eChapters

Introduction

Tree improvement through the application of genetic principles is directed towards modifying the heredity of tree populations so that they are able to meet the needs of the forester and the wood based industry. The breeder of annual crop plants resorts to hybridisation, selection, testing of the improved strains, multiplication of improved seeds and finally their release for commercial planting in that logical sequence. But the tree breeder, whose career may be shorter than the life cycle of the tree species taken up for improvement, tends to carry out the above operations in a modified form and often some of the operations are carried out concurrently.

Many of our tree species are wild unselected populations. They are, therefore, expected to exhibit considerable natural variability for many characters and also to respond well to selection for further improvement. Further, altitudinal and latitudinal variation in many characters between populations are expected for those tree species which have a wide geographical distribution. So, the selection of desirable seed sources or provenances is an essential step that is basic to all tree improvement programmes. The right provenance is identified through well laid out provenance trials at a number of locations in the area where the improved selection is to be grown. Further improvement is then planned and executed for exploiting the naturally available intrapopulation variation. The variation normally measured is the phenotypic variation which is the result of interaction of the genotype of the individual with the environment. However, what is essential for selection to be effective is genetic variation for the character under consideration. Genetic variation is assessed by appropriate progeny trials. Since many of the economically valuable characters of tree species are polygenically controlled and exbibit continuous variation, measurements have to be in metric units. The use of biometrical techniques for assessing and analysing this variation, therefore, becomes an essential requirement.

Forage crops are plant species used to feed domestic animals (such as cattle, sheep, goat, pig, and poultry). Both cultivated and wild plants can serve as forage. The domestic animals feed on forages either by grazing or stall feeding (cut fodder eaten in stalls).

Grazing, that is pasturing of animals, is cheaper and allows production of both livestock and dairy produce at a lower cost because no expense is involved in growing, cutting, transporting or feeding. In North and South America, Australia, New Zealand, and in many other temperate countries pastures are widespread and popular because sufficient land is available. While land is cheap and plentiful, labour is scarce and expensive; hence management of natural, and in recent years sown pastures has received much attention.

Until about fifty years ago, the natural grasslands in the Western countries met the minimum needs of the farmers and grassland management was operated at a level comparable to subsistence agriculture of many developing countries. In other words, emphasis was laid on exploitation of natural productivity of the grassland, using a low level of management based on minimum use of inputs like chemical fertilizers. However, it was soon realized that productivity of native grasslands will have to be greatly improved if the high demands for meat and milk were to be adequately met.

In India, however, out of a total cultivable area of about 131.11 million ha less than 2 per cent is utilized for forage crops because feeding the large human population takes the first preference for land use to produce grain and cash crops. It is, therefore, necessary to intensify efforts, particularly in over-populated countries, for a more efficient production of high yielding forage crops to get maximum return out of minimum land.

Breeding methods for vegetable crops are basically similar to those adopted in others like cereals, oilseeds or pulses. Certain aspects, peculiar to vegetables, however, require specific attention: (i) In many cases, the edible part of the plant is not the seed (as cauliflower, cabbage, carrot and onion) and hence while breeding for improved plant part that is used as vegetable, its seed production capacity needs due consideration, (ii) Vegetables like tomato, garden pea and onion are consumed in different forms (fresh or processed) and the end use of the product (like processing quality) has to be taken into account in breeding programmes, (iii) Consumer preferences (as for example in eggplant) size, colour and shape of the vegetable has to be given due emphasis, (iv) Being perishable in nature, keeping quality and suitability for long distance transportation are important attributes in vegetable crops unlike grain crops where bulk handling and haulage can be mechanised, (v) Another peculiarity with vegetable crops is that they vary widely in modes of reproduction necessitating appropriate choice of breeding methods. In addition to vegetatively propagated crops like potato, sweet potato, cassava (tapioca), garlic, pointed gourd or “Parwal (Trichosanthes dioica L.) and ivy gourd or ‘Kundru’ (Coccinia cordifolia L.), there are many seed propagated vegetable crops which are auto- or allogamous or intermediate (Table 1).

Within these broad groups, variations in the extent of cross- or self-pollination, namely, 15-20 per cent natural cross-pollination in chillies and 20-25 per cent natural self-pollination in muskmelon have been recorded.

