PRINCIPLES OF AGRICULTURAL EXPERIMENTATION1

Experimentation

1. PRINCIPLES OF CROPS EXPERIMENTATION CODE: APT06210 CREDITS: 12 TUTOR: TRIPHONIA NDENDYA 2022/2023

2. 1.0 INTRODUCTION An experiment: is defined as the systematic procedures carried under controlled condition , in order to discover new idea or test hypothesis. OR is an investigation set up to provide answers to question or questions of interest. For example: We may wish to conduct experiment to test the efficiency of using organic manure (goat manure) in groundnut production or different inorganic fertilizers at different rates or different spacing. Experiment is more likely to involve comparison of treatments, for example methods, varieties, spacing etc.

3. Cont.. However in some cases experiments do not involve comparison of one treatment with the other treatments, hence experiment can be absolute or comparative If we conduct the experiment to examine the usefulness of the newly developed fungicide for controlling certain plant disease without comparing its effect with other fungicides, the experiment will be an absolute experiment. If we conduct the experiment to assess the effectiveness of one fungicide as compared to the effect of other fungicides on controlling plant disease, then the experiment is said to be comparative experiment. Experimentation : it involves designing and testing of different factors of interest using experiments.

4. The major concern of experiment : The primary concern in any experiment is to accurately estimate or compare effects of certain factors or treatments on the productive or physiological performance of plants.

5. A factor is a variable which is believed to affect the outcome of the experiment. Factors are also called test materials . Factors in agricultural experiments are simply identifiable as categories of inputs or management practices. Example of factors for crop experiments are varieties, fertilizers, herbicides etc.

6. Level The various values or classifications of the factors are known as the levels of the factor (s) Is individual settings /conditions of factor.

7. NB: A factor is usually expressed by capital alphabet and its level by the same alphabet with suffixes. Its good idea to use alphabets which help one to understand what these alphabets stands for. E.g. when comparing varieties you may use V 1 ,V 2 ,V 3 …Nitrogen rates, N 1 ,N 2 ,N 3 .. And Spacing….S 1 ,S 2 , S 3

8. Treatment or Treatment combination is one or more things that are compared or investigated in an experiment. OR It is a dosage/ amount of materials or procedure which is to be tested in experiment. Example: In experiment involved spacing trial [a factor] and a fertilizer trial[another factor], now trial treatments can be:- Planting at 75x60cm with 60kgs N/ha. Planting at 25x10cm with 20 kgs N/ha

9. NB: Total number of treatments is the product of levels in each factor. For above example will be: [ 2 factors ] x[2 level ] = 4 treatments

10. Variable(s) is any quantitative or attribute whose values varies from one unit of investigation to another. A variable is a characteristic that changes from unit to unit or one individual to another individual. They shows variability Example :plant heights, weights, pod height, number of flowers, fruit height, etc.

11. Experimental unit: Is the unit of experimental material to which the application of the treatment is made and on which the variable under study is measured . Or Are the pre-determined plots or the blocks where different treatments are applied. Such experimental units must be selected (defined) very carefully. Examples, a plot in agricultural experiments and petri dish in laboratory experiments. Experimental unit measures the effectiveness of factor or treatment.

12. Experimental area : Is that area where the experiment is to be conducted. It can divided to form replicates and that replicates divided to form experimental units. It is selected from an experimental site.

13. Experimental factors (variables): These are factors that are of experimental interest which tend to vary from one treatment to another. Non-experimental factors (variables): T hese are factors that are not of experimental interest. These are factors which remain fixed or applied uniformly over the trial.

14. Control: It is used to restrain experimental conditions. Experimental unit does not receive any treatment, but the effectiveness of other treatments should be found through comparison with that control . Response (output of experiment ): This is the numerical results observed for a particular experimental unit. e.g. (grain yield) one may be interested to know the amount of a grains in kg produced when different types of fertilizers are applied to a piece of land.

15. Population: It is the aggregate from which the sample is chosen for measurement of particular variable. For example total number of maize plants in the field of 1 acre. Sample: It is a part of population used as a substitute for population, e.g. measuring 10 plants in each plot of maize experiment. The value obtained from 10 plants represents the rest of plants in a given plot.

16. Sampling unit: Is the unit on which actual measurement is made, e.g. 1 0 plants in a 10mx5m maize plot. It is potential member of the sample. Data : is the set of values assigned to response variable or set of quantitative values obtained by measuring or counting.

17. The purpose of research The purpose of research is to discover answers to questions through the application of scientific procedures. To find out the truth which is hidden and which has not been discovered yet. To test a hypothesis of a causal relationship between variables (such studies are known as hypothesis-testing research studies). To address different production problems which face farmers through development of appropriate technologies. E.g. new crop varieties, new animal feeds, appropriate animal housing, e.tc.

18. To portray accurately the characteristics of a particular individual, situation or a group (studies with this object in view are known as descriptive research ); To determine the frequency with which something occurs or with which it is associated with something else (studies with this object in view are known as diagnostic research );

19. Principles of experimentation There must be clear statement of research aims, which defines the research question. There must be information sheet for participants, which sets out clearly what the research is about, what it will involve ,which laid down prior to research beginning The methodology is appropriate to the research question , if is qualitative or quantitative

20. iv. The research should be carried out in an unbiased fashion. researcher should not influence the results of the research in any way. v. From the beginning, the research should have appropriate and sufficient resources in terms of people, time, transport, money e.t.c vi. People conducting the research should be trained in research and research methods vii. All research should be ethical and not harmful in any way to the participants.

21. 2.TYPES OF EXPERIMENT AND SURVEY There are three basic types of experiment in agriculture, which are i . Exploratory experiment: these types of experiment seek to better define and characterize a particular production problem. Used to find causes to problem and problem prioritization. ii. Determinative experiments : test possible solutions to a production problem that is well understood.

22. iii. Verification experiments : used to test technology in larger scale and in wide range of circumstances. These kind of experiment are meant to publicize the positive attributes of treatments (demonstration plots may be used).experiment on these category are usually on-farm

23. Survey A survey is the gathering and analysis of information about a topic, an area or a group of people Surveys can be an economical and efficient tool for collecting information, attitudes and opinions from many people and for monitoring project/program’s progress. When designed and administered correctly, the information collected can be a true reflection of opinions held by the group from which you want information However, a high level of knowledge and skill is needed to design and implement a good quality survey.

