eChapters

Seedlings of several vegetables are first raised in well prepared nursery beds, followed by transplanting in the field. Raising of seedling in the nursery beds is economical as well as easier to take care of young, tender seedlings against pest and diseases. Further, raising of seedlings in nursery allow more time for land preparation.

Site and soil for nursery

In selecting a site for nursery beds, following points should be considered viz sunny situation, good drainage and irrigation facilities. Sandy loam soil with plenty of organic matter are ideal for starting nursery beds. In case of heavy soils it is desirable to add coarse sand to modify the soil condition. Before sowing seed it is essential to sterlize the beds by steam or formaldehyde or any other chemical to do away with problem of damping-off disease. Burning of leaves or straw in the beds is quite helpful in controlling certain soil borne diseases and pests.

Seed

A seed is a mature ovule consisting of an embryo together with stored food, all enclosed by a protective coat. It is product of the ripened ovule of gymnosperm and angiosperm plants which results as a consequence of fertilization and some growth within the mother plant formation of seed completes the cycle of reproduction. The embryo develops from zygote and the seed coat from the integument of ovule. A seed has three main parts these are seed coat, endosperm and embryo. Embryo includes the root, the cotyledon and embryonic leaves. Seed is located inside fruit and a fruit is a mature ovary. In broad sense any plant part which is used for commercial multiplication of crop is called seed. Thus in case of potato stem and tuber are also known as seed. Improved seed results in (1) Better germination (2) Vigorous seedling growth (3) Higher crop stand (4) Better quality of produce (5) Ultimately in higher crop yield.

Development of seeds: Successful pollination and fertilization are the prerequisites to produce a viable seed. During pollination the pollen grains are transferred from anther to the stigma and get stuck there in the sticky fluid secreted by the stigma. The fluid contains sugar which induces germination of pollen grains. The pollen tube grows downward through the style in the ovary until it reaches the embryo sac and discharges two male gametes-one is fused with a female gamete and forms the zygote (2n) and the other is united with two pollar nuclei to produce endosperm (3n). The zygote develops and gives rise to embryo embedded in nutritive tissue developed from endosperm. The nucellus tissues surrounding these structures provide necessary energy for their proper development. Like fruit, the seed also exhibits sigmoid and sometimes double sigmoid pattern of growth. Various changes occur during the course of seed development and the sequences of development may vary from species to species keeping the basic theme unaltered.

The purpose and the mode of marketing of vegetables determine the type of vegetable gardening. The various types of vegetable gardening are as follows.

Home or Kitchen gardening

Market gardening

Truck gardening

Processing gardening

Vegetable forcing

Seed production gardening

Floating vegetable gardening

Vegetable crops: A seed is borne on fruit and fruit is a mature ovary and ovary is a typical part of flower. Flower in angiosperm, consists of four parts these are calyx/sepal, corolla/petals, androecium/stamens and gynocium/pistil, outer whorl of flower is collectively referred to as calyx. The individual components of calyx are called as sepal. Petals are collectively referred to as corolla. Petal is a unit of second inner whorl after sepals. Stamen is part of flower bearing the male reproductive parts composed of anther and filaments. Pistil is part of flower bearing the female reproductive parts composed of stigma style and ovary.

Seed Production Techniques in Vegetable Crops

Share (%) of F₁ hybrids- Cabbage 85%, Watermelon 70%, Okra 23%, Tomato 80%, Chilli 80%, Cauliflower 50%. Among the Asian countries, India is the 2^nd largest production of hand pollinated hybrid seed in vegetables.

The planning of a garden is an art. It is said that a poet is born, and in that sense a garden architect is also born. But that does not mean that a garden architect has nothing to learn. On the contrary he should learn enough of geology, geography, garden history, styles of gardening, and above all should have a profound knowledge about plants. It is imperative to know at the outset what is meant by landscape gardening. A landscape may be defined as any area, either big or small, on which it is possible or desirable to mould view or a design, whereas landscape gardening may be described as the application of garden forms, methods, and materials with a view to improving the landscape. The art of designing is known as “landscape architecture,” although the older term landscape gardening” is also popular.