Since genetic variability is essential for any crop improvement programme, the creation and management of genetic variability becomes central to crop breeding. Experimentally induced mutations provide an important source of variability. An advantage of such created variability is that the starting point can be an agronomically accepted cultivar rather than a genetic stock of no direct commercial value with many undesirable attributes. It should, however, be realized that none of the imposed treatments in a plant system has so far been able to direct a specific change. As a result, following mutagenic treatments, a mixed bag of induced variants is found and many of these may not be of any value. Also, many events take place concurrently in a treated cell to produce variation at several loci within a genome. Because of these reasons it will be unrealistic to expect miracles out of a mutation breeding programme. The desired results can be achieved only when the objectives are clearly defined and the experiments are specifically designed to achieve those objectives. When a mutant of the required type is obtained, we only reach the step of identification of a desired recombinant in a conventional breeding programme and all the succeeding steps of evaluation of the mutant are in no way different from those in the conventional breeding procedures.

Genetic resources deal with germplasm of a given species that provides the necessary genetic variation for the improvement of that species in terms of particular products, increased efficiency and higher levels of productivity. Genetic resources are, in this sense, the building blocks, fundamental not only to a crop improvement programme, but also for the very survival of the species in time and space (Swaminathan, 1983).

As agriculture is becoming more and more intensive and location-specific, plant breeding objectives are becoming more and more complex. Besides high yield, a modern variety is often expected to combine specific duration, nutritional attributes, adaptation to varying soil and water regimes, and resistance to ever-multiplying pests and diseases—to mention only a few of the character combinations. To meet these objectives, a wide range of germplasm must exist, and the plant breeder should have an easy access to the genetic resources.

In conventional breeding for genetic upgradation of yield and other desirable attributes of crop plants, favourable assemblages of genes from two or more parents are made to develop a commercial variety. The important steps of this exercise are: locating the right kind of parents that possess the desired characteristics; making crosses between or among them; handling the segregating generations in an appropriate manner so as to identify the required recombinants; stabilizing the recombinants and finally evaluating their performance in a series of trials. This approach has been very successful in the sense that it has provided increments in productivity continuously. In an analysis done by the American Society of Agronomy on the genetic contributions to yield gains of five major crop plants, it was concluded that sorghum yields increased due to genetic improvements at the rate of 1.2 per cent per year between 1950 and 1980. For corn, the corresponding yield increase was 90 kg/ha/year between 1930 and 1980, which is equivalent to a rate of increase of 1 per cent per year. It was also estimated that 70 per cent of the total gains in corn yields are attributable to genetic improvement and 30 per cent of the yield gains are due to non-seed inputs like fertilizers, pesticides and cultural practices.

In India also, the improvement of productivity of the major food, fibre and several cash crops has been very impressive. The success is due to intensive research in plant breeding resulting in the evolution of varieties with very high genetic yield potentials and the concurrent support from other disciplines that has provided the package of practices which help in the realization of the created potential. In all crop plants, where the breeding efforts have been intensive, the phenomenon of plateauing of yields has become apparent. Wheat provides, perhaps, the best example of the situation. Since the 1970s, when the gains from genetic restructuring of the plant type had been established, major yield increases have not occurred. Since the demands of the future will be undoubtedly much larger, and additional cultivable land will not be available, ways will have to be found for a sizable increase in productivity per unit area in the immediate future. This requirement calls for the development of strategies and procedures that will make dramatic yield gains possible. For meeting the challenge, a continuous search is being made for innovative methods which will give yield increments of a much higher order than those provided by the conventional approach. In this chapter, an attempt has been made to introduce some such promising approaches.

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

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).

The satellite data is available in raster format. Raster data consists of two – dimensional array of pixels or grids which stores the values representing features. All the pixels are of same size and shape with their particular coordinate location. A raster data format is shown below.

Sensors onboard the satellite records the intensity of emitted or reflected electromagnetic radiations in the form of digital number (DN) for each pixel. These digital numbers can be transformed to more meaningful intensity units like radiance or reflectance. This process of conversion of digital numbers to radiance or reflectance is known as radiometric calibration. Sensor specific information is required to conduct radiometric calibration.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).

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.

