{"id":93345,"date":"2025-02-17T09:31:24","date_gmt":"2025-02-17T09:31:24","guid":{"rendered":"https:\/\/peraltafinancing.com\/agriculture\/theory-example-and-demonstration-in-agri-analyze-tool\/"},"modified":"2025-02-17T09:31:24","modified_gmt":"2025-02-17T09:31:24","slug":"theory-example-and-demonstration-in-agri-analyze-tool","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=93345","title":{"rendered":"Theory, Example and Demonstration in Agri Analyze tool"},"content":{"rendered":"


\n<\/p>\n

\n

\u00a0This blog explains RCBD design in detail, guides step by step to perform analysis and demonstrates its analysis online using Agri Analyze tool. Link of the MCQ is shared in bottom! (Reading time 15 min)<\/i><\/p>\n

Introduction<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>Experimental
\ndesign is a systematic approach in scientific research, essential for
\ninvestigating relationships among variables. It ensures valid and interpretable
\nresults through randomization, replication and control. Randomization
\ndistributes extraneous variables evenly, reducing bias. Replication increases
\nreliability and precision by accounting for variability within experimental
\nunits. Control ensures that observed differences are due to the independent
\nvariable. Designs range from simple completely randomized designs to complex
\nones like randomized complete block designs, factorial designs and Latin
\nsquares. These designs help isolate variable effects and understand their
\ninteractions. Effective experimental design is crucial for drawing valid
\nconclusions and advancing scientific knowledge across various fields.<\/span><\/p>\n

Randomized Complete Block Design (RCBD)<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>Randomized
\nComplete Block Design (RCBD) is a fundamental experimental design used
\nextensively in scientific research to control for variability within
\nexperimental units. In RCBD, each block contains all treatments, with random
\nassignment within blocks, controlling for variability and ensuring
\ncomprehensive treatment comparison. Hence, it is called “Randomized
\nComplete Block Design.” This design reduces experimental error and
\nenhances the precision of treatment comparisons by accounting for
\nblock-to-block variability. RCBD is particularly useful in experiments with
\nknown or suspected gradients in conditions, such as soil fertility in
\nagricultural studies. It is essential for drawing valid inferences about
\ntreatment effects while minimizing the influence of extraneous factors.<\/span><\/p>\n

When RCBD is used?<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/b>The
\nRCBD is employed in agricultural research under specific conditions to achieve
\nreliable and precise results. Here are scenarios when RCBD is used:<\/span><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Heterogeneous
\nExperimental Units:<\/span><\/b> When there is
\nsignificant variability within the experimental field, such as differences in
\nsoil fertility, moisture, or topography, RCBD helps control this variability by
\ngrouping similar units into blocks.<\/span><\/p>\n

2.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Known
\nGradients:<\/span><\/b> When there are known gradients in the
\nexperimental area (e.g., fertility gradients across a field), RCBD is used to
\nensure that each treatment is tested across all levels of the gradient,
\nreducing the impact of these gradients on treatment comparisons.<\/span><\/p>\n

3.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Multiple
\nTreatments:<\/span><\/b> When comparing multiple treatments
\n(e.g., different crop varieties, fertilizers, or pest control methods), RCBD
\nensures that each treatment is equally represented within each block, allowing
\nfor accurate comparison.<\/span><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Limited
\nExperimental Units:<\/span><\/b> In cases where the
\nnumber of experimental units is limited, RCBD maximizes the use of available
\nunits by reducing experimental error, thus enhancing the precision of the
\nresults.<\/span><\/p>\n

2.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Small-Scale
\nTrials:<\/span><\/b> For small-scale trials where variability
\nwithin the experimental area can significantly impact results, RCBD provides a
\nrobust method to control for this variability.<\/span><\/p>\n

Assumptions of RCBD<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>The
\nRCBD operates under several key assumptions to ensure valid and reliable
\nresults:<\/span><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Homogeneity
\nwithin Blocks:<\/span><\/b> The experimental units within each
\nblock are assumed to be homogeneous, meaning they are similar in terms of
\ncharacteristics that could affect the response variable (e.g., soil fertility,
\nmoisture levels).<\/span><\/p>\n

2.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Independence
\nof Observations:<\/span><\/b> Observations from different
\nexperimental units are assumed to be independent of each other. The response of
\none unit does not influence the response of another.<\/span><\/p>\n

