eChapters

For maintenance of garden, a person should well acquainted with different types of tools and implements and their uses. A tool and implement is any physical item that can be used to achieve a goal, describe a procedure or process with a specific purpose. Tools that are used in particular fields or activities may have different designations such as instrument, utensil, implement, machine or apparatus. The set of tools needed to achieve a goal is called equipment. The knowledge of constructing, obtaining and using tools is technology. Varieties of implements and tools are required for carrying out the various garden operations like digging, breaking soil, clods, removal of weeds, levelling of ground, mowing lawns, planting of bulbs, layering and grafting, manuring etc. Some of the gardening hand tools are spades, garden forks, pitchfork, hoes, ploughs, shovels, pruners and ice choppers. Many hand tools have been replaced by electronic tools but gardening and landscaping hand tools are still preferred, because they are easier to use and cheaper. Garden tools can be made up of wood, metal or plastics. Landscaping and Gardening is one of the most rewarding and stress-relieving hobbies. The most common garden tool set includes: shovels, rakes, spades, diggers, trowels, etc. Gardening tools can be power or hand driven. There are some garden tools commonly used (Table 1).

Introduction

The concept of Computer Aided Design (CAD) for landscape gardening is comparatively new. It can be defined as design and drafting of a landscape garden with the aid of computer. It can also be defined as use of information technology in garden designing. The core of the CAD system is software, which makes use of graphics for product representation. Computer aided designing is an important industrial art, extensively used in industry and landscape architecture designing and for many more purposes. For landscape designs, it represents a practical solution for the imperative needs of helping to visualise the future. Now-a-days, CAD is used to create 2D or 3D computer models. CAD assists the designer

Landscaping is an art of beautifying a piece of land or a landscape with planting material, non-living material in order to create a picturesque effect or to imitate nature. Landscaping makes a place more peaceful, beautiful, appealing or pleasing, where people can rest and enjoy with their family, friends, etc.

Landscape gardening is landscaping of a garden. More or less landscape gardening and painting on a canvas or paper sheet is similar. In both cases colours are used to form a picture, only difference is that painting is non-living 2D image and garden is collection of living plants with non living things in 3D form.

When going for landscaping, one can put anything or collection of different articles of interest in a place. It requires proper planning and for planning landscape, there are principles or facts that should be followed. It is not necessary and also possible to follow all the principles at a time in a landscape plan but there are 8 basic principles one should follow during landscape planning, which are discussed below.

The history of gardening in India is older than civilizations of Mohenjo-daro and Harappa. During the pre-Aryan period (around 2500 B.C.), Indians were cultivating flowers and plants of religious significance. Later importance of gardening was not properly understood in India. But presently, importance of landscaping and gardening is realised by planners and landscaping has now become an integral part of any new establishment or property. Due to urbanization and industrialization, pollution has increased considerably and landscaping is one of the best options to reduce the problem. Landscaping is an art of beautifying a piece of land or garden or a landscape with planting materials and non-living materials in order to create a picturesque effect or to imitate nature in that particular land for human use. The importance of landscape gardening can be divided into three categories.

The status of gardening during ancient time has been presented in Ramayana, written by Valmiki. Ayodhya city was described as having wide streets, large houses, richly decorated temples and gardens. These gardens were planted with fruit trees and flowering plants and had lakes full of lotus (Nelumbo sp.) and different kinds of birds. During exile, Lord Rama and Sita are believed to have observed a number of trees and were fascinated by beautiful flowers; one of them was Sita Ashok (Saraca asoca).

Mahabharata, written by Saint Vyasa also contains description about gardens. During the Mahabharata era, pleasure gardens were planted with flowering plants. The famous tree of this era was Kadamba (Anthocephalus cadamba), which is associated with Lord Krishna.

The systematic gardening in India is as old as civilization of Indus of Harappa, which existed between 2400 B.C. and 1750 B.C. At that time, people were living in well-planned roads cut across one another almost at right angles. There are many evidences which prove that trees and ornamental plants were associated with the Harappan civilization. India has a long history of close association of people with flowering plants. It begins with the arrival of Aryans to India in 1600 B.C. They were literate people and brought with them the four Vedas viz., Rig veda, Atharva veda, Yazur veda and Sam veda and the Puranas. They appreciated the beauty of flowering plants, lakes, mountains, forests, etc. and named their children after flowers like Kamal, Champa, Bela, Chameli, Rukmani, etc. The followers of Hindu religion worship their Gods and Goddesses with flowers early in the morning.

The life of Lord Buddha was associated with a number of trees from birth to his Nirvana. He was born in 563 B.C. under the tree of Ashoka (Saraca asoca) at Lumbini. Further, Buddha attained his enlightenment under a Pipal (Ficus religiosa) tree, spread his new teachings under shady banyan and mango trees and breathed his last in a Sal (Shorea robusta) grove.

When Lord Buddha visited Vaishali, Amrapali presented a park known as Amravana which was dominated by flowering trees. Buddhists were planting trees and flowering plants on a large scale for making surrounding peaceful and place ideal for meditation.