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

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.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

Raster transformations involve mathematical manipulation of raster layers to generate new raster images. These transformed raster images either contain the information of interest or highlight the features which are not discernible in the original image. Transformations are also performed to preserve the essential information in reduced number of dimensions or layers. In this chapter, we are going to discuss few most commonly used transformation tools listed below:

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Supervised classification is the technique to classify raster image in which prior information about the classes in the form of training sites is provided by the user. Training site represents a homogeneous area in a particular class. The spectral response pattern of training sites is used to classify the raster image.The pixels of raster imagewhich have spectral response pattern similar to that of training sites of a particular class are grouped in that class. As there is some variability in the spectral response pattern of a particular class, therefore, multiple training sites per class are taken for supervised classification. Supervised classification of raster images can be performed by various methods. Conventional methods include maximum likelihood classifier, minimum distance to mean classifier, and parallelepiped classifier. Now a days, machine learning algorithms are also gaining importance in this context. These classifying algorithms are first trained or calibrated using the data of training sites.This trained model is then validated on the independent datasets for accuracy assessment. If the accuracy of the developed model is of acceptable level, then that model is used for the classification of raster data.

In this chapter, we are going to discuss the execution of maximum likelihood classifier and few machine learning techniques for supervised classification of raster image in R software. The training sites used in this exercise are in the form of points shapefile. The raster image is classified into six classesi.e. built- up, fallow soil, riverine sand, salt-affected soil, vegetation, and water. For performing this exercise, first of all,set the working directory and import the required packages.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Maps make it easier to interpret and communicate the results of any GIS analysis. R has a large number of packages for making thematic maps. Static mapping is straightforward with plot()methods provided by core spatial packages sf and raster. The use of plot() function have been discussed in the previous chapters. Another package for mapping isspwithspplot() having enough options for producing good quality thematic maps.It is also possible to create advanced maps using ggplot2. This chapter discusses, separately, preparing thematic maps from vectors and rasters with the above mentioned packages. A dedicated map-making package tmaphas been given special focus in this chapter.

Precise collection and processing of soil sample is the first and foremost step for carrying out any soil study, therefore it is essential to collect the coil sample very accurately keeping in view the variations in slope, colour and texture of the soil management aspects. Soil sampling should be avoided from non-representative sites like recently fertilized plots, bunds, channels, marshy tracts and spots near trees, wells, compost piles etc. It should be kept in mind that erroneous samples will never yield accurate result in spite of very improved method of analysis. The depth of sampling may vary according to the need of soil study and extent of rhizosphere. However, in general the sampling depth for field crops is the plough layer (0 - 15 cm) but it could be up to one meter or as per the purpose of the study. Soil samples in dry farming areas should be taken depth wise as the roots of the crops in this conditions reaches to deeper layers in search of water and nutrients.

Mainly two types of soil samples (disturbed and undisturbed) are used for determining the physical properties of soil. The disturbed soil samples are used for determination of particle size distribution, particle density, aggregation, column studies for diffusion-dispersion coefficients and soil-water diffusivity. Whereas, undisturbed (core) soil samples are used for the estimation of bulk density, porosity, retention and transmission characteristics of air and water.

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:

Bulk density is the ratio of oven-dry mass of soil to its total volume before drying. Knowledge of bulk density is useful in calculating moisture content by volume, porosity using particle density, volume of a known weight of soil, and weight of a furrow-slice. Bulk density is a direct measure of soil compaction. Bulk density is highly dependent on any kind of physical manipulation, degree of wetness, and kind and arrangement of soil particles. For this reason, preservation of original structure in the sample is most essential aspect of the methodology for determination of bulk density. Common methods of determining soil bulk density use soil cores or clods in their natural state.

Particle density of soil is the ratio of mass and volume of soil solids. Particle density of soil is an important physical characteristic because the interrelationships of porosity, bulk density and air space, and rates of sedimentation of particles in fluids depend on particle density.

Principle

The determination of particle density (ρ_p) requires the measurement of mass of the solid and its volume. The mass is obtained by weighing the oven- dry soil sample and volume by calculating from the mass and density of water displaced by the sample in a pycnometer. The pycnometer is a glass flask with a ground glass stopper having a capillary opening. The particle density can be calculated using the following equation:

Bulk density to which a soil can be compacted is dependent on water content and force of compaction. Adsorbed water on soil particles acts as lubricant and every addition of water increases ease of compaction to progressively a greater density. However, this increase in density will occur only to a certain value of water content beyond that it decreases with further addition of water in soil. The moisture content at which maximum dry density is obtained for a limited and fixed effort is called as proctor moisture and the density at that point is known as maximum or proctor density”.

