the Winter Garden at Sir Harold Hillier Gardens, Romsey, described in the case study and illustrated above. The starting point for good design is an understanding of the principles of design, which apply equally to small domestic gardens and large parks. Linked with an understanding of design principles is an understanding of garden history. Styles have changed throughout history, associated with fashions for a variety of formal and informal designs. Great influxes of new plants into cultivation in particular eras have also influenced these changing styles.

Gardens and parks need to be accessible for everyone. The educational potential for children is immense. Gardens, parks and green spaces provide fresh air, exercise and stimulation. The value of these throughout history has not always been recognised to the same degree, but it is again being brought to the forefront with links to the benefits of a healthy lifestyle.

It is also vital that garden planning is carried out with sustainability in mind (see Chapter 18). A garden designed for wildlife will make a valuable contribution, providing different habitats, shelter and food sources for our native fauna. It does not matter what size the garden is, it is an effective way of gardening on any scale.

INTRODUCTION

Horticulture is, and has been, science based for many millennia. When humans, hunter-gatherers, first started to collect wild seeds and plant them as part of our initial ‘agricultural revolution’ twelve thousand years ago, we were unknowingly selecting the seeds that contained the genetic information desirable for our use. Subsequent generations of selection helped produce the agricultural and horticultural crops we enjoy today. However, the refinement and development of the plants used by humans is an ongoing process but with an expanding understanding being used.

Today the science of horticulture takes on a much more rigorous approach in striving for refined plants or the conservation of species both in the wild and cultivation. This work includes field trials, trials under glass, plant collections, pesticide and herbicide research and development, and, of course, genetic engineering.

This chapter explains some of the processes and organisations involved in horticultural science, and discusses some of the reasons why such scientific rigour is required in order to achieve the high standard of plants sought in horticulture.

The Millennium Seed Bank Project at Wakehurst Place, Surrey, is highlighted as a leading site of plant conservation and also shows the co-operation between national institutions, in this case the National Trust and the Royal Botanic Gardens, Kew.

INTRODUCTION

As with every complex endeavour it is always best to learn, or at least appreciate, the foundations of that subject or topic. Horticulture provides one of the most challenging but rewarding mixtures of endeavours, encompassing, but not limited to: art, chemistry, design, faith, frustration, health, history, languages, patience, physical effort, relaxation, religion, science, social development, therapy and wildlife.

This part comprises the chapters that relate to the background of growing and using plants. Answering those perennial horticultural questions: How does a plant work? What situation should it grow in? Where can I use it for maximum effect? Why was it so good last year/month/week?

Please use it to introduce, re-acquaint or remind yourself of the wonders that are found in the natural world and that you can tap into to provide a lasting and satisfying result: be it food production for the family, a wildlife haven, or a green oasis away from the busy world of today.

Starting with the range and development of the ‘five kingdoms’ classification and naming of plants, this leads on to the structure of plants in their many forms, and the new and developing language, such as eudicots, providing valuable technical updating for all those with an interest in plants. Also covered are the basic environmental conditions required for successful plant establishment and growth, this is encompassed within the requirement and effect of light, water and its importance for plants, and the ever-changing and most talked about topic: climate, weather and seasonal effects. This must be one of the most challenging aspects of modern gardening. With no growing season being the same as any other, and the range of temperatures within a 24-hour period being so wide, this continues to focus the mind of everyone growing plants.

The examples in Chapter 2 demonstrate that many time series in economics and business have a trending pattern, where for macroeconomic time series such trends typically move upwards. Although many practitioners would be able to indicate roughly what a trend is (“a general tendency for a variable to increase or decrease over time”), a formal definition of a trend cannot be given otherwise than in the context of a model. Put differently, only after we have agreed upon a time series model that (presumably) describes the data at hand, we can define a trend within this framework and discuss the “trend properties” of the time series in a meaningful way. In this book, the focus is on ARMA-type time series models, and hence the current chapter deals with trends within this model class.

For several reasons it is important to investigate the appropriate formulation of the trend in a time series prior to putting effort into modeling other data features and forecasting. First and foremost, the trend will dominate long-run out-of-sample forecasts, although also short-run forecasts can be affected, as we will see in Section 4.4. An inadequate specification of the trend possibly leads to forecasts that are biased or inaccurate in other ways. Second, a time series that displays a trend is nonstationary, in the sense that it does not have a constant mean. In addition, depending on the nature of the trend, the unconditional variance of the series is not constant over time but increases with every new observation.