3.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Additivity
\nof Effects<\/span><\/b>: The effects of blocks and treatments are
\nadditive, meaning there are no interactions between blocks and treatments.<\/span><\/p>\n

4.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Random
\nAssignment:<\/span><\/b> Treatments are randomly assigned to
\nexperimental units within each block to ensure unbiased estimates of treatment
\neffects.<\/span><\/p>\n

5.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Normality:
\n<\/span><\/b>The response variable for each treatment
\nis assumed to be normally distributed within each block.<\/span><\/p>\n

6.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Equal
\nVariance:<\/span><\/b> The variance of the response variable is
\nassumed to be the same for all treatments within each block.<\/span><\/p>\n

7.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>No
\nMissing Data:<\/span><\/b> It is assumed that there are no
\nmissing data points. Each treatment is represented in every block.<\/span><\/p>\n

<\/p>\n

<\/a><\/div>\n

Randomization steps in RCBD<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>Randomization
\nin a Randomized Complete Block Design (RCBD) is a crucial step to ensure
\nunbiased allocation of treatments to experimental units within each block. Here
\nare the detailed steps for randomization in RCBD:<\/span><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Identify the Treatments<\/span><\/b>: List all the treatments to be tested in the experiment. Let’s assume
\nthere are t treatments<\/span><\/p>\n

2.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Define the Blocks<\/span><\/b>: Identify and define the blocks based on homogeneous characteristics.
\nEach block will contain all the treatments. Let’s assume there are r blocks
\n(replications).<\/span><\/p>\n

3.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Assign Treatments Randomly within Each Block:<\/span><\/b> <\/span>To randomly
\nassign treatments within each block in an RCBD, list all treatments and use a
\nrandomization method such as random number tables, computer software, or
\ndrawing lots. Document the random allocation for each block to ensure clear and
\nunbiased treatment distribution across the experimental units.<\/span><\/p>\n

4.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Record the Assignment: <\/span><\/b>Document the random allocation of treatments for each block to ensure
\nthe layout plan is clear and can be followed accurately during the experiment.<\/span><\/p>\n

5.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Repeat for All Blocks:<\/span><\/b> Repeat the randomization process for each block until all treatments
\nhave been randomly assigned to plots within every block.<\/span><\/p>\n

6.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Verify Randomization<\/span><\/b>: Ensure that each treatment appears once in every block and that the
\nallocation is indeed random. This can be done by checking the documentation or
\nusing software outputs.<\/span><\/p>\n

7.\u00a0\u00a0\u00a0\u00a0
\n<\/span><\/span><\/span>Create a Layout Plan:<\/span><\/b> Develop a visual representation or map of the experimental layout
\nshowing the randomized assignment of treatments within each block.<\/span><\/p>\n

<\/p>\n

<\/a><\/div>\n

<\/p>\n

<\/a><\/div>\n

Example of RCBD<\/span><\/b><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span>Ten
\nwheat varieties were put under yield trial against local in randomizes block
\ndesign with four replications at the Vijapur farm. Observed yield data is given
\nbelow.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Genotypes<\/span><\/b><\/p>\n<\/td>\n

\n

Replications<\/span><\/b><\/p>\n<\/td>\n

\n

Total of genotype<\/span><\/b><\/p>\n<\/td>\n

\n

Mean of genotype<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

R-1<\/span><\/b><\/p>\n<\/td>\n

\n

R-2<\/span><\/b><\/p>\n<\/td>\n

\n

R-3<\/span><\/b><\/p>\n<\/td>\n

\n

R-4<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Haura<\/span><\/b><\/p>\n<\/td>\n

\n

148<\/span><\/p>\n<\/td>\n

\n

132<\/span><\/p>\n<\/td>\n

\n

148<\/span><\/p>\n<\/td>\n

\n

132<\/span><\/p>\n<\/td>\n

\n

560<\/span><\/p>\n<\/td>\n

\n

140<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12<\/span><\/b><\/p>\n<\/td>\n

\n

155<\/span><\/p>\n<\/td>\n

\n

156<\/span><\/p>\n<\/td>\n

\n

157<\/span><\/p>\n<\/td>\n

\n

160<\/span><\/p>\n<\/td>\n

\n

628<\/span><\/p>\n<\/td>\n

\n

157<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-65-4<\/span><\/b><\/p>\n<\/td>\n

\n

112<\/span><\/p>\n<\/td>\n

\n

136<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n

\n

150<\/span><\/p>\n<\/td>\n

\n

524<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-6<\/span><\/b><\/p>\n<\/td>\n