Principle

A soil at varying water contents, compacted with a specific amount of compaction energy, would result in a bulk density-water content relationship. The maximum bulk density obtained at the proctor water content represents 100 % compaction. The test can be performed in laboratory on various soils using Abbot’s compactor

Proportion of solid, liquid (water), and gas (air) components in a soil mass indicates physical conditions such as soil structure, porosity and water content. A well Structured soil may have a high pore volume whereas a compacted soil may have little pore-space for other phases to occupy. In general, soil solids may occupy 50 % of the soil volume in a well-aggregated fertile loam and the remaining volume may be occupied by liquid and gases interchangeably.

Soil aggregates refer to a group of two or more primary particles which cohere to each other more strongly than to surrounding particles. Under field conditions, these units also cohere to some degree with neighboring aggregates and form larger aggregates. The aggregates form and break easily into smaller aggregates during tillage and by disruptive action of water and air. The aggregates maintaining their identity are those in which the cohesive forces among particles are greater than the disruptive forces. A quantitative characterization of aggregates is being done by determining aggregate stability and size distribution of aggregates. The aggregate stability refers to the resistance of soil aggregates to breakdown by water, air, and mechanical manipulations. Numerous methods have been proposed to determine size distribution and stability of soil aggregates (Kemper and Rosenau, 1986). The size distribution of wet and dry aggregates determines overall tilth, size of pores and susceptibility of aggregates to movement by water and wind.

Breaking of soil aggregates and dispersion followed by a rapid drying leads to rearrangement and coherence of soil solids in the form of surface crust. The cohesion exhibited by soil upon drying is directly related to surface crusting and clod formation. The formation of soil crusts and hard clods is a major structural feature of soils. Seedling emergence of cereal crops is generally controlled by point resistance around the shoot tip (Richards, 1953). Penetrometer, which measures the resistance to vertical penetration in soil, is an effective instrument to evaluate the crust strength. A relationship between penetration resistance and seedling emergence would serve as a guide to ensure optimum soil condition for a desired plant density.

Apparatus and Accessories

Penetrometer, moisture box, trowel, electronic balance and oven.

The variable amount of moisture contained in a unit mass or volume of soil and is the key factor affecting the growth of plants. Numerous other soil properties depend very strongly upon moisture content. Gravimetric (direct) and sensor based (indirect) methods are generally employed for soil moisture determination.

9.1 Gravimetric Method (Direct)

Principle

The gravimetric method is based on the principle that the water in the porous material is lost by evaporation on heating and the porous material undergoes an equivalent loss in its weight. However, the water loss may be limited by the internal operation within the complex porous material such as soil. This method is simple, routine, reliable, inexpensive, and easy to use. The major limitations of this method are that it is destructive, laborious and time consuming.

Soil consistency refers to the resistance offered by soil against forces that tend to deform it. The soil consistency varies with texture, kind of clay and moisture content. The efficiency of agricultural implements, animals and labour is largely affected by soil consistency as it directly inf luences the workability of a soil. Operations at suboptimal or supraoptimal water contents lead to clod formation, compaction, poor drainage and aeration. The common terms suggested by Atterberg (1912) to describe consistency are liquid limit, plastic limit, plasticity index, and sticky point. The physical basis of consistency limits is related to the interaction of water with soil. The water content at which plasticity is exhibited is the plastic limit or lower plastic limit of soil.

10.1 Liquid limit

Principle

The liquid limit refers to the water content at which resistance to f low becomes negligible and soil represents a semi f luid. A trapezoidal groove of specified shape cut soil held in a special cup will close in 25 taps on a hard rubber plate. The soil in the cup is 1.0 cm deep and the groove is 2.0 mm wide at the bottom with slopes outward at a 60^° angle horizontally. The soil in the walls of the groove f lows under the impact of the cup on the hard base and close the groove.

Soil pores differ greatly in size, shape, tortuosity, and continuity. They are characterized generally in terms of total porosity, air-filled porosity, and pore-size distribution. The characterization of pore space is important in understanding the storage and movement of water and gases, the development of plant root systems, the flow and retention of heat, and soil strength. The observation that bulk density is considerably lower than the average particle density indicates that only part of the bulk volume is occupied by soil solids and the remainder is occupied by a lighter material, such as air and water.