24. Types of survey Formal(structured) survey : is a kind of survey which collect standardized information from carefully selected sample. They use questionnaires in which the wording of the questions and the order in which they are asked is fixed. They are have a specific direction from begin up to the end The data from structured interview are easy to compare and analyzed statistically.

25. ii. Informal(Unstructured) survey: They use questionnaire / checklist which are not standardized and not ordered in a particular way to collect information. They have no specific direction in way they performed, question asked respondent depend on previous answer of respondent It is particularly useful for exploratory research where lines of investigations are Cleary defined. It provides opportunity to explore the various aspects of the problem in an unrestricted manner

26. There are nine steps to conducting a survey, including: 1: Decide what you want to find out 2: Decide /Select a sample to survey 3: Select survey types and method 4: Write the survey questions 5: Trial the survey questions 6: Conduct survey 7: Analyze information 8: Interpret data 9: Report findings

27. 1.Decide what you want to find out The first decision to be made is what information do we need to collect. Means a topic to deal What do the survey questions need to determine 2.Decide /Select a sample to survey As it is not usually possible to survey the whole community, you will need to survey a sample that represents the group. The sample needs to be representative of the people you really want to talk to so that as little bias as possible occurs.

28. 3.Select the survey type & method The survey type determines the way a survey is to be conducted, what is to collected and what is to recorded. The type of survey used depends on the type of information you want, how much information can be analyzed and the time and resources available. A combination of survey types and method can also be used.

29. There are three common methods of surveys: a. Self-completed questionnaires Are most commonly presented as written questions on paper. The questions are completed or ‘filled in’ by the participant, usually without any assistance from the people who designed the questionnaire. c. Face-to-face interviews Involve an interviewer asking questions verbally to an individual ( interviewee) personally. b. Telephone surveys Involve an interviewer asking questions verbally to a single, anonymous individual over the phone.

30. 4.Write down the survey questions Questionnaires should be designed to be attractive, easily understood, easily answered and to give you the required information. This step looks at: i . the types of questions to ask ii. how to design questions iii. sequencing and presentation of questionnaires

31. 4.i.The types of questions to ask There are two main types of questions: a. open-ended b. closed-ended. a. Open-ended questions Are questions that can have unexpected answers as they allow the answer to be left entirely to the respondent so they can express their feelings without restriction. They can generate a wide range of replies Open-ended questions give ‘qualitative’ information

32. Example Qn 1. In your village there is decrease in crop production? …………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………. Qn. 2 what to be done in order to increase crop production in your village?...........................................................................................................................................................................................................

33. b. Closed-ended. Closed-ended questions are questions followed by a list of answers and a format for making an answer Closed-ended questions provide ‘quantitative’ information that can be counted. The information can be discussed in terms of numbers, frequencies, and percentages.

34. Example Question 1 . Are you a farmer ? YES ⃝ NO ⃝ Question 2 . Have you practiced farming activities ? YES ⃝ NO ⃝ Question 3 . Farmers in your village they prefer to produce which category of crops ? i . Annual crops ⃝ ii. Perennial crops ⃝

35. Reading assignment Outline the advantages and disadvantages of open ended questions Outline the advantages and disadvantages of close ended questions

36. 5: Trial the questionnaire or interview questions A trial or pilot study refers to testing or having a practice run of the questionnaire or interview. Circulate the questionnaire among colleagues, friends and a variety of people to get their opinion It is also necessary to choose a small number of the actual target group Incorporate any valid suggestions into the questionnaire design.

37. Testing is done to ensure: the information you receive is the information you set out to get there are no unexpected weakness or imperfections the information you obtain can be interpreted

38. 6: Conduct the survey It is the point when the survey is done It may involves Election of a questionnaire coordinator, Organise questionnaire distribution, Organise questionnaire returns and Send reminder notices for self-completed questionnaires

39. 7.Analyse the data An analysis and discussion is necessary to make sense of the data collected. The method of analysis used depends on the type of data gathered. 8.Interpreting results When interpreting what the results of the survey mean, it is important not to generalise too much. It is also important to recognise and acknowledge any possible bias in the results. Not all people in the community have been asked (only a representative sample),

40. 10: Report the findings It is important that information gathered is given back to the community from which the information was obtained. Or to extension institute The survey results should also be given to and used by relevant decision-makers. In the report, it is important to recognise and discuss any difficulties or problems that might affect the interpretation and generalisation of the findings.

41. Practical 1 Conduct a survey and collect data

42. SAMPLING What is sampling………. ?????? There are two ways of choosing a sample: Probability sampling : is the one in which every member in a population have equal chance to be selected. Non probability/intention : is the one in which the sampler or investigator decide in advance the factor that will determine whether a particular unit/ member of population should be included in the sample. Not all member has equal chance to participate in the sample.

43. A:Probability sampling techniques It includes the following sub techniques Simple random sampling Systematic sampling Cluster sampling .Stratified sampling

44. SIMPLE RANDOMLY SAMPLING Is the method of obtaining sample where every individual of a population is chosen randomly by chance. Every individual has the same probability of being chosen to be part of a sample. SYTEMATIC SAMPLING. Researcher divide the entire population into strata or subgroup within a population. Each sub group is separated from the others on the basis of a common characteristics such as gender, sex, religion age. For example if you are dividing a students population by its course engineers, linguistics, and education, SYSTEMATIC SAMPLING. Researcher use this method to choose the samplSe members of a population at regular intervals it requires selecting a starting point for the sample and sample size determination that can be repeated at regular interval eg .

45. Sample of 500people from population of 5000. he/she numbers the population from 1-5000 and will choose every 10 th individual to be part of sample. Total population/sample size =5000/500 =10

46. CLUSTER SAMPLING. Is the method in which the researcher divides the population into smaller groups called clusters and then randomly select some of these cluster as your sample. Uses of probability sampling 1. Reduce sample bias. 2. Used in diverse population. 3. Create an accurate sample.

