eChapters

1. Botanical Name : Zizyphus rugosa Lamk

2. Family : Rhamnaceae.

3. Common Name : Wild Jujube, wrinkled jujube

4. Name in Indian languages

Churna, Suran (Hindi), Toran, Turan (Marathi), Totari (Tamil), Thodali, Tutari (Malayalam), Gottichettu (Telugu), Belahadu, Mulluhannu (Kannada), Kotte pan (Kodwa).

5. Description

Zizyphus rugosa is a large armed, spiny, evergreen tree growing around 5 - 9 metres tall with large elliptic usually subcordate leaves, paniculate flowers and wood is reddish, moderately. This plant is found chiefly in deciduous and semi-evergreen forest, hills and mountains below 1200 m altitude. The leaves are simple, and are broadly elliptic and short tips. Fruits are globose or pyriform, white when ripe. Whole fruit expect the seed is consumed at ripe stage. It is found in degraded forest areas of entire of Western Ghats. The plant is harvested from the wild for local use as a food and a medicine.

Stem cell research is one of the most important and promising areas of research in biological science in recent times. The stem cell technology has revolutionized modern biology. It is playing a major role for improving human as well as animal health by controlling number of incurable degenerative diseases and restoration of damage to organs via transplantation therapy. Stem cells are a specific group of cells that remain in an undifferentiated state and can be found in embryos, fetuses and adult individuals. Stem cells are primitive, undifferentiated cells present in almost every tissue that have the ability to differentiate into different types of tissue like bone, tendon, ligament, cartilage etc. Bone marrow stem cell is the ideal stem cell that is unspecialized and can undergo lineage specific differentiation into bone or blood cells under different signals or environment with new special function. Several verities of stem cells have been isolated and identified in vivo and in vitro. The two broad types of mammalian stem cells are embryonic stem cells that are found in blastocyst, and adult stem cells that are found in adult tissue, bone marrow.

Mesenchymal stem cells are multipotential cells capable of proliferation and differentiation into osteogenic, chondrogenic and adipogenic lineages both in vitro and in vivo. Minimal criteria for defining MSC according to International Society of Cell Therapy (ISCT) are (a) they must be plastic-adherent when maintained in a standard culture conditions, (b) MSC must express some surface antigen such as CD105, CD73, CD90, CD44 and must not express CD34, CD45, CD 14, CD19, and HLA-DR, and (c) MSC must differentiate in vitro into osteoblast, adipocytes and chondrocytes under specific differentiating condition.

Although traditionally MSC isolated from bone marrow, more recent reports have detailed the isolation of cells with MSC characteristics from a variety of tissues including umbilical cord blood, chorionic villi of the placenta, Wharton’s jelly, peripheral blood, fetal liver and lung, adipose tissue, skeletal muscle, periosteum, deciduous teeth, amniotic fluid, dental pulp, testis, synovium and the circulatory system. Bone marrow and periosteum sources are richest in young animals with their numbers diminishing, but still present in old age. It is estimated that MSCs represent approximately only 0.01 to 0.001% of the total nucleated cells within isolated bone marrow aspirate. The yield of MSCs from bone marrow varies from species to species. e.g., Canine =1 in 2.5 × 10⁴; Feline =1 in 3.8 × 10⁵; Murine =1 in 10.8 ×10³ to 1 in 3.45×10⁴ and Horses =1 in 4.2 × 10³. Despite this low number, they are remains a great interest, as they can be easily isolated from a small aspirate and culture-expanded through as many as 40 population doublings to significant numbers in about 8 to 10 weeks.

Bone marrow contains variety of tissues including hematopoietic lineage cells and mesenchymal stem cells (MSCs). The hematopoietic cells are the major source of the blood cells in the adult body which are regulated within the microenvironment of the stromal cells of the bone marrow. MSCs are multipotent cells and can be differentiate into various cell lineage depend upon their environment and culture condition in which they are kept. Rat bone marrow derived stem cells (rBM-MSCs) have long term self renewing and capabilities of pluripotency including osteoblast, adipocytes, chondrocytes, tenocytes, muscle cells and neurogenic cells, which make them ideal source of stem cells for regeneration of injured tissue.