\n

112<\/span><\/p>\n<\/td>\n

\n

114<\/span><\/p>\n<\/td>\n

\n

100<\/span><\/p>\n<\/td>\n

\n

118<\/span><\/p>\n<\/td>\n

\n

444<\/span><\/p>\n<\/td>\n

\n

111<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12-5-3<\/span><\/b><\/p>\n<\/td>\n

\n

124<\/span><\/p>\n<\/td>\n

\n

125<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n

\n

125<\/span><\/p>\n<\/td>\n

\n

500<\/span><\/p>\n<\/td>\n

\n

125<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-5-7-2<\/span><\/b><\/p>\n<\/td>\n

\n

92<\/span><\/p>\n<\/td>\n

\n

94<\/span><\/p>\n<\/td>\n

\n

98<\/span><\/p>\n<\/td>\n

\n

96<\/span><\/p>\n<\/td>\n

\n

380<\/span><\/p>\n<\/td>\n

\n

95<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-8<\/span><\/b><\/p>\n<\/td>\n

\n

116<\/span><\/p>\n<\/td>\n

\n

124<\/span><\/p>\n<\/td>\n

\n

130<\/span><\/p>\n<\/td>\n

\n

134<\/span><\/p>\n<\/td>\n

\n

504<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Kalyan
\n sona<\/span><\/b><\/b><\/p>\n<\/td>\n

\n

115<\/span><\/p>\n<\/td>\n

\n

121<\/span><\/p>\n<\/td>\n

\n

122<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n

\n

484<\/span><\/p>\n<\/td>\n

\n

121<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Sonalika<\/span><\/b><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n

\n

132<\/span><\/p>\n<\/td>\n

\n

130<\/span><\/p>\n<\/td>\n

\n

524<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

GW-24<\/span><\/b><\/p>\n<\/td>\n

\n

145<\/span><\/p>\n<\/td>\n

\n

149<\/span><\/p>\n<\/td>\n

\n

150<\/span><\/p>\n<\/td>\n

\n

156<\/span><\/p>\n<\/td>\n

\n

600<\/span><\/p>\n<\/td>\n

\n

150<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Rep total<\/span><\/b><\/p>\n<\/td>\n

\n

1250<\/span><\/p>\n<\/td>\n

\n

1282<\/span><\/p>\n<\/td>\n

\n

1289<\/span><\/p>\n<\/td>\n

\n

1327<\/span><\/p>\n<\/td>\n

\n

5148<\/span><\/p>\n<\/td>\n

\n

<\/p>\n

\u00a0<\/p>\n

<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p>\n

<\/a><\/div>\n

<\/p>\n

<\/a><\/div>\n

<\/p>\n

\n

Conducting
\nLSD test for multiple mean comparison<\/span><\/b><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Arrange
\nvarieties means in descending order, find difference (d) between two
\nconsecutive means and follow procedure given below:<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Varieties<\/span><\/b><\/p>\n<\/td>\n

\n

Mean<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12<\/span><\/p>\n<\/td>\n

\n

157<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

GW-24<\/span><\/p>\n<\/td>\n

\n

150<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Haura<\/span><\/p>\n<\/td>\n

\n

140<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-65-4<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Sonalika<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-8<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12-5-3<\/span><\/p>\n<\/td>\n

\n

125<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Kalyan sona<\/span><\/p>\n<\/td>\n

\n

121<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-6<\/span><\/p>\n<\/td>\n

\n

111<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-5-7-2<\/span><\/p>\n<\/td>\n

\n

95<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

2.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Find
\ndifference between two consecutive means<\/span><\/p>\n

3.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>d=
\n157-150 =7. If d >= CD, then both means are significantly different.<\/span> <\/span>There is no need to find d between HY-12
\nand GW-24. If d <\/span><\/span><\/p>\n

4.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>But
\nd = 157 (HY-12) \u2013 140 (Haura) =17, Here d >= CD, then both means are
\nsignificantly different. <\/span><\/p>\n

5.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>d
\n= 150 (GW-24) \u2013 140 (Haura) =10, Here d >= CD, then both means are
\nsignificantly different.<\/span><\/p>\n

6.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>d
\n= 140 (Haura) \u2013 131 (HY-65-4) = 9, Here d <\/span><\/p>\n

7.\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Follow
\nsame procedure to exhaust all means.<\/span><\/p>\n