47. B:Non probability sampling It includes the following sub techniques Accidental sampling: Quota sampling Purposive or judgmental sampling

48. 3. IMPORTANT CONCEPTS AND DEFINITIONS i . Experiment: Is an investigation set up to provide answers to a question or questions of interest. OR Is the process of examining the truth of a statistical hypothesis, relating to some research problem. For example, an experiment conducted to test the efficiency of a certain newly developed drug for curing a certain skin condition in animals.

49. ii . Experimental Design or Designing of an Experiment: A design is a plan/ framework for obtaining relevant information to answer the research question of interest. In other words, it can be defined as the complete sequence of steps laid down in advance to ensure that the maximum amount of information relevant to the problem under investigation will be collected. Example RCBD, RCD, Split plot

50. iii. A factor is a variable which is believed to affect the outcome of the experiment. Factors are also called test materials. Factors in agricultural experiments are simply identifiable as categories of inputs or management practices. Example of factors for crop experiments are varieties, fertilizer, herbicide and for livestock can be pastures, vaccines and breed

51. iv. Level The various values or classifications of the factors are known as the levels of the factor (s) Is individual settings /conditions of factor Factor LEVEL Nitrogen rate 0kgN/ha,80kgN/ha,100kgN/ha Spacing 90x30cm,75x50cm, 75x60cm Variety SARO,IR64,NERICA -1 Planting date Feb, March, April, e.tc

52. NB: A factor is usually expressed by capital alphabet and its level by the same alphabet with suffixes. Its good idea to use alphabets which help one to understand what these alphabets stands for. E.g. when comparing varieties you may use V 1 ,V 2 ,V 3 …Nitrogen rates, N 1 ,N 2 ,N 3 .. And Spacing….S 1 ,S 2 , S 3

53. v. Treatment or Treatment combination is one or more things that are compared or investigated in an experiment. It is a dosage/ amount of materials or procedure which is to be tested in experiment. Example: In experiment involved spacing trial [a factor] and a fertilizer trial[another factor], now trial treatments can be:- Planting at 75x60cm with 60kgs N/ha. Planting at 25x10cm with 20 kgs N/ha

54. NB; Total number of treatments is the products of levels in each factor. For above example will be [2factors] x[2level] =4treatments

55. vi. Experimental unit: Is the unit of experimental material to which the application of the treatment is made and on which the variable under study is measured . Or Are the pre-determined plots or the blocks where different treatments are applied. Such experimental units must be selected (defined) very carefully. Examples, a plot in agricultural experiments and petri dish in laboratory experiments. Experimental unit measures the effectiveness of factor or treatment.

56. vii. Experimental area : Is that area where experiment is to be done. It can divided to form replicates and that replicates divided to form experimental units It is selected from an experimental site

57. Experimental factors (variables): These are factors that are of experimental interest which tend to vary from one treatment to another. Non-experimental factors (variables): T hese are factors that are not of experimental interest. These are factors which remain fixed or applied uniformly over the trial.

58. Control: It is used to restrain experimental conditions. Experimental unit does not receive any treatment, but the effectiveness of other treatments should be found through comparison with that control . Response (output of experiment ): This is the numerical results observed for a particular experimental unit. e.g. (grain yield) one may be interested to know the amount of a grains in kg produced when different types of fertilizers are applied to a piece of land.

59. Population: It is the aggregate from which the sample is chosen for measurement of particular variable. For example total number of maize plants in the field of 1 acre. Sample: It is a part of population used as a substitute for population, e.g. measuring 10 plants in each plot of maize experiment. The value obtained from 10 plants represents the rest of plants in a given plot.

60. Sampling unit: Is the unit on which actual measurement is made, e.g. 1 0 plants in a 10mx5m maize plot. It is potential member of the sample. Data : is the set of values assigned to response variable or set of quantitative values obtained by measuring or counting.

61. xiii) Analyze: study or examine in order to learn about something. Analysis simply means separation of whole into its parts for study and interpretation. xiv) Data analysis: involve the application of one or more statistical techniques to set of data with the purpose of extracting as much information as possible from given data

62. xv. Tabulation Is the process of summarizing raw data and displaying them in compact form for further analysis xvi. Experimental Error Is a measure (gauge) of the variation among experimental units receiving same treatments. The difference among experimental plots treated alike is called experimental error.

63. That measures mainly inherent/ inborn variation among them. This error is the primary basis for deciding whether an observed difference is real Or just due to chance. Thus experimental error is a technical term and does not mean a mistake, but includes all types of extraneous variation due to;

64. Sources of experimental errors Inherent variability in experimental units (ii) Error associated with the measurements made (i.e. eye parallax or instrumental error) (iii) Lack of representative of the sample to the population under study

65. Experimental error cannot completely be controlled, but can be reduced. Variations among experimental units sometimes cannot be avoided in practice, some variations are controllable and some are beyond the control of the experimenter. Other factors such as soil fertility, moisture, and damage by insects disease and birds also can affect responses like yields, plant height and pod weight.

66. Because these other factors affect responses, a satisfactory evaluation of the two treatments must involves a procedure that can separate treatment difference from other sources Of variation. That is, the experimenter must be able to design an experiment that allows him to decide whether the difference observed is caused by treatment difference or by other factors.

67. Every experiment must be designed to have a measure of the experimental error. by ( a ) Replication means repetition, another copy, to look (exactly) alike. It is the number of times a treatment appears in an experiment In the same way that at least two plots of the same treatment are needed to determine the difference among plots treated alike, Experimental error can be measured only if there are at least two plots planted to the same variety (or receiving the same treatment).

68. Thus, to obtain a measure of experimental error, replication is needed. This refers to the number of experimental units on each treatment A treatment is said to be replicated if it is applied to more than one experimental unit In short replication means the number of times a treatment appears on experimental units

69. ( b ) Randomization Randomization is more involved in getting a measure of experimental error than simply planting several plots of the same treatment . For example, suppose, in comparing two rice varieties, the plant breeder plants varieties A and B each in four plots Randomization ensure each variety will have an equal chance of being assigned to any experimental plot and, consequently, of being grown in any particular environment existing in the experimental site.

70. Control of error Because the ability to detect existing differences among treatments increases as the size of the experimental error decreases Now a good experiment incorporates all possible means of minimizing the experimental error. Three commonly used techniques for controlling experimental error in agricultural research are:-

71. Blocking Proper plot technique Data analysis

72. a. Blocking Dividing the field into several homogenous parts is known as ‘blocking. In By putting experimental units that are as similar as possible together in same group (generally referred to as a block) and by assigning all treatments into each block separately and independently, variation among blocks can be measured and removed from experimental error.

73. In general, blocking is the means at which we hold an extraneous factor fixed, so that we can measure its contribution to the total variability of the treatment by means of a two-way analysis of variance

74. b. Proper plot technique For almost all types of experiment, it is absolutely essential all other factors aside from those considered as treatments be maintained uniformly for all experimental units. For example, in variety trials where the treatments consist solely of the test varieties, it is required that all other factors such as soil nutrients, solar energy, plant population, pest incidence, and an almost infinite number of other environmental factors are maintained uniformly for all plots in the experiment.

75. c. Data analysis In cases where blocking alone may not be able to achieve adequate control of experimental error, proper choice of data analysis can help greatly

76. xvii. Research hypothesis: When a prediction or a tentative answers to be tested by scientific methods, it is termed as research hypothesis. The research hypothesis is a predictive statement that relates an independent variable to a dependent variable. Usually a research hypothesis must contain, at least one independent and one dependent variable.

77. Predictive statements which are not to be objectively verified or the relationships that are assumed but not to be tested, are not termed research hypotheses The hypothesis has to be verified or disproved through experimentation. These hypotheses are usually suggested by past experiences, observations, and at times by theoretical considerations

78. 4.STEPS FOR DESIGN ON STATION EXPERIMENTATION On station experimentation these are experiments conducted by researcher/experimenter on the station field Conducted with high control of condition while on farm experiment are performed with less control of conditions Usually only researchers are involved while on farm both researcher and farmers can be involved

79. OSE are really experiment while OFE are not really experiments are just demonstrations During designing OSE the selection of procedures for research depends to large extent on the subject in which the research is to be conducted and the objective of the research. The research conducted must be descriptive and involving a sampling survey or it might involve controlled experiment e.tc

80. Important steps to be taken Definition of the problem Review relevant literatures Setting the objectives of the experiment Specify the population Evaluate the feasibility of testing the hypothesis. Selection of treatments

81. 7.Design an experiment 8.Conduct experiment 9.Analysis of data 10.Interpretation of results 11.Reporting

82. 1.Definition of the problem This involves precise problem identification and formulation of problem statement. It is important to develop enough information related to a particular problem in order to define it correctly and also try to develop appropriate solution through experimentation if necessary. Problem statement is a concise descriptive and balanced statement which portrays the issue to be investigated.

83. 2. Review relevant literatures It involves to learn what has been done by other researcher in the field and to become familiar enough with the field of interest allow to you to discuss it with others. The best ideas often cross disciplines and species, so a broad approach is important. For example, recent research in controlling odors in swine waste has exciting implications for fly and nematode control.

84. 3.Setting the objectives of the experiment The objective of the experiment state what will be achieved. This may be inform of question to be answered, hypothesis to be answered, or the effects to be estimated.

85. Important points to take in consideration when setting objectives The objective(s) must be clear , concise i.e. clearly understandable, short, direct to the point, and easy to understand by stakeholders. The objective(s) must be specific , must be related exactly to the problem/solution to be developed. If objective are not specific they become hard to attain. Too ambitious objectives must be based on technical, financial, and time at disposal.

86. Avoid too many objectives for one experiment. With too many objectives, designing, conducting, data collection and analysis and even coming with conclusion becomes very complicated. When there many objectives list them in order of importance and work with few first and then the others later In writing the objective look at the problem and reformulate it as positive statement.

87. 4.Specify the population /study area Specify the population on which research is to be conducted. For example, specify whether you are going to determine the N requirements of common beans on the KARUCO Station or the N requirements of common beans throughout the Region, or the P requirements of papaya in sand or solution culture.

88. 5. Evaluate the feasibility of testing the hypothesis. Researcher should be relatively sure that an experiment can be set up to adequately test the hypotheses with the available resources. Therefore, a list should be made of the costs, materials, personnel, equipment, etc., This ensure that adequate resources are available to carry out the research. If not, necessary modifications will have to be made to design the research to fit the available resource

89. 6.Selection of treatments Is very crucial and can make the difference between success or failure in achieving the objectives. 7.Design an experiment Refer to specific manner in which the experiment will be i ) set up ii) conducted and iii) the plans for data collection and analysis

90. Importance of proper designing of experiments i . ) Relevant comparison of the selected treatments can be achieved ii.) Experimental units receiving different treatments do not differ in any systematic way. Thus reduce experimental error iii.) The conclusion will have wide range of validity if experiment will be well designed ( it will sound reasonable)

91. NB The size of experiment must be suitable i.e. not too small or large experiment. Large experiments are costly. A proper statistical analysis of results should be possible without artificial assumptions.

92. 8.Conduct/Install experiment At this point the experiment is laid out using relevant experimental design. Care should be taken in measuring treatment materials(fertilizers, herbicides, or other chemicals, food rations, etc.) and the application of treatments to the experimental units. The experimental site should be frequently visited and take note/action on everything.

93. 9.Data collection Careful measurements of experimental variables should be made with the appropriate instruments. It is better to collect too much data than not enough. Data should also be recorded properly in a permanent notebook. Data are collected and recorded in field notebook, or forms before input in computer for analysis or manual analysis of data. Always put a duplicate of data collected in case same forms get destroyed

94. 9.Analysis of data Data are analyzed manually or using appropriate statistical tool or software packages, include MSTATC, SPSS * ,Excel, GenStat *** etc. 10.Interpretation of results Interpretation means give meaning of results in light of experimental conditions. Hypothesis tested and in relation to the facts previously established by other researchers

95. 11.Reporting Finally, prepare a complete, correct, and readable report of the experiment. Involves preparation using a given format for reporting the results and write report of the results This may be a report to the farmers or researchers or an extension publication.

96. 5.EXPERIMENTAL DESIGNS Experimental design refers to the framework or structure of an experiment . There are three basic principles of experimental designs: (1) the Principle of Replication; (2) the Principle of Randomization; and (3) Principle of Local Control. Already introduced but let us discuss them again

97. Three basic principles of experimental designs: 1.Principle of Replication Randomization Is a procedure of assigning treatment to experimental unit without bias or subjectivity. Process of allocation of different treatments such that, treatment has an equal chance of being applied in each plot/unit and each plot has an equal chance of receiving each treatment. All the treatment are allocated in in the experimental unit at random to avoid any types of personal bias or due to unforeseen patterns of variation amongst the units.

98. Purposes of randomization. To ensure that there is association between treatments and any characteristics of units i.e. randomization gives each treatment chance of being to any unit. To ensure validity of results because randomization is guard against environmental factors that may invalidate the conclusion of study It helps to have an objective comparison among treatments

99. 2. Principle of replication This is the procedure whereby treatment is applied to more than one experimental unit so that treatments can be compared using the natural variability from one unit to another In the same way that at least two plots of the same variety are needed to determine the differences among plot treated alike. Experimental error can be measured if there at least two plots planted to the same variety (or receiving the same treatment) thus to obtain the measure of experimental error replication is needed.

100. Factors that determine the number of replication The experimental design to be used: with CRD you can have more replication than other designs. The inherent variability of the experimental material: the more the variability the more replication or blocks will be required. The degree of precision required: when the degree of precision for the factors being tested is different split plot design is best. Resources and expertise available: the more the number of replication, the more the plots and the more inputs and financial needed.

101. Importance of replication technique in experiment Replication are necessary for valid estimate or error from the experiment and hence results can be interpreted correctly. The more the replications the more information collected. Even when some of observation are missing the analysis can be carried without much difficult

102. 3. Principle of local control( blocking) Dividing the field into several homogenous parts is known as ‘blocking’. In general, blocks are the levels at which we hold an extraneous factor fixed, Example in diet trial pigs of the same age may be grouped in a litter (a house) to eliminate age effect. So age is known source of variation among this experiment units (pig) In a fertilizer trial, experimental units can be grouped (blocking) and placed in area with the same level of the same fertility.

103. When and how to use blocking technique When productivity pattern of the experiment field is known, orient the block so that soil differences between blocks are maximized and those wit blocks are minimized. Example, for the field with unidirectional fertility gradient along the length of the field blocking should be made across or perpendicular to the gradient.

104. Important Experimental Designs We can classify experimental designs into two broad categories, these are informal experimental designs and formal experimental designs. Informal experimental designs are those designs that normally use a less sophisticated form of analysis based on differences in magnitudes, whereas formal experimental designs offer relatively more control and use precise statistical procedures for analysis

105. Important experiment designs are as follows: ( a) Informal experimental designs: ( i ) Before-and-after without control design. (ii) After-only with control design. (iii) Before-and-after with control design. ( b) Formal experimental designs: *** ( i ) Completely randomized design (C.R. D). (ii) Randomized complete block design (R.C.B.D ). (iii) Split plot (iv) Factorial designs. ( v) Latin square design (L.S. Design).

106. Completely randomized design (C.R. D): Involves only two principles, which are the principle of replication and the principle of randomization of experimental designs. It is the simplest possible design and its procedure of analysis is also easier. The essential characteristic of the design is that treatments are randomly assigned to experimental units .

107. One-way analysis of variance (or one-way ANOVA) is used to analyze such a design. Even unequal replications can also work in this design. It provides maximum number of degrees of freedom to the error. Such a design is generally used when experimental areas happen to be homogeneous.

108. Steps for layout and randomization CRD 1. Determine the total number of experimental plots (n) as the of treatments (t) and the number of replications (r); that is , n = (r)(t). For example, n = (3)(4)=12 2. Assign a plot number to each experimental plot in any convenient manner; for example, consecutively from 1 to n. For our example, the plot number 1,..., 12are assigned to the 12 experimental plots. 3 . Assign the treatments to the experimental plots by any of the randomization schemes: table of random numbers, scientific calculator**, drawing cards

109. Advantages of CRD The design is suitable for experiments with homogeneous experimental units like laboratory experiment where the environment effects are easy to control. Randomization in CRD is done without any restrictions i.e. it’s not necessary all treatments to appear in one replication e.g. two treatments for example D and D has appeared in one replication.

110. The design is flexible in such that, the number of treatments and replication is limited by the number of experimental unit available. Effect of loss of information due to missing data is small relative to losses with other designs.

111. Disadvantage of CRD The design is seldom used in field experiments because generally the land is not homogenous.

112. Randomized Complete block design (R.C.B. D) Is an improvement over the C.R.D design. In the R.C.B.D the principle of local control can be applied along with the other two principles of experimental designs The primary distinguishing features of RCB design is the presence of blocks of equal size each contains all treatments. Complete set of treatments are randomized within each block(replication). This aims at keeping variability within blocks

113. The main feature of the R.C.B. design is that Each treatment appear in every block hence the name COMPLETE BLOCK The R.C.B.Design is analyzed by the two-way analysis of variance (two-way ANOVA)* technique.

114. Layout and randomization The randomization process for a RCB design is applied separately and independently to each of the blocks. The following steps can be adopted 1.Divide the experiment area into equal blocks/replicate where (r) is the number of replications desired following the blocking techniques.

115. 2.Subdivide the first block into ( t) experimental plots, where (t) is the number of treatments. 3.Number the plots consecutively from 1… t, 4. Assign treatments at random to the t plots following any of the randomization schemes for the CRD 5.Repeat step 2 and step 3 to complete each of the remaining blocks/replicate.

116. Advantages of RCBD The design is efficient because all principles of designing are used It is very appropriate for field experiment which lack homogeneity There is no chance plots with the same type of treatments appear in adjacent plots as in CRD.

117. Disadvantage of RCBD If the blocks are not homogenous as expected we have large experimental error. Missing observation must be estimated before carrying out the analysis of observation from whole blocks has to be dropped.  

118. Split-plot design The split-plot design is specifically suited for a two-factor experiment that has more treatments than can be accommodated by a complete block design. In the 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 divided into subplots to which the second factor, called the subplot factor , is assigned.

119. Thus, each main plot becomes a block for the subplot treatments (i.e., the levels of the subplot factor). With a split-plot design, the precision for the measurement of the effects of the main-plot factor is sacrificed to improve that of the subplot factor. Measurement of the main effect of the subplot factor and its interaction with the main-plot factor is more precise than that obtainable with a randomized complete block design.

120. On the other hand, the measurement of the effects of the main-plot treatments (i.e., the levels of the main-plot factor) is less precise than that obtainable with a randomized complete block design. In a split-plot design, both the procedure for randomization and that for analysis of variance are accomplished in two stages-one on the main-plot level and another on the subplot level

121. Factorial experiment An experiment in which the treatments consist of all possible combinations of the selected levels in two or more factors is referred to as a factorial experiment. For example, an experiment involving two factors, each at two levels, such as two varieties and two nitrogen rates, is referred to as a 2 X 2 or a 2 2 factorial experiment. Its treatments consist of the following four possible combinations of the two levels in each of the two factors

122. TREATMENT NUMBER TREATMENT COMBINATIONS VARIETY N rate ( kg/ha) 1 X 2 X 60 3 Y 4 Y 60

123. If the 2 2 factorial experiment is expanded to include a third factor, say weed control at two levels, the experiment becomes a 2 x 2 x 2 or a 2 3 factorial The term complete factorial experiment is sometimes used when the treatments include all combinations of the selected levels of the variable factors. In contrast, the term incomplete factorial experiment is used when only a fraction of all the combinations is tested.

124. 6.ANALYSIS OF VARIENCE FOR CRD 1. Analysis of variance for CRD There are two sources of variation among the n observations obtained from a CRD trial. One is the treatment variation, and the other is experimental error. The treatment difference is said to be real if treatment variation is sufficiently larger than experimental error

125. 2. Analysis of variance for RCBD There are three sources of variation among the n observations obtained from a RCBD trial. Detail on how to do displayed well on the blackboard during class period

126. Comparing with tabulated F. value Obtain tabulated F value from Appendix E. Set the f 1 =treatment d.f ( t-1) and f 2 = error d.f ( t(r-1) For our example f 1 = 4 (read from horizontal in bale of tabulated F value) and f 2 = 8 ( read from vertical). Now if your check from table of tabulated F value we get 3.84 at 5% and 7.01 at 1%

127. 1.If the computed F value is larger than tabular F value at the 1% the level of significance, the treatment difference is said to be highly significant. Such a result is generally indicated by placing two asterisks (**) on the computed F value in the analysis of variance. 2.If the computed F value is larger than the tabular F value at the 5% level of significance but smaller than or equal to the tabular F value the 1% level of significance, the treatment difference is said to be significant

128. Such a result is indicated by placing one asterisk (*) on the computed F value in the analysis of variance. 3.If the computed F value is smaller than or equal to the tabular F value at the 5%level of significance, the treatment difference is said to be non significant. Such a result is indicated by placing ns on the computed F value in the analysis of variance.

129. When 1% used, means chances are less than 1 in 100 that all the observed differences among the seven treatment means could be due to chance. Also when 5% used means chances are less than 5 in 100

130. Coefficient of variation The cv indicates the degree of precision with which the treatments are compared and is a good index of the reliability of the experiment. It expresses the experimental error as percentage of the mean; thus, the higher the cv value, the lower is the reliability of the experiment. The cv varies greatly with the type of experiment, the crop grown, and the character measured

131. comparing treatments differences The two most commonly used test procedures for pair comparisons in agricultural research are the least significant difference (LSD) test which is suited for a planned pair comparison, And Duncan's multiple range test (DMRT) which is applicable to an unplanned pair comparison .

132. least significant difference (LSD) two treatments are declared significantly different at a prescribed level of significance if their difference exceeds the computed LSD value; The procedure for applying the LSD test to compare any two treatments I. Compute the mean difference between t1 and t2 treatment as: d= t1-t2

133. 2.Compute the LSD value at a level of significance as: LSD ˠ = (t n, ˠ )( sd ) where sd is the standard error of the mean difference and ta is the tabular t value, from Appendix C, at a level of significance and with n = error degree of freedom. sd = √ 2 S 2 /r r= rep number and S 2 =error MS in ANOVA

134. 3. Compare means difference obtained in step 1 with the LSD value obtained in step 2. if value of means different is greater than LSD value ,treatment are said to be significant Also we can apply the use of level of significance ( 5% or 1%)

135. SCIENTIFIC/TECHNICAL REPORT WRITING Title of the article Authors and their affiliated institutions Abstract Introduction( incorporating literature review) Material and methods( methodology) Results Discussions Conclusions Recommendations References or literature cited

136. 1.Title of the article -briefly identify the subject and indicates the purpose of the study 2. Author and their affiliated institutes In case of co authorship, names should be written in proper order. The first name should be principal/senior author 3. Abstract -it should have fair amount of details regarding the nature, objectives, methodology, main results obtained and major conclusion reached

137. 4. Introduction -it tell why the study is important and what exactly the study is about. Introduction ends up with a statement of study specific hypothesis or hypothesis. 5.Materials and methods( methodology) -it explain materials and methods used to control the experiment with their detailed procedures. It should be written in paragraph with little repetition as possible

138. 6. Results -the section describe the results, it give factual account of the findings 7.Discusion -here references to other researchers did similar work is made. 8.Conclusion - States major inferences that can be drawn from the discussion

139. 9. Recommendations -indicates any further work that needs to be done. Identifies the alternative you think best solves or improve the problem 10.References or list of literature cited -some books , article or other reading materials consulted during research progress 11. Appendices -additional information that support but not essential to explanations

140. FARMING SYSTEMS APPROACH Concept Farming system is an integrated set of activities that farmers perform in their farms under their resources and circumstances to maximize the productivity and net farm income on a sustainable basis. The farming system takes into account the components of soil, water, crops, livestock, labour , capital, energy and other resources, with the farm family at the centre managing agriculture and related activities.

141. Farming System is defined as a complex inter related matrix of soil, plants, animals, implements, power, labour capital and other inputs controlled in part by farming families and influenced to varying degrees by political, economic, institutional and social forces that operate at many levels. The farming system therefore, refers to the farm as an entity of inter dependent farming enterprises carried out on the farm”.

142. Need for Farming System Approach cost of farm inputs, fluctuation in the market price of farm produce, risk in crop harvest due to climatic vagaries and biotic factors. Environmental degradation, depletion in soil fertility & productivity, unstable income of the farmer, fragmentation of holdings and low standard of living add to the intensity of the problem.

143. Why Farming Systems Approach To develop farm – house hold systems and rural communities on a sustainable basis To improve efficiency in farm production To raise farm and family income To increase welfare of farm families and satisfy basic needs.

144. Farming Systems approach In view of serious limitations on horizontal expansion of land and agriculture, only alternative left is for vertical expansion through various farm enterprises required less space and time but giving high productivity and ensuring periodic income This helps small and marginal farmers located in rain fed areas, dry lands, arid zone, hilly areas, tribal belts and problem soils.

145. The following farm enterprise could be combined Agriculture alone with different crop combinations Agriculture + Livestock Agriculture + Livestock + poultry Agriculture +Horticulture + Agro-forestry + pasture Agriculture (Rice) + Fish culture Agriculture (Rice) + Fish + Mushroom cultivation Floriculture + Apiary (beekeeping) Fishery + Duckery + poultry

146. Farming systems research (FSR) Is an approach in which there is close cooperation between agricultural technicians and social scientists towards improved technologies through research. The FSR approach evolved because of an increased awareness on the part of researchers that such farmers: Had a right to be involved in the technology development process, because they stood to gain or lose most from adoption of the technology. Could effectively contribute to the development of appropriate improved technologies

147. FSR intend to consider farmers Are rational (i.e., sensible) in the methods they use Are natural experimenters Understand the environment in which they operate rather complex farming systems, consisting of crops, livestock, and off-farm enterprises

148. the fundamental principle of FSR was that farmers could help in identifying the appropriate path to agricultural development Farmers participate at all stages relates in one way or the other to the selection, design, testing, and adoption of appropriate technologies. SO……..FSR approach evolved primarily as a result of a lack of success in developing relevant Improved technologies

149. farming systems research (FSR) methodology The farming systems approach to development (FSD) has two inter-related thrusts. One is to develop an understanding of the farm-household, the environment in which it operates, and the constraints it faces, together with identifying and testing potential solutions to those constraints, The second thrust involves the dissemination of the most promising solutions to other farm households facing similar problems

150. list of the steps involved in FSR should include Selection of target areas and sub-areas. Selection of research areas. Selection of cooperators. Description and diagnostic-stage Design-stage activities. Testing and implementation stage Dissemination and impact evaluation stage

151. i ).Selection of target areas and sub-areas Generally, the selection of a target area for a FSR programme is made by national decision makers, usually in Ministries of Agriculture or their equivalent, is completed before FSR team members have been assembled to begin organizing FSR work. To meet the needs of the people living there and/or To take advantage of the agricultural potential of the area

152. ii). Selection of research areas. After the target area has been chosen, a research area or areas within the target area will be identified. This selection is usually made by members of the FSR team as one of their first activities in the field, factors to consider Representativeness of the Research Area, Accessibility, Cooperation of Farmer Contact Agencies and Leader Support etc

153. iii). Selection of cooperators in identifying individuals to be interviewed or individual fields for experimental purposes is the selection of participating farmers. This selection is made by FSR team members but often may be improved by consulting with local authorities, agents, etc Participating farmers need to be selected at the beginning of every season or at the start of any new research initiative. The cooperating unit may be a dwelling unit, a farming household, or specific members within a household

154. iv) The Descriptive or Diagnostic Stage, Actual farming system is examined in the context of the total environment to identify constraints farmers face and to determine the potential flexibility in the farming system in terms of timing, unused resources, etc. The aim is to identify the constraints limiting farm productivity and production and hindering improvement in the welfare of the farm households themselves .

155. An effort also is made to understand the goals and motivation of farmers that may affect their efforts to improve the farming system Potential solutions to these problems are identified, and the results of this analysis formulated as suggestions for further action then are passed on to the relevant 'actors'. These could include researchers, extension and support service staff, or policy makers.

156. v) The Design Stage, in which a range of strategies/approaches are identified that are thought to be relevant in dealing with the constraints determined in the descriptive or diagnostic stage. , the process involves the development of ideas and little field work. visiting scientists and cooperating colleagues from other agencies often have contributed ideas to FSR to be better Trials and Surveys to quantify farmers' attitudes or preferences are needed to help in prioritizing what should be tested

157. vi ) The Testing and Implementation Stage, in which one or more promising strategies/ approaches arising from the design stage, are examined and evaluated under term conditions to determine their suitability for producing desirable and acceptable changes in the existing farming system.

158. vii) The Dissemination and Impact Evaluation Stage, in which the strategies that were identified and screened during the design and testing stages are extended to farmers. In terms of activities at this stage, impact/adoption studies can be very important

159. TESTING HYPOTHESIS Introduction Often in real life we take observations on a sample with a specific question in mind. Hypothesis testing is another way of data analysis. It begins with some theory, claim,or assertion about a particular parameter of a population. The theories or claims we have in our mind are what we call hypothesis

160. view to choose between two conflicting hypotheses about the value of a population parameter. Hypothesis testing helps to decide on the basis of a sample data, whether a hypothesis about the population is likely to be true or false

161. Hypothesis simply means a mere assumption or some supposition to be proved or disproved OR prediction or a tentative answers to be tested by scientific methods, logical supposition, a reasonable guess which may give direction to thinking with respect to the problem and, thus aid in solving it.

162. If we are to compare method A with method B about its superiority and if we proceed on the assumption that both methods are equally good, then this assumption is termed as the null hypothesis. As against this, we may think that the method A is superior or the method B is inferior, we are then stating what is termed as alternative hypothesis The null hypothesis is generally symbolized as H0 and the alternative hypothesis as Ha.

163. example Researcher can suppose that, the average waiting time for donkey to give birth is 9 months H O : µ= 9months H a : µ ‡ 9months or (H a : µ ˂ 9months or H a : µ ˃9months)

164. A formal procedure for deciding between H0 and Ha is called a hypothesis test or test of significance. Hypothesis can be tested using various test Statistics. The important test Statistics are: (1) z-test; (2) t-test; (3) X 2 -test, and (4) F-test. The calculated test statistic value (under a specified level of significance ) is then compared with the tabulated test statistic in order to reject or accept the null hypothesis.

165. Important test Statistics X 2 -test is based on chi-square distribution and as a parametric test is used for comparing a sample variance to a theoretical population variance. F-test is based on F-distribution and is used to compare the variance of the two-independent samples. It used for analysis of variance z-test is based on the normal probability distribution and is used for judging the significance of several statistical measures, particularly the mean.

166. t-test i s based on t-distribution and is considered an appropriate test for judging the significance of a sample mean or for judging the significance of difference between the means of two samples in case of small sample(s) when population variance is not known (in which case we use variance of the sample as an estimate of the population variance).

167. Alternative hypothesis is usually the one which someone wishes to prove and the null hypothesis is the one which someone wishes to disprove. Thus, a null hypothesis represents the hypothesis we are trying to reject , and alternative hypothesis represents all other possibilities.

168. 1.The level of significance This is very important concept in the context of hypothesis testing. It is always some percentage (usually 5% or 1%) which should be chosen with great care, thought and reason. In other words, the 5 per cent level of significance means that researcher is willing to take as much as a 5% risk of rejecting the null hypothesis when it happens to be true. Like wise to 1%

169. Thus the significance level is the maximum value of the probability of rejecting ( H o ) when it is true and is usually determined in advance before testing the hypothesis.

170. 2.Decision rule or test of hypothesis: Given a hypothesis H and an alternative hypothesis Ha, we make a rule which is known as decision rule according to which we accept H0 (i.e., reject Ha) or reject H0 (i.e., accept Ha).

171. 3.Type I and Type II errors: In the context of testing of hypotheses, there are basically two types of errors we can make. We may reject H0 when H0 is true and we may accept H0 when in fact H0 is not true. The former is known as Type I error and the latter as Type II error. In other words, Type I error means rejection of hypothesis which should have been accepted and Type II error means accepting the hypothesis which should have been rejected.

172. Type I error occurs when the null hypothesis (H0) is rejected when in fact it is true and should not be rejected. On the other hand, a Type II error occurs when the null hypothesis H0 is not rejected when in fact it is false and should be rejected. Type I error is denoted by a (alpha) known as a error, also called the level of significance of test; and Type II error is denoted by b (beta) known as b error.

173. In a tabular form the said two errors can be presented as follows ACCEPT HO REJECTING HO HO True Correct decision Type I error HO false Type II error Correct decision Decision rule

174. In order to make a choice of whether to reject or accept the null hypothesis we need based on sample information, compute the value of a test statistic, which will tell us what action to take. A test statistic is a function of the sample information that is used as a basis for deciding between H0 and H1. For example, . Z= x-µ/ σ / a test statistic.

175. In testing hypothesis we partition the possible values of the test statistic into two subsets: an acceptance region for H0 and a rejection region for Ha

176. Two tail and one tail Hypotheses: General form: H0: μ = μ0 against One-sided: H1: μ > μ0 or H1: μ < μ0 Two-sided: H1: μ ≠ μ0

177. Two tail A two-tailed test rejects the null hypothesis if, say , the sample mean is significantly higher or lower than the hypothesized value of the mean of the population. Such a test is appropriate when the null hypothesis is some specified value and the alternative hypothesis is a value not equal to the specified value of the null hypothesis

178. A two-tailed test , also known as a non directional hypothesis , is the standard test of significance to determine if there is a relationship between variables in either direction. Two-tailed tests do this by dividing the 0.05 in two and putting half on each side of the bell curve.

179. A one-tailed test A one-tailed test would be used when we are to test, say, whether the population mean is either lower than or higher than some hypothesized value . For instance, if our H0: m = m and Ha H : m < m 0 then we are interested in what is known as left-tailed test (wherein there is one rejection region only on the left tail)

180. One-Tailed Test Also we can have right sided on tail or left sided .The level of significant is not divided A one-tailed test , also known as a directional hypothesis , is a test of significance to determine if there is a relationship between the variables in one direction. A one-tailed test is useful if you have a good idea, usually based on your knowledge of the subject, that there is going to be a directional difference between the variables.

181. Often in real life we take observations on a sample with a specific question in mind. Hypothesis testing is another way of data analysis. It begins with some theory, claim, or assertion about a particular parameter of a population.

182. PROCEDURE FOR HYPOTHESIS TESTING ( i ) Making a formal statement: The step consists in making a formal statement of the null hypothesis (H0) and also of the alternative hypothesis (Ha). This means that hypotheses should be clearly stated, considering the nature of the research problem.

183. (ii) Selecting a significance level: The hypotheses are tested on a pre-determined level of significance and as such the same should be specified. Generally, in practice, either 5% level or 1% level is adopted for the purpose

184. The factors that affect the level of significance are: the magnitude of the difference between sample means; (b) the size of the samples; (c) the variability of measurements within samples;

185. (iii) Deciding the distribution to use: After deciding the level of significance, the next step in hypothesis testing is to determine the appropriate sampling distribution. The choice generally remains between normal distribution and the t-distribution.

186. (iv) Selecting a random sample and computing an appropriate value: Another step is to select a random sample(s) and compute an appropriate value from the sample data concerning the test statistic utilizing the relevant distribution. In other words, draw a sample to furnish empirical data.

187. (v) Calculation of the probability: One has then to calculate the probability that the sample result would diverge as widely as it has from expectations, It involves to know if the null hypothesis were in fact true.

188. (vi) Comparing the probability: Yet another step consists in comparing the probability thu s calculated with the specified α value , the significance level. If the calculated probability is equal to or smaller than the α value in case of one-tailed test (and a α / 2 in case of two-tailed test), then reject the null hypothesis (i.e. accept the alternative hypothesis

189. but if the calculated probability is greater , then accept the null hypothesis . In case we reject H0, when it is true committing Type I error, but if we accept H0, when it is false we committing Type II error .