Bone marrow derived mesenchymal stem cells have been isolated from different species. Each species is having different culturing and growing capacity. For example human bone marrow derived stem cells are easy to harvest and maintain in culture condition whereas rat MSCs it is more difficult compared to other species. Technical difficulties in isolation limited the number of animal experiment and its required animal transplantation model for pre clinical studies. Density gradient centrifugation, plastic adherence and immunomagnetic selection like various methods are available for isolation of MSCs from bone marrow. No appropriate methods are available for selection of suitable cells. Each method has their own pros and cons. Therefore, in this chapter, combined density gradient centrifugation and plastic adherence as easy and reliable method for isolation, culture and characterization of rat bone marrow derived mesenchymal stem cell is described.

Mesenchymal stem cells are multipotential cells capable of proliferation and differentiation into chondrogenic, osteogenic and adipogenic lineages. Minimal criteria for defining MSC according to International Society of Cell Therapy (ISCT) are (a) they must be plastic-adherent when maintained in standard culture conditions, (b) MSC must express some surface antigens such asCD105, CD73, CD90, CD44 and must not express CD34, CD45, CD 14, CD19, and HLA-DR, and (c) MSC must differentiate in vitro into osteoblast, adipocytes and chondrocytes under specific differentiating condition. 
Although MSC is traditionally isolated from bone marrow, more recent reports have detailed the isolation of cells with MSC characteristics from a variety of tissues including umbilical cord blood, chorionic villi of the placenta, Wharton’s jelly, peripheral blood, fetal liver and lung, adipose tissue, skeletal muscle, periosteum, deciduous teeth, amniotic fluid and synovium. Bone marrow and periosteum sources are richest in young animals with their numbers diminishing, but still present in old age. One of the attractive advantage of bone marrow derived mesenchymal stem cells (BM-MSCs) as a source of cell transplantation is their low immunogenecity. Recently, several studies have reported that BM-MSCs may be immune-privileged cells that do not elicit immune response due to an absence of their immunologically relevant cell surface markers. BM-MSCs also are known to inhibit proliferation of T lymphocytes, B lymphocytes, dendritic cells and natural killer cells.

The knowledge and principles from interconnected disciplines of bioengineering, material science and life sciences are combined in tissue engineering with the aim of developing a construct that can wholly or partly restore/maintain/augment the function of a damaged tissue or organ. Tissue engineering construct generally comprises cells on a suitable scaffold. The ultimate functionality of a tissue engineering construct is mainly dependent on the cell behavior on scaffolds. The proliferation and cell viability on scaffolds varies from one cell type to another and from one species to another. So it is important to investigate individual cell-scaffold combinations.

The similarity in structure and composition to bone mineral, osteoconductive properties, ability to integrate with the bony tissue and absence of immune response makes hydroxyapatite [Ca10 (PO4)6(OH)2] the choice of calcium phosphate biomaterial with only disadvantage of slow rate of resorption. On contrary, silica–calcium phosphate composite in comparison to calcium phosphate is rich biomaterials having a faster resorption rate. Studies have shown that coating of hydroxyapatite with a calcium silicate containing layer encourages cell proliferation and osteogenic differentiation of human bone marrow-derived stromal cells.

In this chapter a composite scaffold (calcium silicate, hydroxyapatite and tricalcium phosphate), namely HASi with elements in the following percentages: 66.36 % - calcium, 25.35 % - phosphorus and silicon - 8.29 % and porosity of 50 – 500 µm is undertaken to evaluate the rBMSC proliferation and differentiation properties in vitro with the aim of assessing cytocompatibility of HASi and rBMSC in terms of cell attachment, cell morphology, cell proliferation and osteogenic differentiation.

Fracture healing is a complex process in which damaged bone restores its original architecture through a cascade of molecular and cellular events. The process of fracture healing is unique, in that it does not form a poorly organized replacement matrix, otherwise known as scar tissue, but rather regenerates the original matrix and retains its mechanical properties. Normal fracture and bone repair follows a known sequence of events includes: haematoma formation, inflammation, angiogenesis, osteogenesis, and bone remodeling.