INTRODUCTION

One of the most rewarding elements of gardening is that of harvesting and consuming what you have just grown, the taste is outstanding, whatever the crop, and the sense of achievement immense. The range of crops becoming available, both for new species and cultivars, is on a world scale; the variation of colours, textures and tastes is increasing with each new growing season.

The opportunity to grow edible crops across a wide range of sites is increasingly being taken by the widest range of people imaginable, fuelled on one level by concerns over international biosecurity: uncontrolled use or high levels of chemicals, bacterial or fungal infections and unbalanced nutrient levels. Add to this the general perception of modern food having bland flavouring and low nutritional levels, and the drive and stimulus to grow your own crops has captured most people’s imagination and heightened their interest as to what can fit into a smaller modern space.

All-year-round cropping, regional variation and a return to seasonal continuity have all fuelled this drive to grow your own. Not to mention the wider benefits: the reduction in food miles and the potential improvement in general health. Also seen is the social side of allotments and community projects to develop or rekindle neighbourhood relationships, a true activity for all the family, together with the positive therapeutic advantages.

This chapter encompasses the range of food crops becoming available to the grower, including herbs, nuts and the developing fruit and vegetables, whether old favourites that have fallen out of favour or newer cultivars. Included are tips and recommendations as to their selection, arrangement, suitability, timings, growing, management, maintenance, harvest and storage. Also covered are the practicalities of producing these crops: dealing with diseases, disorders and weeds. The control of pests is covered more broadly in Chapter 17.

INTRODUCTION

The shapes and forms of plants are the structural backbone of any well-designed garden. They continue to provide interest throughout the year, especially in winter when the plants that provide colour through the changing seasons are much less in evidence. Size is also important for a number of reasons. Over-large plants have no place in small gardens, supermarkets increasingly require produce of a uniform size as well as shape, while saleable pot plants need to be compact and in proportion to their containers.

There is a great natural variation in the shapes and sizes of plants. The branching habit determines the architecture of the plant and shapes vary from upright forms such as Prunus ‘Spire’, where branching is largely suppressed, to those with spreading canopies such as oak, Quercus robur, and Prunus × blireana. Even small plants vary in habit from low spreading types, Thymus serpyllum, to those consisting of compact rosettes such as Saxifraga cochlearis ‘Minor’. Although shape is ultimately regulated by their genetic constitution, plants also have a remarkable degree of plasticity, which enables them to be modelled into a variety of different forms by the gardener. One only has to consider the difference between a step-over apple tree and a fully grown standard to realise the extent to which size and shape can be modified even in the same cultivar. This chapter considers the many factors that can modify shape and size, emphasising those that are under the control of the gardener.

INTRODUCTION

Using the soil and other products to grow and sustain plant material has been practised since the earliest times, when the volcanic dust settled and the spores and seeds began to grow. An understanding of the type of soil that we are operating on, its capabilities, how to improve, cultivate and manage it, has been the main topic of conversation when and wherever growers of plants meet. A number of different alternatives, e.g. peat free, have become available, but which is the best route to follow? Or is a combination of recommendations the one to produce the most balanced, productive and sustainable media?

The area of cultivation or non-cultivation is another topic of discussion, with guidance as to the terms employed and the order of use. Unfortunately, short cuts have been used with the aid of modern machinery that can do untold damage, which may take years to correct. Equally, what are the benefits and limitations presented by the no-dig policy and is it successful in all situations?

Once germinated, planted or established, the nutrient requirements of a wide range of plants, their situation and the timing of use, may depend on the origin or source: be it artificial or organic. This is discussed, with linkage to nutrient cycling, organic matter breakdown and pH, together with the identification of different plant growth stages and the nutrient requirements that these bring. How to prevent damage or delay in the harvest or aesthetic quality of whichever plant species you are growing is also discussed, including how plants uptake nutrients and adapt themselves to the media present.

INTRODUCTION

Water is an essential component of all living cells. In addition, for plants water is needed to maintain the rigidity or turgor of cells and as a substrate for photosynthesis (see also Chapter 3). However, the amount of water in a plant at any one time is small compared with the amount that passes through on a bright day.

The problem for land plants is that a supply of CO2 from the surrounding air is needed for photosynthesis. This necessitates pores (the stomata) through which the gas can enter into the cells of the leaf. The plant must therefore be able to maintain an adequate supply of CO2 to the photosynthesising cells while preventing a net loss of water vapour to the atmosphere. If water is lost from the leaves at a faster rate than it can be supplied from the soil, turgor is lost and the leaves become flaccid and wilt.

This chapter describes how plants have evolved to prevent a net loss of water under different environmental conditions. The chapter also considers how gardeners can manage water most efficiently in the context of the garden, especially as climate change is likely to pose increasing problems of water management in the future, together with the issues of cost, sourcing, storage and quality as well as quantity, including practical opportunities such as utilising ‘grey water’.

Instances of excess water, such as flooding, are also discussed highlighting plants’ reactions to these situations.

Univariate time series models, as discussed in the previous chapters, can be very useful for descriptive analysis and for out-of-sample forecasting. Such univariate models are restrictive, however, in the sense that they rely purely on the history of the time series itself and do not try to exploit information present in other variables. Given that often economic variables are closely related with other variables, such information may be potentially useful and using it may lead to more realistic descriptions of the behavior of economic variables and also to more accurate out-of-sample forecasts. In fact, ignoring relationships with other variables may give rise to complications in the specification of an appropriate univariate time series model. It may be that the empirical specification of a univariate model is hampered by outliers and structural shifts, which may be attributed to one or more other variables than the yt variable under consideration. For example, the change in the trend of US industrial production around 1979.4 (see Figure 2.3) may be caused by the large oil price increase around that time. Additionally, the same oil price variable may be responsible for the large negative growth in production in 1974/1975, which seems to correspond to an innovation outlier. In other words, it may be worthwhile to include an oil price variable in a model for US industrial production to render it more realistic and more accurate.

This book concerns the construction of time series models for describing the dynamic properties of economic variables and for out-of-sample forecasting. The economic variables can originate from various subject areas in economics and business, including macro-economics, finance, and marketing. Specific examples of time series of interest are inflation rates, unemployment rates, stock market returns, and market shares. Out-of-sample forecasts for such variables are often needed to set policy targets. For example, the forecast for next year's inflation rate can lead to a change in the monetary policy of a central bank. A forecast of a company's market share in the next few months may lead to changes in the allocation of advertising budgets. The models in this book can be called econometric time series models because we use econometric methods for analysis.

Time series variables can display a wide variety of patterns. Typically, macroeconomic aggregates such as industrial production, consumption, and wages show an upward trending pattern. Industrial production, tourism expenditures, and retail sales, among many others, display a pronounced seasonal pattern, that is, tourism spending is usually largest during the summer and retail sales tend to peak around Christmas. Another feature is that certain observations on economic data look aberrant in the sense that they occur rarely and deviate strongly from the typical behavior of the variable. For example, if new car registrations are almost zero in a certain month because of a computer breakdown, this does not reflect the true sales of new cars.

Introduction

The use of geophysical methods in an exploration programme or during mining is a multi-stage and iterative process (Fig. 2.1). The main stages in their order of application are: definition of the survey objectives, data acquisition, data processing, data display and then interpretation of different forms of the data. The geologist should help to define the objectives of the survey and should have a significant contribution during interpretation of the survey data, but to ensure an optimum outcome, an understanding of all the other stages highlighted by Fig. 2.1 is required. Survey objectives dictate the geophysical method(s) to be used and the types of surveys that are appropriate, e.g. ground, airborne etc. Data acquisition involves the two distinct tasks of designing the survey and making the required measurements in the field. Data processing involves reduction (i.e. correcting the survey data for a variety of distorting effects), enhancement and display of the data, all designed to highlight what is perceived to be the most geologically relevant information in the data. The processed data can be displayed in a variety of ways to suit the nature of the dataset and the interpreter’s requirements in using the data. Data interpretation is the analysis of the geophysical data and the creation of a plausible geological model of the study area. This is an indeterminate process; an interpretation evolves through the iterative process as different geological concepts are tested with the data. It is often necessary to revise aspects of the data enhancement as different characteristics of the data assume greater significance, or as increased geological understanding allows more accurate reduction.

The interpreter needs to have a good understanding of the exploration strategy which was the basis for defining the survey objectives. Ideally the interpreter should also have a working knowledge of the geophysical acquisition–processing sequence since this impinges on the evolving interpretation of the data. The type of survey and the nature of the data acquisition affect the type and resolution of the geological information obtainable, whilst the interpretation of geophysical data is dependent on the numerical methods applied to enhance and display the data. Analysis of the data involves their processing and interpretation. We emphasise that interpretation is not a task to be undertaken in isolation; it is an inextricable part of the iterative and multi-stage analysis shown in Fig. 2.1.