Total Porosity

Total porosity (n) is the percentage of the bulk volume not occupied by solids and can be computed using following relationship:

Tensiometer is a device to measure soil moisture tension or equivalent negative pressure of water in the soil. A tensiometer consists of a tube sealed at one end by a porous ceramic cup which is in contact with the soil and a vacuum gauge or a manometer connected at the other end above the soil surface. When the tensiometer is filled with water, the manometer or gauge will read zero tension. When such a tensiometer is placed in an unsaturated soil, the soil in contact with the porous ceramic cup will immediately start sucking water through the porous cup wall. The water in the soil pores immediately establishes hydraulic equilibrium with the water in the cup through saturated pores of the cup wall. Water and solutes can f low through the pores of the cup wall but not the air within the working range of the tensiometer. Water could be sucked from the tensiometer by the soil until vacuum created inside the tensiometer is just sufficient to overcome the suction of the soil. At equilibrium, the tensiometer reads directly the amount of soil suction. The outf low of water from the cup is very little. The effective range of the tensiometer is 0 to 0.85 bars depending on quality of the porous cup.

As water enters the soil, air is displaced and the soil pores in the upper part of the profile become saturated. A continuous supply of water leads to an increasing depth of saturation. If the supply of water at the soil surface is cut off then the rapid downward movement of water may essentially ceased within 48 hours. The soil is then said to be at its field capacity. At this stage macropores are drained off but micropores are still filled with water for use by plants. Since it is difficult to draw a boundary line between macro-and micropores and there is no sudden change in the profile drainage rate for many soils, it is not easy to specify a depth and time after which soil water movement is negligible. Therefore, measurements are necessary in each soil to specify the time and depth of sampling after wetting. Special care would be necessary in soils with impeded drainage and sand lenses, which produce perched water tables.

Over most of the wetness range in which plant roots normally function, all the water in soil is controlled by matric forces. A relationship between water content and matric or soil water suction is referred to as water characteristics of the soil. Increase in matric suction is associated with decrease in water content in the soil and the process is known as desorption. On the other hand with decrease in suction soil moisture content increases and this process is known as sorption. The curves showing relationship during progressive intake and withdrawal of water are known as sorption and desorption curves respectively.

Tensiometer is suitable for finding out soil water characteristic at tension values below 2.0 kPa, whereas gas phase pressure is required for higher tensions. A pressure chamber containing a porous membrane or a ceramic plate with all its accessories to provide regulated compressed air is most convenient. A detailed methodology to determine water characteristic curve by pressure plate apparatus is described below.

Water holding capacity of soil is the amount of water that a given soil can hold against the force of gravity. Soil texture and organic matter are the key components that determine the water holding capacity of a soil. Soils with smaller particle sizes (silt and clay) have larger surface area and can hold more water as compared to sand which has large particle sizes and smaller surface area. It is an important hydro-physical characteristic of a soil because it provides simple means of determining useful moisture levels to maintain soil for good plant growth. Detail procedure for determination of water holding capacity of soil using Keen box method (Piper, 1966) is described here.

Hydraulic conductivity is an important soil property that determines the ability of a soil to transmit water. A method of measuring hydraulic conductivity of saturated soil using undisturbed soil core is described in this exercise.

Principle

Flow of water across a soil layer or a sample of known dimension can be best described by Darcy’s law expressed as:

Infiltration rate refers to the volume of water entering into the soil per unit area in the unit time, when the soil is subjected to a shallow depth of ponding at the surface. Under continued ponding, the high infiltration rate in the beginning decreases to a constant value, known as final infiltration rate. The reduction in infiltration rate is due to combined effects of reduced hydraulic gradients, dispersion of aggregates, blocking of pores, cracks and channels, and swelling of colloids. Numerous techniques for measurement of infiltration rate have been developed to meet specific needs but use of double-ring infiltrometer has given consistent result in a variety of conditions.

Principle

In double-ring infiltrometer method, a small representative area in the field is bounded by two concentric metal rings (Figure 17.1) which permit impounding of water over the soil within the rings. The rate fall of surface of water ponded on the soil in the inner ring is taken as the rate of water intake or infiltration rate. In the cropped field, infiltration rate can be measured only within the rows and for which the diameter of the cylinders may be changed to meet the specific needs. The measurements under wetland conditions should be made from a distance of about 40-50 cm to avoid pressure effects.