For the repair of bone defects, a tissue engineering approach would be to combine cells capable of osteogenic (i.e. bone-forming) activity with an appropriate scaffolding material to stimulate bone regeneration and repair. An ideal tissue-engineered bone substitute should possess 3 elements: osteoprogenitor cells, osteoinductive factors, and an osteoconductive scaffold, and is considered as a potential substitute for autologous bone transplantation. Tissue engineering approaches have proven very effective in bone regeneration recently, and the successful repair of bone defects has been demonstrated in larger animals like canine, goat and sheep.

Stem cells from adult tissues are attractive materials for cell therapy, gene therapy, and tissue engineering. These cells generally have restricted lineage potential when compared to embryonic stem cells, and this may be advantageous in certain therapeutic applications. Till and McCulloch in 1961 describes the presence of hematopoietic progenitor cells in bone marrow. After this, the concept of MSCs is brought to light by Friedenstein and co-workers who demonstrated the osteogenic potential of BM cells by heterotrophic transplantation.

Despite the benefits that minimally invasive surgery and osteosynthesis have brought to fracture management and bone healing, there are still many circumstances where bone healing remains challenging. Large bone defects are serious complications that are most commonly caused by extensive trauma, tumour, infection, or congenital musculoskeletal disorders. In nonunion cases repairing of bone defects with composite biomaterials as defect filler can promote bone regeneration. Currently, the gold standard for bone regeneration is use of autogenous bone graft. In order to avoid morbidity at the donor site or if large amount of autogenous bone is needed, bone substitution materials can be used.

Bone substitution materials can be combined with cells such as mesenchymal stem cells (MSCs) to increase bone formation. Bone-marrow derived mesenchymal stem cells (BMSCs) represent an attractive cell population for tissue regeneration. Bone marrow stem cells are pluripotent cells that have been used to facilitate bone repair because of their capability of differentiating into osteoblasts. Several studies on the regeneration of bone have shown that cultured BMSCs, seeded on different bioabsorbable implants are able to induce bone formation in vivo and lead to improved healing of critical-size defects.

Disruption of the cellular or anatomical continuity of the normal organ structure is known as wound. Healing involves migration, infiltration, proliferation and differentiation of several cell types like keratinocytes, fibroblasts, endothelial cells, macrophages, and platelets which are culminate as an inflammatory response, new tissue formation and wound closure. Healing of the wound starts from the moment of the injury and can continue for varying periods of time depending on the extent of wound. The process of wound healing involves coagulation, inflammation, formation of granulation tissue, matrix formation, remodeling of connective tissue, collagenization and acquisition of wound strength.

Chitosan is a -1, 4-linked polymer of glucosamine (2-amino-2-deoxy—D-glucose) and lesser amounts of N-acetylglucosamine. It is a derivative of chitin (poly-Nacetylglucosamine) which is the second most abundant biopolymer after cellulose. Chitosan is first discovered in 1811 by Henri Braconnota, a French chemist and pharmacist. Later in the century, chitin is found in crustaceans (such as crabs, lobsters, shellfish and shrimp). Chitosan is a biodegradable polymer and it accelerates wound healing. It has been reported that chitosan permits regeneration of tissue elements in skin wounds and has positive application effects on wound healing.

The cornea is the anterior transparent part of the fibrous tunic of the globe which has both mechanical and optical function. It functions to support the intraocular contents and transmit and refract light. Histologically, the cornea is composed of an outer stratified squamous corneal epithelium and its basement membrane, an underlying collagenous stroma, a thin acellular membrane, and an inner monolayer of cells, the corneal endothelium. In dogs, the central cornea is thinner than the peripheral cornea, whereas regional differences in thickness may not exist in cats. The active pumping mechanism of corneal endothelium and the barrier function of anterior epithelium keep the cornea in a state of relative dehydration. Compromise of either of these mechanisms will allow corneal edema to develop, with a concomitant increase in corneal thickness. Corneal ulceration is one of the most common ophthalmic disorders in veterinary medicine. Corneal ulcers are classified based on depth and ease of healing. There are several phases during the corneal epithelial wound healing process, including a lag phase where cells alter their metabolic status; a migration phase to cover the bare surface; a proliferation phase and a differentiation phase, where cells stratify and re-establish multiple layers of distinct cells. Methods of surgical treatment for canine corneal ulcers include temporary tarsorrhaphy, third eyelid flap, conjunctival pedicle graft, tissue adhesive, corneo-scleral graft and transplantation of natural or synthetic material.

Full thickness burn wound will not heal spontaneously which need early excision and adequate coverage to prevent fluid loss and risk of infection. Major portion of the skin is lost during burn injury which is the active layer for protecting the entry of micro organism from outside environment. So far, meshed autologous skin graft is regarded as the gold standard for treating extensive full thickness burns. However, their frequent use may be hampered by several impediments such as limited availability, donor site morbidity, associated with additional scarring. Severe burn patients invariably lack additional skin donor site. Moreover, creation of donor site in burned patient is equivalent to second degree burn and which further increase the total body surface area affected (TBSA). In past decades, numerous biomaterials have been investigated for the use as a dermal skin substitute for burn patients but none give promising results. The innovative tissue engineered bioactive materials offer potential tissue regeneration and prevent the tissue deterioration.

The extracellular matrix (ECM) derived naturally from mammalian tissue have been successfully used as scaffold for tissue reconstruction and wound management in clinical applications. ECM can be derived from various tissue or organs of different species but most commonly, porcine small intestinal submucosa and urinary bladder have been successfully used as scaffold for reconstruction. ECM is obtained by process of decellularization that involves the use of various ionic and non ionic detergents, acids, enzymatic solutions and mechanical/physical methods.

Burn wounds are considered to be one of the most devastating kinds of trauma that affect the physical, physiological and psychological well being of an individual. Based on the depth of the affected area, burn wound is divided into three categories: first degree (superficial), second degree (partial) and third degree (full thickness). Extensive and deep burns result in scar formation and subsequent deformation of the affected part. Healing of burn wound involves several interrelated and overlapping stages such as inflammatory, proliferative and remodeling phases. The interaction of a variety of mediators, cells and extracellular matrix proteins are involved in this three phases of healing.

Burn wound therapy has been performed in various ways such as bioengineered skin grafts and administration of growth factors. Small intestinal sub-mucosa (SIS) is a resorbable xenogenic bioscaffold that has induced constructive remodeling in variety of animal models. It is unique from other acellular matrices as it contains few growth factors such as fibroblast growth factor (FGF) and transforming growth factor (TGF) that play a role in the regenerative process. Chitosan is currently proposed to be one of the most promising polymers in wound dressing development and it accelerates wound healing. Epidermal growth factor is known to stimulate growth of keratinocytes in vitro, thus it might be expected to promote wound healing.

In this chapter efficacy of Chitosan and porcine acellular small intestinal sub-mucosal (SIS) matrix seeded with bone marrow derived mesenchymal stem cells (BM-MSCs) and recombinant murine epidermal growth factor (m-EGF) on healing of full thickness burn wound in rat model is described.

The liver is an organ that plays a central role in maintaining metabolic homeostasis, in its functions of metabolism, synthesis and storage of nutrients. The cells in the liver are parenchyma cells and non- parenchyma cells. The former include hepatocytes (80% of the cell population) and the later includes endothelial cells, Kupffer cells, lymphocytes and stellate cells. Liver dysfunction or failures are having different etiologies. Liver cancer has the fifth highest cancer incidence in the world, and is the third highest cause of cancer related deaths, with resection of the liver remaining the only curative option.

Following liver injury, the repair process involves two distinct phases: a regenerative phase, in which injured liver cells are replaced with regenerated hepatocytes; and a phase known as fibroplasia or fibrosis, in which connective tissue replace normal parenchyma tissue. The process even though initially beneficial becomes pathogenic when it is not controlled appropriately. Extensive accumulation of extra-cellular matrix (ECM) components ultimately leads to cirrhosis and liver failure. Thus, the ideal strategy to treat liver injury is to generate new hepatocytes replacing damaged cells without causing excessive ECM deposition.

Bone is the only tissue in the body able to heal without microscopical scarring. On the other hand fracture healing is a slow process causing long immobilization periods with consequently high costs for fracture healing giving rise to delayed healing, fibrous healing or non-healing with subsequent problems for both, the patient and the orthopedic surgeon. Fracture healing is complex phenomenon which involves numerous cells, regulators of cell function and biochemical interactions in the repair process. The healing potential of bone, in a fracture model is influenced by a variety of biochemical, biomechanical, cellular, hormonal and pathological mechanisms. A continuous occurring state of bone deposition, resorption and remodeling facilitates the healing process.

Biodegradable and bioinert ceramic materials such as hydroxyapatite (HA), tri­calcium phosphate (TCP), aluminum-calcium-phosphate (ALCAP) ceramics provides scaffold to support the attachment and migration of newly formed bone cells into the osseous defect and also help in formation of a vascular network through the newly formed bone. Bioceramic also act as a carrier to deliver stem cells for osteogenesis.

Specific bone-inductive proteins/growth factors can induce bone formation and healing in vivo. These morphogens are therefore, to be an ideal alternative to autogenous bone grafts. These findings initiating intensive research into bone regeneration orchestrated by putative soluble signals and lead to the discovery and identification of an entirely novel family of protein initiators collectively called the bone morphogenic protein (BMPs), which belong to transforming growth factors-ß (TGF- ß) super family. These growth factors might possess a biological activity that is site and tissue specific in the induction of bone formation. Different growth factors have been demonstrated to possess osteoinductive and fracture healing properties.

Collect the femur and tibia and clean them very well from blood, tendons, muscle, skin and other tissues (using scalpel)

Cut off epi-and metaphysis from the long bones with electric/manual bone saw
Transfer the open bones to a falcon tubes containing cell culture medium/DPBS without FBS but containing 1% PS (Penicillin-streptomycin)
Transfer the bones to a Petri dish containing 5 ml medium without serum (FBS) and without antibiotic (40-50 mm Petri dish)
Remove the pteri dish to the laminar cabinet
Collect the bone marrow by flushing out to a 50 ml falcon tube (use syringes and needles to flush out the bone marrow; flush out the bone marrow with complete (10% FBS, 1% PS, 90% DMEM-LG) culture medium
Centrifugation: 300rpm, 8 min (in the meanwhile prepare 5 ml of Ficoll into a 15ml falcon tube)
Aspire the medium

1.    Collect fat tissues from euthanized animals into a Falcon tube containing DPBS + 1% PS ( for transferring lab to lab)
2.    Wash 3 times with DPBS + 1% PS
3.    Inside a laminar cabinet, transfer the fat to a Petridis (use an sterile spatula to collect the fat)
4.    Cut the fat into small (mm) pieces using scalpel and forceps or scissors
5.    Transfer the fat into a Falcon tube (eg into a 50 ml Falcon tube you can add upto 10 ml fat tissues)
6.    Wash the fat by adding approx 40 ml DPBS; Centrifugation: 430g, 10 min (without break)
7.    Transfer the fat into a fresh 50 ml Falcon tube (with the help of sterile spatula); Repeat steps 5 to 7 for 2-3 times

1.    Collect the bone marrow from iliac crest of rabbit, dog, goat and sheep; sternum of pigs by 16-18 G bone marrow biopsy needle under sedation/anesthesia and maintain strict asepsis protocol
2.    To aspirate bone marrow, negative pressure should create in the syringe by pulling back the plunger. Syringe should be first flushed with heparin before bone marrow collection
3.    Collect approximately 2.5-5 ml of bone marrow from each animal
4.    Mix the bone marrow sample with equal volume of low DMEM having 10% FBS and 1 ml of PS and then layer on 5 ml of histopaque/Ficol-Paque Plus in a falcon tube
5.    Centrifuge at 3000 rpm for 30 min
6.    Collect stem cells from the interface with the help of a micropipette
7.    Cells wash thrice with DPBS 
8.    Centrifuged at 2000 rpm for 5 min

1.    Remove the growth media
2.    Wash 2 times with warm PBS (w/o Ca and Mg)
3.    Add 5ml of trypsin /EDTA 0.35 % (Invitrogen), place at 370C in a CO2 incubator for 4-5 min
4.    Wait until cells are detached/lifted and make sure by examining on microscope
5.    Add equal volume (5ml-1:1) of growth medium as appropriate and mix with 5 strokes
6.    Transfer into 15 ml centrifuge tube
7.    Centrifuge at room temperature for 4 min in 980 rpm
8.    Remove supernatant complete but carefully without touching the cell aliquot
9.    Re-suspend in 10ml DPBS (with Ca and Mg)-5 strokes
10.    Immediately after mixing transfer 100µl of cell suspension into Casy vial, containing 10ml Casyton

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