Before discussing the principles of gardening in detail, it will be worthwhile to make some useful suggestions. The first and the foremost thing are not to imitate another garden which has secured a prize in a competition. One has to develop one’s own design giving due consideration to the local conditions. One more mistake which is commonly made is to plant many more specimens than a garden can accommodate causing overcrowding. In a landscape garden any difference in levels has to be taken advantage of, but in a perfectly flat land it will be costly to create artificial undulations. In each garden the care should be at least one feature or there be a second these two should harmonize with each other. Before planning a design are must be sure for what purpose the garden is utility or beauty or both.

Lawn

Lawn is a natural green carpet, and it is important feature of a landscape and a garden without a lawn is not considerd complete. Lawn is an area of land closed mowed grasses it is primarily developed for aesthetic and recreational purpose. A lawn is an integral part of a garden and landscape besides aesthetic and recreational purpose and several other purposes. Lawn provides a place for taking rest after tiring of the day, holding parties, social functions, passive and active recreation. Distinguishing characteristic of turf grasses is the ability to with stand close mowing and still providing a functional dense healthy ground cover. Lawn is also called as heart of garden. Lawn can be called as turf pitch a field, sod, yard, are green depending upon the plantation usage continent. Lawn is a ground cover of perennial grass, ditch, persists enclose mowing and require proper management practice.

J.B. Olcoth designed first experiment in lawn grass at Monchester in USA in the 19th century. Festuca and Agrostis species were used to make turf (Lawn) during 1880 in U.K. In the early 19th century lawn become wild spread out side of parks golf course. Now a day in many metro cities of India lawn has become a cultural of the passion of many homes.

Garden

Garden is a place where vegetables, fruits and ornamental plants are grown mainly for recreation. The garden incorporates both natural and man-made materials.

Types of garden

The major garden types which shall be discussed are: English garden, Mughal garden, Persian garden, Italian garden, French garden, Japanese garden etc. Out of these the Mughal, Persian, Italian and French styles fall in the category of formal gardens, whereas the English and Japanese gardens are classified in the informal style of gardening.

Hedge

When shrub is planted on boundary for fencing, it is also called as hedge. In other word shrubs or trees planted at regular intervals to form a continuous screen is called a hedge. It may be ornamental or protective or both. Hedge also become an important feature of formal layout to serve various function as screening of the area out building, tennis court, vegetable garden, hardening of unwanted place etc. All the shrubs cannot be planted for making an ideal hedge due to different nature of growth.

Garden Walls : A garden lover will never like to block the view of his garden by putting a wall along its periphery. A garden as it is enjoyed from inside should also be visible from outside.

• Low brick or concrete or stone wall of say 60-90 cm height and to put over it some grills.
• Alternatively walls from 1.8-5.0 m may also be erected depending upon the size of garden.
• One may grow creepers such as Ficus repens over the wall.

Flower Beds: The most essential criterion of a flower beds is display the flowers in the best way possible. It may be born in mind that flowers look best when massed in a bed. The flower beds are of special importance under condition of the plains of India since they can be kept planted throughout the year unlike those in England or other parts of Europe where nothing can be grown in the open between November-March. Annuals and herbaceous, perennial flowers, flowering plants are grown in flower beds to provide massing effect of different colours borders is continuous beds of more length than width containing plant of one kind only.

Annuals

Annuals or seassonals are the group of plants which complete their life cycle in one season or in one year. Annuals are commonly known as seasonal as they complete the process of life like germination, growth flowering and seed formation in one season and finally the plant without. Annuals are widely used for garden decoration, cut flower and pot plants. They are frequently grown as bedding plant and in rock garden on the sides of lily pool and in shrubberies.

Annuals Border

In this type of border annuals are planted according to their height to beauty of the garden. Annuals border are found two types.

• Single face border.
• Double face border.

Single face border- In this type dwarf plant is kept on front of beds, medium height annuals are planted in the centre while the taller annuals are planted in the back.

Double face border- In this type tall annuals are planted in the center and medium and dwarf annuals are planted in both sides of tall annuals so that full view can be seen from both side.

Trees

A trees is a woody perennial plant with clear stem or trunk. Trees trend to be long leaved living up to several 100 year.

TREE- logical meaning of each letter

T - Temperature and micro-climate moderation.