Soil aeration signifies the ability of a soil to supply oxygen to actively growing roots and soil organisms. Because of constant biological activity causing oxygen consumption and carbon dioxide evolution, the oxygen present in a soil pore or oxygen renewal by mass flow becomes inadequate to meet the plant demand. A continuous diffusion of oxygen from atmosphere to soil and to plant roots through the wetted pores plays an important role. The oxygen reaching plant roots has to diffuse not only through the gaseous phase in the soil pore and wetted pore neck but also through a water film that is present on the hydrated root surface.Lemon and Erickson (1952) suggested a platinum microelectrode method that simulates the field situation. In this method measurement of the rate of oxygen diffusing through the liquid phase to a reducing surface similar to the plant root.

The ratio of soil air-to-soil water permeability indicates the magnitude of breakdown of structure as a result of wetting. A value of one indicates no change in structure and a value greater than one reflects a deterioration of soil structure.

Principle

The permeability of soil to air can be obtained essentially in the same way as the permeability of soil to water. The measurement of permeability of soil to air involves the preparation and placement of the soil in a suitable container with provision for monitoring pressure differential across the sample and the rate of flow of air through it. For steady state case, this involves measurement of inlet and outlet pressures and the volume flux of air. The suitable form of the Darcy’s equation for calculating permeability of soil to air is:

Soil temperature plays an important role in plant growth. Solar radiation received on the earth surface raises the temperature of the soil surface from morning to solar noon followed by a decline until the next morning. Soil temperature gradients established on the surface lead to flow of heat downwards causing a rise in temperature of lower layers. These variations in temperature affect germination, emergence and further growth of plants. Several types of thermometers such as mercury or liquid in glass, bimetallic, bourdon gauge, thermocouples and electrical resistance have been used in soil research but the use of thermocouples and thermistors has been found most useful. A brief description of different types of thermometers used in measurement of soil temperature is given below:

Liquids in Glass Thermometer

A liquid-in-glass thermometer (Figure 20.1) consists of a capillary glass tube with a bulb at one end filled with a thermometric liquid (mercury), vacuumed and sealed. By reading the position of the liquid level on a scale the temperature value can be obtained.

Garden Rakes

1.For use with moving debris.

2.Aids in the cleaning process.

3.Proper use is with two hands.

Classifications and Nomenclature

Plants can be classified according to the following criteria: (1) botanical, (2) descriptive and (3) agricultural. Botanical classification is based on the morphological characteristics of plants as well as on their anatomy, physiology and DNA sequences. Descriptive classification is based on the environmental adaptation, growth habit and other observable features. In agriculture, plants can be broadly classified as either useful or useless. Those which are useful are called crops while those which are not useful are called weeds.

A common, well-defined system of crop classification is important in crop science and agriculture. Grouping of plants following established systems will simplify plant collection initiatives, research and breeding specialized development efforts. Having standardized botanical names will also facilitate efficient communication, dissemination and retrieval of scientific information. Additionally, the grouping of crops will indicate that these crops may have similar uses, adaptation, growth habits and methods of culture. Classifying crops by family is likewise important in intergeneric grafting. To students of crop science, a list of crops under the various agricultural classifications will be a useful reference in crop names and identification and as a review guide. To the farm manager himself, the same list will offer innumerable benefits in finding alternative crops for specific uses.

Despite these advantages, however, classifications of crops in agriculture vary from place to place and from one country to another due to differences in the primary usage of certain crops, intensity of care, extent of cultivation and other criteria under consideration. It is not uncommon to find crops having two or more classifications, used simultaneously, alternatively or sequentially. FAO(2010) in its report entitled “Crops Statistics - Concepts, Definitions and Classifications”, expressed its difficulty in the collection and presentation of statistical data due to differences in the concepts, definitions, coverage and classifications” of crops which are grown in different countries.

Climate and Ornamental Crops

There are many factors which affect the production of the ornamental crops. It may be internal or external factors. Climate and soils are the chief natural factors on which the success or failure of ornamental crops production is dependent. A knowledge of the effect of various soil and climatic conditions on ornamental crops production is very essential for every prospective to grower, climate includes several factors like temperature, relative humidity, rainfall, wind, hail, light whereas, soil covers such factors as moisture supply texture, pH etc.