16.1 Entering Biological Data into Computer

After the forms have been designed, the database in ready for entry of biological data. Biological data is entered one record at a time. To enter the biological data, the user issues a command, which calls up and displays the appropriate form with blank fields. The user then keys in the data for each field in the appropriate spaces. In this manner, the user enters the data for the first record, then for the second record, and so on. In most database systems, the records are automatically assigned a number as they are entered.

16.2 Saving Biological Data into Computer

While entering data into the fields, the tab key or enter key is usually used to move to the next field. Pressing enter or tab key in the last field on the form saves the record in the database and moves to a new, blank form for the next record to be entered. In addition to using the tab or enter key to move forward through fields, one can directly go to any field on the form at any time by clicking on it with the mouse.

15.1 Computer Network

It is a group of computers and peripherals which are interconnected to share information and resources. A communication system that supports many users can be called a Network or we can say network is a communication system which interconnects many users who have something in common, either with respect to the type of data being sent or to the geographic areas that the users cover. There are three types of computer networks:

15.1.1 Local area network (LAN)

It is a small network (2 to 100 nodes) usually located within a single building or group of buildings belonging to an organization or an institute.

14.1 Computer

Computer is an electronic device designed to accept and store data, process them and produce meaningful results under the direction of detailed step by instructions.

In 1822, Charles Babbage, a professor of Cambridge University designed a machine called an “Analytical Engine”, which could store data and perform arithmetic operations and printing out results. He is known as “Father of Computer”.

14.1.1 Basic anatomy of computer

The word Anatomy of a computer means inner structure of the computer. The study of anatomy of computer is necessary to acquire the knowledge and functioning of its inner components. Thus, essentially a computer consists of the following three main units

13.1 Introduction

In 1935 Prof. Ronald A. Fisher laid the foundation for the subject in his monumental work entitled “The Designs of Experiments”. Experimental designs concern the arranging of treatments or variables in such a manner that the inferences and conclusions regarding the effects of these treatments can be easily done and their reliability measured. Experiments are made with a view to find the validity of a particular hypothesis and to have an idea about the extent of the reliability that can be placed on a particular conclusion arrived. The selection of the design will have a very great bearing on the accuracy of the ultimate results. By a random selection of experimental units it is possible to remove the ambiguity about the casual interpretation of the observed associations. Random sampling is the most essential ingredient of all experimental designs. Besides, there are many devices for increasing the precision of the inference and the calculations.

12.1 Introduction

The name was coined by George W. Snedecor, in honour of Sir Ronald A. Fisher. The object of the F-test is to find out whether the two independent estimates of population variance differ significantly or whether the two samples may be regarded as drawn from the normal populations having the same variance.

11.1 Introduction

Chi-square test is a non-parametric test. Such non-parametric tests have assumed great importance in statistical analysis and statistical inference because they are easy to compute and can be used without making assumptions about parameters as they are distribution-free tests.

10.1 Introduction

The assumptions on which analysis of large samples is done generally do not hold well in case of small samples (n < 30). In case of large samples we had presumed that the random sampling distributions of statistics are approximately normal and further that the values obtained by sampling study are close to the population values and can be used in their place for the calculation of the standard error of the estimate. These assumptions do not hold well if the size of the samples may or may not be normally distributed and similarly it is not possible to substitute the mean or the standard deviation of a small sample in place of the parameter mean or standard deviation for the calculation of standard errors. Under such circumstances the analysis of small samples has to be done by techniques which are different from those applicable in case of large samples.

9.1 Introduction

The tests used to ascertain whether the differences between estimator and the parameter or between two estimators are real or due to chance are called tests of hypothesis or tests of significance. In other words, the procedures, which enable us to decide whether to accept or reject hypothesis, are called tests of hypothesis or tests of significance. The following terms are commonly used in testing of hypothesis

8.1 Introduction

The normal distribution was discovered by De Moivre in the year 1733. Normal distribution is probably the most important of all theoretical distributions for the reason that so many physical measurements and natural phenomena have observed frequency distributions which very closely resemble the normal distribution.

7.1 Introduction

The theory of probability has its origin in the games of chance related to gambling such as throwing a die, tossing a coin, drawing cards from a pack of cards etc. Jerame Cardon (1501-1576), an Italian mathematician was the first man to write a book on the subject entitled “Book on Games and Chance”, which was published after his death in 1663. The systematic and scientific foundation of the mathematical theory of probability was laid in mid-seventeenth century by two French mathematicians B. Pascal (1623-1662) and Pierre de Fermat (1601-1665).

6.1 Correlation

The term correlation indicates the relationship between two such variables in which with changes in the values of one variable, the values of the other variable also change. In short, the tendency of simultaneous variation between two variables is called correlation.

5.1 Graphical Representation of Data

Diagrams are generally useful for the purpose of publicity and propaganda. No statistical conclusion can be drowning as these provide only an approximate and rough expression about the phenomenon. Thus for a more rigorous and improved representation of numerical statements graphs and charts are used.