R - Removal of air pollutant.

E- Erosion control.

E - Energy conservation.

Climbers

Climbers are group of plants which have weak stems and ability to climb up the support with the help of modified organs for sunlight and air, twiners differ from climber in the way that they do not posses such modified organs but twine around the support cover it and reach the top. Climber are woody or herbaceous plant which climb up the trees and other tall objects with the help of support of their special modified organ, such as tendrils, root, thorns, rootlets, hooks etc.

Tendrils- Bigonia gracilis, Clematis paniculata etc.

Thorns- Bougainvillea species, Climbing roses etc.

Rootlets- Ficus repens etc.

Hooks- Gallium aperine, Rubus australis etc.

Climbers and twiners are important group of plants. Climbers and twiners add the beuty, colour or fragrance in the garden and artificial structure like garden wall, topiary, arches, pergola etc. are made with the help of climbers.

Proper handling, packaging, transportation and storage reduce the post harvest losses of fruits and vegetables. For every one percent reduction in loss will save 5 million tons of fruit and vegetable per year. Processing and preservation technology helps to save excess fruit and vegetable during the glut season (off season). The technology has become a necessity to improve the food safety and strengthen nation’s food security. The technology helps to boost export of agricultural commodities in the form of preserved and value added products e.g. mango, pineapple, citrus, grapes, tomatoes, peas, potato and cucumber being processed on a large scale.

Principles and Methods of Preservation of Fruits

Preservation: Preservation means just protect the foods against the spoilage, but scientifically it may be defined as a science which deals with the process for prevention of decay or spoilage of the food is called preservation. In other words, just controlling the physical, chemical or microbial changes in the foods is called preservation.

Canning: Canning is the process by which sealing of the food product and sterilizing them by heat for long storage is known as canning.

History: The term canning was first invented by N. Appert (1804) in France. In honour of the inventor canning is also known as appertization (Appart is known as the father of canning) and Saddington (1807) in England first described the method of canning. Peter Durand (1810) got first british patent on canning of food in tin container and William under wood introduced canning of fruit on a commercial scale in U.S.A.

Principle of canning: The main principle of canning is destruction of spoilage organism by means of heat.

Drying- Removal of water from product by influence of non conventional energy source like sun and wind.

Dehydration- Process of removal of moisture by the application of artificial heat under controlled condition of temperature, humidity and airflow etc.

Advantage of dehydration over sun drying

The process of dehydration is much more rapid than sun drying.

Dehydration requires less floor area.

Dehydration is done under very hygienic condition.

Sun drying is not possible in cloudy weather or during rain while dehydration or mechanical drying is not dependent on weather.

The colour of dehydrated or mechanically dried fruit and vegetable remain uniform due to uniform drying temperature.

Jam: Jam is a product made by boiling fruit pulp with sufficient sugar to a reasonably thick consistency. Jam is mainly prepared by apple, pear, sapota, apricot, peach, papaya, karonda etc. It can be prepared from one kind of fruits or from two or more kind of fruits. In general jam contains 0.5-0.6% acid and invert sugar should not be more than 40%.

Growth: Growth involve cell division subsequently cell elongation. Increase in volume that is associated with fruit growth largely result of cell division or cell elongation or both. In some fruit such as apple, extension of intercellular space may also contribute the growth. Generally growth by cell division in early stage whereas, cell elongation during later stage.

Two distinct type of growth curve are observed when increase in such variable such as fresh weight, volume and dry weight of fruit.

1. Single sigmoid curve
2. Double sigmoid curve

In case of single sigmoid growth curve firstly there is rapid increase in size then after decline so there is only one rapid growth is observed e.g. Apple, pear, tomato, cucumber and strawberry.

In case of double sigmoid two rapid growth period are separated by an intermediate period that is less growth or no increasing growth curve. Double sigmoid growth curve can be used as two successive sigmoid curve thus there are three clearly define stage of growth.

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

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.

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

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.

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

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:

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.

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.

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

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.

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 :

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.

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.

A treatment is the combination of N & P

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

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.

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.

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

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.

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.

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.

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. 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.

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.

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.

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;

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;

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

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:

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.

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).

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.

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