WHY ANOTHER HORTICULTURAL TEXTBOOK?

Well, the world of horticulture is constantly changing, and the Royal Horticultural Society, one of the major guardians of excellence in this area, has rethought and restructured the information required to obtain its highly regarded qualifications. These changes make the transition through the levels of knowledge more logical and smoother for anyone wishing to know more about plant selection, propagation, growing, use and design.

This is the first textbook written to take account of these latest adjustments and provide sequential development over a wide range of topics, arranged in three broad parts, allowing quick access to an appropriate starting point for readers to move forward in their quest for horticultural knowledge and understanding.

Most people start learning about plants through their families, especially grandparents: the techniques and dates of practical operations have been handed down from one generation to the next, over time, but the reasoning and science behind these events has become confused or totally lost. Therefore with changes such as the advent of climate change, the expanded plant palette at our disposal, the re-awakening of interest in the natural world and the joy of feeding our families by home production of fruit and vegetables, we need to revisit our timings and methods and ensure maximum success with minimum inputs of resources, including the efficient use of valuable time.

In this chapter the focus is on key features of business and economic time series. It also serves to introduce several of the empirical time series that will be used throughout this book as running examples.

The five key features of economic and business data that we consider are (i) trends, (ii) seasonality, (iii) influential data points, (iv) time-varying (conditional) variance (conditional heteroskedasticity) and (v) non-linearity. Typically, an economic time series displays at least one, but usually two or more of these features. To keep matters simple, however, in this chapter each series is analyzed for only one of these five features at a time. In later chapters, the various possible models for each of these features will sometimes be combined to illustrate the practice of time series modeling.

In this chapter, each of the five data features will be illuminated using simple regression-based calculations. This should not imply that these regression models are the best we can do. They are merely helpful tools to demonstrate the properties of the data. A second important tool in this chapter is graphical analysis. In many cases it is already quite helpful just to put the data in a graph with the values of the observations (or some transformation thereof) on the vertical axis and time on the horizontal axis. However, in case of many observations or of a time series with large variance, it may sometimes be more insightful to construct scatter or correlation plots.

The volatility of asset returns, which is viewed as a measure of uncertainty or risk, plays a crucial role in financial decision problems such as risk management, option pricing, and portfolio management. One of the most prominent features of asset return volatility is that it changes over time. In particular, periods of hectic movements in prices alternate with periods during which prices hardly change, see Section 2.4. This characteristic feature commonly is referred to as volatility clustering. In this chapter, we discuss time series models that can be used to describe this feature. In particular, we discuss (extensions of) the class of (Generalized) AutoRegressive Conditional Heteroskedasticity [(G)ARCH] models, introduced by Engle (1982) and Bollerslev (1986).

The outline of this chapter is as follows. In Section 7.1, we discuss representations of the basic GARCH model. Several extensions are briefly reviewed in Section 7.2. We emphasize which of the stylized facts of returns on financial assets can and cannot be captured by the various models. Several aspects that are relevant for implementing GARCH models in practice are discussed in some detail in Section 7.3. This includes testing for conditional heteroskedasticity, parameter estimation, and diagnostic checking. In Section 7.4 we focus on out-of-sample forecasting. Both the consequences for forecasting the conditional mean in the presence of ARCH, as well as forecasting volatility itself are discussed.

INTRODUCTION: DIVERSITY AND EVOLUTION

In planting a garden, we celebrate diversity. There are the plants that we deliberately cultivate for our own benefit, but also the vast and often unseen array of microscopic organisms as well as the obvious birds and butterflies that we may actively encourage.

If you study nature, or are just intrigued by the diversity in the garden, it can perhaps at first be puzzling. Looking more closely at the range of diversity you soon realise there are patterns that when pieced together help to create a picture of life, which we can classify into a system that can be communicated. This pattern is the result of almost 4 billion years of organic evolution on Earth from a common ancestor that we all ultimately share. In sharing common ancestry, whether from millions of years ago or more recently, we have shared characteristics, such as the details of our cellular structure and chemistry or the form of a flower. These shared characteristics are the raw data that enable us to discover these patterns and ultimately build a classification. This provides a structure within which to name organisms and recognise their evolutionary relationships, a system that can be understood worldwide and without ambiguity. This is the science of systematics, which helps bring order and sense to this diversity, and an understanding of it is key knowledge in horticulture.