Graphs are generally drawn with the help of two perpendicular lines known as x-axis and y-axis. The following are the various types of graphs, which are in common use:

4.1 Diagrammatic Representation of Data

Diagrams are meant only to give a pictorial representation of the data with a view to make them readily intelligible. Several types of diagrams are used for the presentation of data in biological and agriculture sciences. The following are the important types of diagrams in common use :

1. One-dimensional diagrams
2. Two-dimensional diagrams
3. Three-dimensional diagrams

The measure of scatteredness of observation around their average is necessary to get a better description of data. The extent or degree to which data tend to spread around an average is called Dispersion or Variation. Measure of Dispersion also called the averages of second order; help us to measure the scatteredness of observation around an average. The commonly used measures of dispersion are as follows;

One of the most important aspects of describing distribution is the central value around which the observations are distributed. A statistical measure used for representing the centre of central value of a set of observations is known as “Measure of Central Tendency”. This central value is also called as ‘Average’. The commonly used measures of central tendency are as follows;

1.1 Classification

The raw or ungrouped data are in unorganized form and need be organized in meaningful and readily comprehensible form in order to facilitate further statistical analysis. Classification of data is the first step in this direction. It is the process of arranging thing in groups according to their resemblance and affinities. There are two basic types of classification namely-qualitative and quantitative. In qualitative classification, the basis of classification is some attribute or quality like: sex, literacy, religion, etc. A qualitative classification may be simple or manifold. The classification done with respect to one attribute is termed as ‘Simple Classification’. The classification in which two or more attributes are considered and several classes are formed is called ‘Manifold Classification’. In quantitative classification, the collected data are grouped in respect of characteristics, which can be measured and numerically expressed such as height, weight, etc.

At country level, there is major paradiam shift from cropping systems towards farming system research in XI Plan period. The mandate of Project Directorate for Cropping Systems Research, Modipuram stands changed with focus on Farming Systems and re-named as Project Directorate for Farming Systems Research. w.e.f. 1.4.2009. The All India Co-ordinated Research Project on Cropping Systems with its 69 on-station and on-farm centres representing different agro-climatic zones of the country also stands re-named as AICRP on Integrated Farming Systems. As such, the much needed methodologies and procedures for planning, monitoring and interpretation of farming systems related data are not readily available. Therefore, some methodologies and procedures which can be adopted to execute Farming Systems Research for drawing meaningful conclusions are discussed here.

A. Agronomic Considerations for Good Experimentation

For laying out of any experiment (Short / long term) at on-station and on farmers field, some of the agronomic considerations are very important for better results using experimental designs. Therefore, these are discussed here in this chapter.

1. Cropping History

The crop performance and its yield is affected by the crop grown and management followed during previous years. Therefore, for meaningful interpretation and factual reflection on yield trend due to the treatment proposed to be evaluated, the details of crop grown during previous 2 years/ seasons with details of management followed especially input applied needs to be recorded.

If x and y are related, we can study degree of relationship between them which is called correlation.

Y = f(x)=a+bx where y is a dependent variable and x is an independent variable. If x is known, y can be known.

X = f(y). Here x is a dependent variable and y is an independent variable or auxiliary variable.

A sample is just a part of a population selected according to some rule or plan. Following are some of the methods of sampling.

1. Simple Random Sampling

The simplest and the most commonly used type of probability sampling is simple random sampling. In this kind of sampling, each member of the population has the same probability of being included in the sample. Simple random sampling may be with or without replacement.

One of the objectives of experiments in cultivators’ field is to test technology for adoption in a targeted area i.e. NARP Zone, or a district. In that case interaction between district x treatment within a zone or between blocks x treatment within a district should not be significant. If the targeted area is a district, an experiment in cultivators’ fields must occur in at least two blocks. Also, if the targeted area is a zone, an experiment should occur in at least two districts in order to estimate interaction.

Analysis of cultivators’ field experiment

The on-farm trials were conducted in 25 villages on kharif rice during 1992-93. The treatments viz. T1-Farmers’ practice with local variety, T2-Farmers’ practice with improved variety, T3-Farmers’ practice with improved variety +recommended fertilizer and T4-Improved practice with improved variety +recommended fertilizer. Farmers level of NPK fertilizer was 40:0:0 and local variety was Suzata while recommended NPK fertilizer was 80:40:20.

Where Y is the average yield of a crop in a sequence over a period of years “n” and S is the standard deviation of yield over a period of years, Y max is the maximum yield of a crop in a certain year.

Example 1. Six crop sequences were evaluated in simple randomized block design replicated four times at Durgapura. The sequences tried are pearl millet-wheat, cluster bean-wheat, pearl milletmustard, cluster beanbarley, cowpea-mustard and pearl millet-Chickpea under irrigated conditions.

In many experimental situations, it becomes necessary to repeat an experiment over time, (for a number of seasons or years). This repetition (or replication) of the experiment broadens the scope of the experiment in the sense that our recommendations will be applicable for a number of seasons. In the case of agricultural experiments, for example, there may be present treatment x season interaction. So the results of a series of experiment, performed for different seasons with the same set of treatments, will have wider applicability.

We shall consider the simplest case of a repetition of experiments of identical structure for a number of seasons. Let us consider randomized block experiments with t treatments in r blocks and conducted in p seasons. The analysis of the experiment in a season is based on the linear model. Before attempting a combined analysis for the p experiments, it is necessary to perform the analysis for p experiments separately and interpret the results separately. It may be of interest to find out whether differences among the treatments are the same in the different seasons so that ‘ best ‘ treatment may be recommended for all seasons or whether different treatments are to be recommended for different seasons.

We next consider the analysis of the combined experiments considering the seasons as a random sample from the population of seasons. Analysis of Variance of series of experiments

Split-plot Design

In field experiments, sometimes a factor has to be applied to a large experimental unit. This is true when the different methods of ploughing or irrigation are to be compared. And in such cases it is possible to introduce a second factor, which does not require large plots, with a small number of levels into the same experiment, at a little extra cost. This is done by splitting the plots (called whole plots of the first factor into as many sub-plots as there are levels of the second factor.)

A split-plot design with an RBD for the first set of treatments (called “the whole-plot treatments”) is obtained by allotting the whole-plot treatments at random to the whole plots of a block and then randomizing the second set of treatments (called “the sub-plot treatments”) to the sub-plots within each whole plot.

This enables us to test for the main effects of the sub-plot treatments and the interaction of the whole-plot treatments and the sub-plot treatments more efficiently than the main effects of the whole-plot treatments in a split-plot design.

The difficulty in conducting a factorial experiment in an RBD is that, as the number of factors and/ or that of levels of the factors increase, the number of treatment combinations to be compared increases too. This in turn necessitates use of large sized blocks to accommodate all the treatment combinations i.e in a 2^4 – experiment there should be 16 plots in a block. But it has been found that the experimental error increases with an increase in the size of a block, for then it becomes less effective in controlling the heterogeneity of the units. A remedy has been found out : this is to divide a replicate (a complete block) into a number of equal blocks (incomplete blocks) and then to allocate the treatment combinations to these blocks so that only the unimportant treatment comparisons gets mixed up or entangled with the block comparisons. These treatment comparisons are then said to be confounded or mixed up with block effects. These effects cannot be separately tested or estimated. But the remaining treatment effects, which are not confounded with the block effects, are still capable of separate estimation and testing.

A treatment is the combination of N & P

If levels of N & P are 5 & 10 respectively, so number of treatments are 5x10=50.

Completely Randomized Design (CRD)

The simplest design using the two essential principles of replication and randomization is the CRD. Suppose that we have t treatments (or t levels of a factor) under comparison and ith treatment is to be replicated r times, for i = 1,2, … t . A particular case of this is equal replication for different treatments, where r1=r2= =r6=r, so that n=rt.

Experimental unit : An experimental unit is the material to which is applied the treatment and on which the variable under study is measured. In an agricultural field experiment, the plot of land, not the individual plant, will be the experimental unit; in a feeding experiment of cows,the whole cow is the experimental unit; in human experiments in which the treatment affects the individual, the individual will be the experimental unit.

Treatment : The different procedures under comparison in an experiment are the different treatments, i.e in an agricultural experiment, the different varieties of a crop or the different manures will be the treatments. In a dietary or medical experiment, the different diets or medicines, etc. are the treatments.

The experimental design and analysis discussed in previous chapters refer essentially to experiments on annual crops. Even when these experiments are repeated in different years, this is done at a new site each year .The use of new crop production practices will generally change the important physical and biological factors of the environment. This process of change can take many years. Generally, the productivity of some types of technology must be evaluated by a series of trials repeated over time. Such trials are generally referred to as long-term experiments. Long-term experiments are those which are continued on the same set of plots over a series of years with a pre-planned sequence of treatments or crops or both. Treatments may be applied every year or periodically. The same crop may be repeated year after year or once planted may continue for several years as perennial trees ,or a sequence of crops may be grown in rotation as for instance rice-rice or rice-wheat (1 year sequence with 2 crops per year ) or sorghum,groundnut,cotton and wheat, each crop cycle being completed in four years. The main features of long-term experiments are as follows :

This is an extension of the analysis of variance to cover the case where observations are taken on more than one variable from each experimental unit. Interest, however, centres on one of these (y, called the dependent variable) and the question is whether the variation of the dependent variable over the classes is due to class effects or due to its dependence on the other variables, (x’s, called the independent or concomitant variables ), which also vary from class to class. The analysis of covariance controls the experimental error by taking ito consideration the dependence of y on x.