8.\u00a0<\/span>\u00a0\u00a0\u00a0 <\/b><\/span><\/span><\/span>Final
\nLSD test for varieties given as under:<\/b><\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Varieties<\/span><\/b><\/p>\n<\/td>\n

\n

Mean<\/span><\/b><\/p>\n<\/td>\n

\n

Group<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12<\/span><\/p>\n<\/td>\n

\n

157<\/span><\/p>\n<\/td>\n

\n

a<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

GW-24<\/span><\/p>\n<\/td>\n

\n

150<\/span><\/p>\n<\/td>\n

\n

a<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Haura<\/span><\/p>\n<\/td>\n

\n

140<\/span><\/p>\n<\/td>\n

\n

b<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-65-4<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n

\n

bc<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Sonalika<\/span><\/p>\n<\/td>\n

\n

131<\/span><\/p>\n<\/td>\n

\n

bc<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-8<\/span><\/p>\n<\/td>\n

\n

126<\/span><\/p>\n<\/td>\n

\n

cd<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-12-5-3<\/span><\/p>\n<\/td>\n

\n

125<\/span><\/p>\n<\/td>\n

\n

cd<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Kalyan sona<\/span><\/p>\n<\/td>\n

\n

121<\/span><\/p>\n<\/td>\n

\n

d<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-11-6<\/span><\/p>\n<\/td>\n

\n

111<\/span><\/p>\n<\/td>\n

\n

e<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

HY-5-7-2<\/span><\/p>\n<\/td>\n

\n

95<\/span><\/p>\n<\/td>\n

\n

f<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

<\/div>\n

\n

Steps to perform analysis of RCRD in
\nAgri Analyze<\/span><\/b><\/p>\n

Step 1: <\/span><\/b>To create a CSV file with columns for Genotype and
\nYield (Gain).<\/span><\/p>\n

<\/div>\n

<\/a><\/div>\n

<\/p>\n

\n

The link of the entire data set<\/a><\/span><\/b><\/p>\n

Step 2: Go with Agri Analyze site. https:\/\/agrianalyze.com\/Default.aspx<\/span><\/a><\/span><\/p>\n

Step 3: Click on ANALYTICAL TOOL
Step 4: Click on DESIGN OF EXPERIMENT
Step 5: Click on RCRD ANALYSIS
Step 6: Click on ONE FACTOR RCRD ANALYSIS
Step 7: Select CSV file.
Step 8: Select treatment, replication and dependent variable (e.g., Gain).
Step 9: Select a test for multiple comparisons, such as the Least Significant Difference (LSD) test, to determine significant differences among groups. Same as for Duncan\u2019s New Multiple Range Test (DNMRT), Tukey\u2019s HSD Test.<\/div>\n

<\/a><\/div>\n

Step 10: After submit download analysis report<\/p>\n

\u00a0<\/span><\/p>\n

Output File Snip<\/span><\/p>\n

\n
<\/a><\/div>\n


<\/b><\/p>\n

<\/a><\/div>\n

Link of the output file<\/a><\/p>\n

REFERENCES <\/p>\n

Gomez, K. A., & Gomez, A. A. (1984). Statistical Procedures for Agricultural Research. John wiley & sons. 25-30.<\/p>\n<\/div>\n

See Also:<\/b><\/p>\n

The blog is written by\u00a0<\/p>\n

Darshan Kothiya<\/a>\u00a0PhD Scholar, Department of Agricultural Statistic, Anand Agricultural University, Anand<\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

\u00a0This blog explains RCBD design in detail, guides step by step to perform analysis and demonstrates its analysis online using Agri Analyze tool. Link of the MCQ is shared in bottom! (Reading time 15 min) Introduction \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Experimental design is a systematic approach in scientific research, essential for investigating relationships among variables. It ensures valid […]<\/p>\n","protected":false},"author":1,"featured_media":93346,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12026],"tags":[25333,24554,23134,2359,1447],"dealstore":[],"offerexpiration":[],"class_list":["post-93345","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agriculture","tag-agri","tag-analyze","tag-demonstration","tag-theory","tag-tool"],"yoast_head":"\nTheory, Example and Demonstration in Agri Analyze tool - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=93345\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Theory, Example and Demonstration in Agri Analyze tool - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"\u00a0This blog explains RCBD design in detail, guides step by step to perform analysis and demonstrates its analysis online using Agri Analyze tool. 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