The book provides a comprehensive guide on the application of statistical methods in agricultural research, with a focus on using R software for data analysis. It addresses the need for practical, understandable statistical analysis in agriculture, where its core objective is to equip readers with the skill manage and analyse data within the software for various experimental designs, perform basic statistical analysis, interpreting results from diverse types of crop trials - be it simple, factorial, or pooled experimental design and effectively presenting agricultural data. The book is structured into several chapters, each addressing a different aspect of statistical applications in agriculture. It begins with an introduction to fundamental statistical terminology and concepts, highlighting the relevance of statistics in various fields and the rationale behind selecting R for data analysis.

The book then guides readers through the installation of R and RStudio, providing practical advice on data import and workspace setup. It explores into basic statistics, focusing on key data metrics & graph and demonstrates the ease of executing these tasks in R. The chapters progress to cover experimental design, offering insights into principles and the use of R for treatment randomization in diverse experiments. The book also addresses correlation analysis, path analysis in plant breeding research and data transformation techniques, each with hands-on R examples. Advanced topics include a thorough examination of Completely Randomized Design (CRD), Randomized Block Design (RBD) and Latin Square Design (LSD), discussing their theoretical foundations, structure and analysis, including ANOVA interpretations in R. Additionally, it explores Split and Strip Plot Designs and their applications, concluding with a chapter on visualizing output, particularly focusing on multiple comparison tests and their representation in R. This book is structured to provide a sequential understanding of both the theoretical and practical aspects of statistical application in agriculture, making it an indispensable guide for researchers and practitioners in the field.

Dr. Rumit Patel, alumni of Anand Agricultural University in Anand, Gujarat, India, has been making significant contributions as a Research Associate in the Department of Agriculture Biotechnology at Anand Agricultural University, Gujarat, since 2021. Before this he served as a Senior Research Fellow at Cotton research Station, Sardarkrushinagar Dantiwada Agricultural University, S. K. Nagar, Gujarat, India for six months. He is expert in designing and interpreting the plant breeding and molecular breeding research. He is also expert in the high dimensional genome data analysis. He adeptly used the R and MS Excel including various other software like TASSEL, STRUCTURE, QTL cartographer, MapMaker, NTSys-pc DARwin and Power Marker etc. to support his research. He is very much expert in variability analysis for this he developed the R package called “variability”. He is also expert in handling multi-environment trait data and analysis. He is sound at various matting designs like L×T, Diallel mating design, GMA, and etc. His scholarly achievements extend to the publication of research papers in reputable journals, as well as the authorship of several book chapters and review papers that reflect his academic credit. Notably, he actively participates in the peer review process in several reputed journals. Beyond his academic responsibilities, Dr. Patel has served as an active plant breeder at Research Station for Distant Hybridization in Fruit and Field Crops at Department of Agriculture Biotechnology, Anand Agricultural University, Anand.

Dr. Sushil Kumar is an accomplished Assistant Professor in the Department of Agri. Biotechnology at Anand Agricultural University, Gujarat, since 2012. A proud alumnus of Rajasthan Agricultural University, Bikaner, he excels in Plant Molecular Biology and Molecular Breeding. Dr. Kumar has significantly contributed to the development of 12 varieties of various crops. His mentorship encompasses guiding 5 master's and one Ph.D. student. Dr. Sushil Kumar's academic achievements include over 100 publications, featuring in books from international publishers. The citations of his research papers exceed 1900 with h-index of 23, reflects his wide-reaching impact in the field of agricultural biotechnology. Additionally, he has led several externally and in-house projects funded by different agencies.

Dr. Prity Kumari, alumnus of Banaras Hindu University in Varanasi, Uttar Pradesh, India, has been making significant contributions as an Assistant Professor in the College of Horticulture at Anand Agricultural University, Gujarat, since 2015. Her research expertise spans a diverse range of areas, focusing on time series forecasting through statistical models and cutting-edge Deep Learning AI techniques. Dr. Kumari adeptly applies methodologies such as ARIMA, ARCH/GARCH, ANN, ML, Memory based Machine learning models and CNN to advance her research. Her scholarly achievements extend to the publication of research papers in reputable journals, as well as the authorship of several books and book chapters that reflect her academic credit. Notably, she actively mentors and guides numerous master's and Ph.D. students in the domain of agriculture. Beyond her academic responsibilities, Dr. Kumari has served as a visiting fellow at Western Sydney University, further enriching her research insights.

1.  Introduction: Understanding Statistics and R
1.1 Basic Concepts & Terminology
1.2 Function/Purpose of Statistics
1.3 Limitations of statistics
1.4 Why R software

2.   Installation of R: Setting Up R and RStudio
2.1 Install R and RStudio
2.2 How to download any package?
2.3 How to import input data in R?
2.4 Setting and Checking the Working Directory

3.   Basic Statistics: Basic Statistical Measures and Graphs
3.1 Basic statistics
3.2 Basic statistics in a single line code
3.3 Analyzing Data Distribution and Visualizing Results

4.   Experimental design with R: Principles and Applications
4.1 Experimental design
4.2 Principles of Experimental Design
4.3 Randomization for CRD using R
4.4 Randomization for RBD using R
4.5 Randomization for LSD using R
4.6 Randomization for Split plot design (SPD) using R

5.   Correlation:Types and Applications 
5.1 Correlation
5.2 Types of Correlation
5.3 Correlation coefficients & its test
5.4 Correlation Analysis In R
5.5 Visualize correlation matrix
5.6 Different types of correlation plots or correlogram

6.   Path Analysis: Path fundamentals and analysis using R
6.1 Path analysis
6.2 Path Coefficients:
6.3 Genotypic and Phenotypic correlation in R
6.4 Path Analysis in R

7.   Transformation: Techniques and Implementation
7.1 Data transformation
7.2 Methods of Data Transformation
7.3 Data Transformation in R
 
8.   Completely Randomized Design (CRD): Theory, Layout and Analysis
8.1 Completely Randomized Design
8.2 Layout
8.3 Randomization of treatments:
8.4 Factorial Experiment
8.5 Factorial CRD:
8.6 Analysis using R
8.7 CRD: completely randomized design in R
8.8 FCRD: Factorial completely randomized design in R

9.   Randomized Block Design (RBD): Theory, Layout and Analysis
9.1 Randomized Block Design
9.2 Layout of RBD
9.3 Randomization of treatments:
9.4 Factorial RBD
9.5 Pooled ANOVA
9.6 RCBD using R
9.7 ‘Variability’ package for RCBD
9.8 Factorial RCBD using R
9.9 Pooled RBD analysis using R:

10. Latin Square Design (LSD): Theory, Layout and Analysis
10.1   Latin Square Design
10.2   Layout of LSD
10.3   Randomization Procedure for 5 x 5 LSD
10.4   LSD using R

11. Split & Strip Plot Designs: Concept and practical analysis in R software
11.1      Split Plot Design
11.2      Strip Plot Design
11.3      Layout & Randomization in Split Plot Design
11.4      Layout & Randomization in Strip Plot Design
11.5      ANOVA for Split Plot Design
11.6      ANOVA for Strip Plot Design
11.7      Split Plot Design in R
11.8      Strip Plot Design in R

12. Visualization of Output: Concept and practical analysis in R software
12.1      Treatment comparison
12.2      Visualization of test of comparison of treatment