In many experimental situations, it becomes necessary to repeat an experiment over time ( for a number of seasons or years ) and / or over space ( a number of places). This repetition ( or replication ) of the experiment broadens the scope of the experiment in the sense that our recommendations will be applicable for a number of seasons /or a number of places . A single experiment performed in one place for only one season will provide recommendations for that place and for that season . It may not be applicable to other places or for other seasons . In the case of agricultural experiments , for example , there may be present treatment x place interaction and/ or treatment x season interaction . So the results of a series of experiments , performed over different places for different seasons with the same set of treatments,will have wider applicability .

We shall consider the simplest case of repetition of experiments of identical structure over a number of places ( it is the same for a number of seasons ). Let us consider randomized block experiments with t treatments in r blocks and conducted in p places. The analysis of the experiment in a place is based on the linear model . Before attempting a combined analysis for the p experiments ,it is necessary to perform the analysis for the p places separately and interpret the results separately .It may be of interest to find out whether differences among the treatments are the same in the different places so that a ‘ best ‘ treatment may be recommended for all the places, or whether different treatments are to be recommended for different places . The model for this combined analysis may be written as

Square-Root Transformation for Counts

stabilizes the variance more effectively.

Counts of rare events, such as number of defects or of accidents tend to be distributed approximately in poisson fashion.

Complete block designs become less efficient as the number of treatments increases,primarily because block size increases proportionally with the number of treatments,and the homogeneity of experimental plots within a large block is difficult to maintain.That is , the experimental error of a complete block design is generally expected to increase with the number of treatments.

An alternative set of designs for single –factor experiments having a large number of treatments is the incomplete block design.As the name implies, each block in an incomplete block design does not contain all treatments and a reasonably small block size can be maintained even if the number of treatments is large.With smaller blocks,the homogeneity of experimental units in the same block is easier to maintain and a higher degree of precision can generally be expected.

Introduction

As a chief feature of RCBD, formation of blocks is a necessity to achieve homogeneity of plots within each block so that an irrevelant source of variation existing to the error component could be removed for purposes of treatment comparisons. But quite frequently, in agricultural experiments, the fertility gradient varies in all directions so that the plots within a block in RCBD could still be heterogeneous. In order to control or remove this additional source of variation, one can think of extending the concept of blocking yet in another direction , orthogonal to the first direction, leading to a new design called Latin Square Design(LSD). In otherwords, if the blocking concept is one-dimensional in RCBD, it is two-dimensional in LSD. As a guiding principle for formation of two-dimensional blocks, it may be stated that each row-block and each column–block should remove as much variability as is possible from total variability so that error component becomes smaller. The chief restrictive features of LSD are:

The split-split plot design is an extension of the split-plot design to accommodate a third factor .It is uniquely suited for a three –factor experiment where three different levels of precision are desired for the various effects. Each level of precision is assigned to the effects associated with each of the three factors. This design is characterized by two important features :

1. There are three plot sizes corresponding to the three factors, namely, the largest plot (main plot) for the main-plot factor, the intermediate plot (sub-plot) for the subplot factor, and the smallest plot (sub-sub plot ) for the sub-subplot factor.
2. There are three levels of precision, with the main-plot factor receiving the lowest degree of precision and the sub-subplot factor receiving the highest degree of precision.

The method of analysis is entirely analogous to that for the case of 2 factors. The analysis consists of 3 parts, between whole plots, between split plots within whole plots,between split-split plots within split plots, each part being put on the basis of a split-split plot in order that the parts may be combined into one complete analysis.

Introduction

The split-plot design is specifically suited for a two-factor experiment that has more treatments than can be accommodated by a RCBD. In a split-plot design,one of the factors is assigned to the main plot. The assigned factor is called the main-plot factor. The main plot is divied into subplots to which the second factor ,called the subplot factor, is assigned. Thus, each mainplot becomes a block for the subplot treatments (i.e the levels of the subplot factor). The split-plot design is profitably employed under the following situations.

1. An experimenter may have already possessed some knowledge about the main effect of a certain factor so that his interest lies in estimating the interaction effect of this factor with some other more important factor in whose main effect also he is equally interested. In either case he wants to estimate the latter two effects with greater precision for which he is prepared to sacrifice the precision in estimating the first mainfactor.
2. The second common type of situation when the split-plot design is automatically suggestive is the difficulties in the execution of other design. For instance, if ploughing is one of the factors of interest,then one cannot have different depths of ploughing in different plots scattered randomly apart. Similarly, if different levels of irrigation are chosen,then random distribution of these levels in an ordinary experiment may involve difficulties in the practical execution of such plans. This difficulty may,however, be offset partly by having strips of plots (in one unit of block,say), all treated alike ;receiving the same level of irrigation with different depths of ploughing; or different levels of irrigation with same depth of ploughing. Different levels of irrigation or different depths of ploughing can,however,be randomly distributed over such strips.
3. The third situation in which the experimenter may adopt a split-plot design is this. A particular factor (factors) under investigation exhibits large treatment differences which can be easily detected even in bigger plots,while a second factor (factors) requires comparatively smaller plots to detect smaller treatment differences with higher precision. Then the first factor will be tested in whole plots and the second factor whose treatment differences are small will be tested in small plots or subplots.

Introduction

In many of the experimental designs considered previously like CRD and RCBD ,the main feature was to compare varieties,methods of cultivation,different kinds of fertilizers, etc. In general, in such experiments, only one factor is allowed to vary at a time, while all other factors known to affect the response are generally controlled. Such experiments are called ‘Simple’, or ‘Classical’ experiments.

Biological organisms are simultaneously exposed to many growth factors during their lifetime. Because an organism’s response to any simple factor may vary with the level of the other factors, simple factor experiments are often criticized for their narrowness. Indeed, the result of a single-factor experiment is, strictly speaking, applicable only to the particular level in which the other factors were maintained in the trial. But there are numerous occasions, especially in agriculture and animal experiments, where the main interest of an investigator does not lie with the effect of a single factor at a time but lies with simultaneous influence of many factors. For example, the interest of an investigator may not be in merely knowing the effect of spacing on the yield of a certain crop, but his interest would lie in finding out the simultaneous effect of both spacing and method of fertilizer application, that is, whether along with spacing, split dose of fertilizer at periodical intervals would be more beneficial than applying full recommended dose at one time. In order to answer this and many other questions, there are some special experiments called factorial experiments.

Introduction

In any experiment, for instance, a varietal trial or a treatment trial, the main purpose of the experimental design would be to detect the differences among the varieties or treatments. This assumes, as an obvious prerequisite that the experimental units, such as plots or animals, be as alike as possible so that differences observed in responses, such as yield in varietal trial, could be attributable to the varietal or treatment differences. But more often than not, this condition is not realized to a satisfactory degree in practice due to heterogeneity present in experimental units. In a field experiment, for instance, a big piece of land might be quite heterogeneous regarding soil fertility, moisture content, salinity, etc; so that experimental plots marked on this land would differ widely among themselves, though plots nearer to each other would be more alike or homogeneous than the plots scattered distant apart; also in the case of animal experiments, animals forming the experimental units might be heterogeneous regarding their ages , weights, genetic composition, etc. With such experimental units in the experiment, the variability produced in the observations will be comparatively high so that only large treatment differences are detectable, since small differences are obsecured by large error variability.

The randomized complete block design (RCBD), is perhaps the most widely used design of all experimental designs, mainly for its simplicity, flexibility and validity. The main objective of the design is either to control, remove or reduce the variability due to heterogeneity in experimental material, which is quite irrelevant for treatment comparisons. In CRD, the error variability as estimated from the residual sum of squares will be considerably high. The RCBD is a device introduced in this design for separating out the variability due to heterogeneity among experimental units from the variability due to all extraneous sources, thus purifying in one sense. The design is especially suitable for field experiments where the number of treatments is not large and the experimental area has a predictable productivity gradient.The primary distinguishing feature of the RCBD is the presence of blocks of equal size, each of which contains all the treatments.

Normal distribution :- A random variable x is said to be normally distributed with population mean u and standard deviation σ if it assumes the following distribution

Leafy vegetables are rich sources of provitamin-A, vitamin C and minerals like calcium, magnesium, iron, etc., They are the riches sources of roughes or fibres essential in human diet. The ICMR recommended dietary allowances of green leafy vegetables for adult women is 100g/day and men are 40g/day while for school children above 10 years it is 50g/day. Greens are rich in vitamins and minerals essential for healthy functioning of organs of body. Amaranth, beet leaf, spinach, fenugreek, etc., are the major leaf vegetables grown in India. In addition, a number of under-utilized annual crops are also grown as leaf vegetables in specific regions. Tender stems and leaves of a number of perennial crops are rich sources of vitamins and minerals and are used for cooking.

10.1. Amaranth (Amaranthus sp)

Amaranth is the most common leaf vegetable grown in India. Amarnath is an annual herb with erect growth and scarce to profusely branching habit. Stem is succulent and green or purple or mixed shades of these two. There are two sections in Amaranthus, viz., Amaranthus and Blitopsis. Majority of leaf cultivars grown in India belong to Amaranthus tricolor. Among the several kinds of greens Amaranthus occupy a special place because it has a large number of species grown in one or the other part of country.

Some of the important improved varieties released are CO-2 and CO-5 from TNAU, Coimbatore, Pusa Kiran, Pusa Kirti and Pusa Lal Choulia from IARI, New Delhi, Arka Arunima and Arka Suguna from IIHR, Bangalore. Amaranth are popular all over India by different names. While in northern parts it is popularly known as choulia, in south it is popularly called as keerai or cheera or koora or soppa. The general composition of amaranth is given below:

9.1. Onion (Allium cepa L.)

The onion (Allium cepa), which is also known as the bulb onion, or common onion is the most widely cultivated species of the genus Allium. It has been used as food since time immemorial and is an important vegetable all over the world. The genus Allium also contains a number of other species variously referred to as onions and cultivated for food, such as the Japanese bunching onion (A. fistulosum), Egyptian onion (A. proliferum), and Canada onion (A. canadense). The vast majority of cultivars of A. cepa belong to the “common onion group” (A. cepa var. cepa) and are usually referred to simply as “onions”. The Aggregatum Group of cultivars (A. cepa var. aggregatum) includes both shallots and potato onions. Onion is suggested to have been originated in Asia, particularly Pakistan and Iran.

China is the largest onion producer in the world followed by India. Largest area under onion is in India followed by China. The major onion producing state are Maharashtra, Karnataka, Madhya Pradesh, Rajasthan, Gujarat, Bihar, Andhra Pradesh, Tamil Nadu and Orissa.

Cultivars/Varieites: Onion varieties differ in size, colour of skin, pungency and maturation. The most popular varieties under cultivation are Agrifound Dark Red, Agrifound Light Red, Agrifound Red, Agrifound Rose, Arka Bindu, Arka Kalyan, Arka Niketan, Arka Pragati, Baswant 780, Brown Spanish, Co-1, Co-2 Co- 3, Co-4, Early Grano, Hisar II, Kalyanpur Red Round, MDU 1 N-2-4-1, N-53, N-257-9-1, Punjab 48 (S 48), Pusa Madhavi, Punjab Red Round, Punjab Selection, Pusa Ratnar, Pusa Red, Pusa White Flat, Pusa White Round, Udaipur 101, 102 & 103.

Leguminous vegetables include peas, French bean, Lima bean, broad bean, cowpea, hyacinth bean, winged bean etc. They belong to the family Fabaceae (Syn. Leguminosae). These are cultivated either as sole crop is sometimes as intercrop. In some areas they are used in backyard cultivation also. Most of them are short duration crops and are self pollinated.

8.1. Peas (Pisum sativum L.)

The pea (Pisum sativum L.) is a very common nutritioius vegetable grown in cool season throughout the world. It belongs to the family Fabaceae (Syn. Leguminaceae) and genus Pisum. A pea is most commonly the small spherical seed or the seedpod of the pod fruit Pisum sativum L. The pea cultivars are grouped into several groups based on shape of seed, plant height, maturity and use of pods. This crop is cultivated for its tender and immature pods for use as vegetable and mature dry pods for use as a pulse. In both the cases the seeds are separated and used as vegetable or pulse. The central Asia is regarded as the place of origin for all legumes including pea. Based on the genetic diversity, Central Asia, the Near East, Abyssinia and the Mediterranean is considered to be its centre of Origin. Cultivated garden pea is not seen in wild state and it might have been originated from wild field pea or other related species. In India it widely cultivated states of Uttar Pradesh, Bihar, Haryana, Punjab, Himachal Pradesh, Orissa and Karnataka. Followed by Madhya Pradesh, Himachal Pradesh, Assam and Jharkhand.

Cultivars/Varietie: Peas varieties are grouped based on maturity (early, mid season and late) plant height (Bush or dwarf, medium tall and tall). The major cultivars under cultivation in different parts of the country are Arkel, Bonneville, Harbhajan, FC-1, Arka Ajit, UN 53-6 (whole edible type), Jawahar Matar 1, Jawahar Matar 2, Jawahar Matar 3, Jawahar Matar 4, Jawahar Matar 5, Jawahar Peas 83, JP 4 (JM 6), JP 19, Lincoin, Mattar Ageta 6, Pantnagar Matar 2, Pant Uphar (IP 3), P 88, PRS 4,Ooty-1 and VL 3.

7.1. Okra or Bhindi (Abelmoschus esculentus L.)

Okra (Bhindi) is a member of Malvaceae family, earlier known as Hibiscus esculentus, and is an annual vegetable crop grown from seeds in tropical and subtropical parts of the world. Okra, also known as “lady finger” or “gumbo”, is a highly nutritious green edible pod vegetable. Botanically, this perennial flowering plant belongs to the mallow family but is grown as an annual. It is named scientifically as Abelmoschus esculentus. Okra originated in tropical and subtropical Africa and India is suggested to be the secondary centre of origin. There are four species under Abelmoschus genus which includes both cultivated and wild forms. They are A. esculentus, A. manihot, A. caillei and A. moschatus. In India, Okra is widely cultivated in states of Andhra Pradesh, West Bengal, Orissa, Bihar Uttar Pradesh, Karnataka, Assam West Bengal, Orissa, Gujarat and Bihar.

Cultivars/Varieties: Okra varieties differ in growth habit, height of plant, presence of purple pigmentation and ridges on fruit. The popular varieties under cultivation in different parts of the country are Arka Abhay, Arka Anamika, Azad Kranti, Co 1, MDU1, Gujarat Bhindi 1, Harbhajan Bhindi, Hisar Unnat, Parbhani Kranti, Perkins Long Green, Punjab 7, Punjab 8, Punjab Padmini, Pusa A 4, Pusa Makhmali, Pusa Sawani, Red Bhindi, TN Hybrid 8 and Varsha Uphar. Besides, a number of hybride from many private sector agencies/seed companies are marketed. Of these, Varsha, Vijay, Adhunik, Panchali, Hybrid No. 6,7 and 8, Nath Sobha, Supriya, Sungro 35, Aroh 1, - 2, -3, -9 and are more popular.

Uses

Different parts of the plant are used for different purposes. Abelmoschus moschatus Medik. Leaves and seeds are considered as valuable traditional medicine. Abelmoschus moschatus is also called ‘musk okra’ a medicinal plant belonging the same family. The leaves and new shoots of this plant is also used as vegetable. The aromatic seeds of this plant are aphrodisiac, ophthalmic, cardio tonic, antispasmodic and used in the treatment of and intestinal complaints and anxiety.

6.1. Potato (Solanum tuberosum L.)

Potato, Solanum tuberosum, is a starchy, tuberous crop belonging to the Solanaceae family. The name potato originally referred to a type of sweet potato, batata, in Spanish. Potatoes are occasionally referred to as “Irish potatoes” or “white potatoes” in the United States, to distinguish them from sweet potatoes. The potato was believed to be first domesticated in the region of southern Peru and extreme northwestern Bolivia between 8000 to 5000 BC. The potato diffused widely after 1600, becoming a major food resource in Europe and East Asia. Greater geographic mobility of farmers led to the rapid spread of potato cultivation throughout China, and India. China is now the world’s largest potato producer and nearly one third of the world’s potatoes are produced in China and India.

Today potato is the fourth major food crop after rice, wheat and maize in the world. In India potato is a very important crop and are mainly produced in the states of Uttar

Cultivars/Varieties: Most of the improved potato varieties were developed by CPRI, Kufri, Shimla. Most of the earlier varieties released as clonal selections derived from foreign varieties have been replaced with high yielding new varieties from the same institute. The popular varieties under cultivation now are Kufri Sindhuri, Kufri Chandramukhi, Kufri Jyothi, Kufri Lauvkar, Kufri Muthu, Kufri Dewa, Kufri Badshah, Kufri Bahar, Kufri Lalima, Kufri Swarna, Kufri Jawahar, Kufri Ashoka, Kufri Megha, Kufri Sutlej, Kufri Pukhraj, Kufri Chipsona-1, Kufri Chipsona-2 and Kufri Giriraj.

Uses

Potatoes are used in different ways like boiled, fried, baked and roasted. Potatoes are used to brew alcoholic beverages such as vodka, potcheen, or akvavit.They are also used as food for domestic animals. Potato starch is used in the food industry as, for example, thickeners and binders of soups and sauces, in the textile industry, as adhesives, and for the manufacturing of papers and boards. Many companies are exploring the possibilities of using waste potatoes to obtain polylactic acid for use in bioplastic products; other research projects seek ways to use the starch as a base for biodegradable packaging. Potato skins, along with honey, are used in folk medicine for burns in India. Burn centers in India have experimented with the use of the thin outer skin layer to protect burns while healing. The potato is best known for its carbohydrate content, which is in the form of starch. Because of its high starch content, it is a staple food in several parts of world including in some parts of India. A small but significant portion of this starch is resistant to digestion by enzymes in the stomach and small intestine, and so reaches the large intestine essentially intact, thereby, increasing its physiological significance. Besides these the potato also provides various other nutritional constituents to the body. Potato is also processed into various products like chips, powder, pappad, savory items.

Carrot, radish, turnip and beet root are major root crops grown in India. In addition, root crops like rutabaga, parsnip, parsley, chervil and celeriac are grown in a limited scale in different parts of the world. There are several other roots and modified underground plant parts that grow in forests and consumed by tribes, but they are not commercially cultivated. Carrot, radish, turnip and beet root are generally consumed as cooked vegetables or fresh salads or as pickled vegetables.

5.1. Carrot

Carrots belong to the Umbelliferae family, named after the umbrella-like f lower clusters that plants in this family produce. As such, carrots are related to parsnips, fennel, parsley, anise, caraway, cumin and dill. Carrot, Daucus carota L. is believed have been primarily originated in Afghanistan, South western Asia and Mediterranean region. Daucus carota ssp. Carota is the most common wild form in Europe and South West Asia, and the present day cultivated carrots have most probably originated from this sub-species.

Haryana, Andhra Pradesh, Punjab and Tamil Nadu, Karnataka, Uttar Pradesh and Assam are the major producers of carrot in India.

Cultivars/Varieties: Varieties with long, orange coloured and smooth roots are preferred in India. Both indigenous and exotic varieties having different root length, shape and colour are grown. Temperate or European type varieties are Nantes, Half long, Early Nantes, Chantenay, Chaman, Pusa Yamadagni and Ooty-1, which require low temperature of 4-8°C for flowering. Tropical or Asiatic type varieties do not require low temperature for flowering and can be grown in plains also. The varieties belonging to this group are Pusa Kesar, Pusa Meghali and Hisar Gairic. Some varieties like Gold King, Indian Kuroda and Super Kuroda are also marketed by private seed companies.