Temperature: The rate of development of a plant (the leaf unfolding rate) is a function of temperature. As temperatures increase, the leaf unfolding rate increases. If the temperature exceeds the maximum temperature, plants experience stress and leaf unfolding rate declines. Given the high costs of fuel, many growers are tempted to reduce the greenhouse temperature to save money on fuel. Because the rate of crop development is dependent on temperature, reducing temperatures increases production time of all crops. However, some crops will be more severely delayed than others. Reducing temperatures from 20°C to 16°C increased time to flower of Salvia farinacea by 13 days. This same temperature drop only increased days to flower of pansy by four days. For crops that are severely delayed by lower temperatures, reducing the growing temperature may actually increase total fuel costs for producing that crop. Every plant has fairly well defined range of temperature to which it is tolerant and below which the plants of that variety are liable to be injured to a more or less extent.

Annual flowers are a group of herbaceous plants which grow from seeds, produce flowers, set seeds and complete their life-cycle within one year or one season. They provide a beautiful display of colours in the garden. Whether it is a small home garden or a big public garden, it is incomplete without beds of annual flowers. They enhance the decorative value of a garden within a short span of time. At their blooming time, one feels elated when a rain of beauty drizzles in the garden.

Classification of Annuals

Based on season, the annual flowers are divided into 3 groups—winter season, summer season and rainy season.

Winter season annuals

This group includes a large number of seasonal flowers. The important ones are acroclinium, agrostemma, amaranthus, anchusa, annual carnation, annual chrysanthemum, antirrhinum, arctotis, bells-of-Ireland, brachycome, calceolaria, calendula, campanula, candytuft, celosia, China aster, cineraria, clarkia, cornflower, daisy, delphinium, dianthus, dimorphotheca, echium, eschscholzia, gazania, godetia, helichrysum, helipterum, hollyhock, gypsophila, limonium, linaria, linum, lupin, marigold, matricaria, mesembryanthemum, mignonette, mimulus, myosotis, nasturtium, nemesia, nicotiana, nemophila, nigella, pansy, petunia, phlox, pimpinella, poppy, rudbeckia, salvia, saponaria, scabiosa, schizanthus, senecio, stock, sweet alyssum, sweet pea, sweet sultan, sweet william, venidium, verbena, viola and wall flower.

Climbers can be used to break the bareness of long walls, soften posts, frame doors and windows and beautify window boxes. With a fence or trellis, they can be used for screens, making a background for flower garden or quickly turning an ordinary porch or patio into a private, shady living centre. Some slower growing climbers are useful as container plants for patios. On the down side, most vine species have the potential to become invasive, spreading to areas where they may not be desirable or becoming too large for the intended area.

Types

Climbers are characterized as three types based on how they cling to a support. The kind of support needed depends on whether the Climbers cling by twining stems, orby specialized structures such as tendrils, adhesive disks and aerial roots. Climbers with twining stems wind around a support as they grow (Fig. 5.2). Examples of plant with twining stem sinclude bitter sweet, wisteria and trumpet creeper.Tendrils resemble thread like stems that wrap around any structure they contact (Fig. 5.1). Examples of plants with tendrils include grape, pea, and cucumbers. Some Climbers have specialized structures for clinging to a support with a disk-like suction cup (Fig. 5.3). Examples include Boston ivy and Virginia creeper. Other Climbers cling by means of small, aerial roots that attach to rough surfaces such as mortar, wood, and tree trunks (Fig. 5.4). Examples include English ivy, trumpet creeper and winter creeper euonymus.

Shrubs are usually woody or semi-woody perennial plants; their branches arise from the base of the plants and grow up to the height of 3 to 4 meter. Many shrubs, which are more or less prostrate, make excellence ground flower. Availability of a large number of species & cultivars, a wide range of variation in shape & size of the plants, diversified types of leaves, growth habit, remarkable ranges of size, shape, colour of flowers & the variety of fragrance have made the shrubs as important garden plants. Several species of shrubs have unusually attractive colours.

Most of the shrubs are hardy & grow in all types of soil with little attention. Although some plants flower almost throughout the year, in most cases they are seasonal. A selection can be made in such a way that some plants in the shrubbery will be in bloom in any season of the year. Most of these plants are fairly quick growing and when vegetatively propagated, start flowering in the first year. Plants like Bauhinia acuminata (Kanchnar), Cassia alata (Candle Bush), Vinca rosea,which are usually grown from seeds also produced flowers in the first year.Shrubs can be classified into three groups according to the requirement of the sunlight for growth & flowering: