Field Research Methods in Agriculture
An Introduction with R
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Field research is one of the key elements to scientific progress in agriculture and applied biology, because it represents the 'bridge' between laboratory experiments in highly controlled conditions and on-farm tests, with purely demonstrative purposes. Like laboratory research, field studies must adhere to scientifically rigorous protocols to yield reliable answers to research questions. At the same time, field research must take place in 'real-life' settings, ensuring that the results are readily applicable to farmers and growers. The necessity of utilizing near-commercial-scale field equipm...
Field research is one of the key elements to scientific progress in agriculture and applied biology, because it represents the 'bridge' between laboratory experiments in highly controlled conditions and on-farm tests, with purely demonstrative purposes. Like laboratory research, field studies must adhere to scientifically rigorous protocols to yield reliable answers to research questions. At the same time, field research must take place in 'real-life' settings, ensuring that the results are readily applicable to farmers and growers. The necessity of utilizing near-commercial-scale field equipment and addressing various sources of random variability—such as fertility gradients, pest invasions, and other unpredictable factors—has led to the development of unique methodologies for both experimental design and data analysis. These approaches are not frequently found collectively in a single concise introductory book that is accessible to students and practitioners in the agricultural and biological sciences, while also containing numerous real-world examples and reproducible code. This book endeavors to address the aforementioned gap by concentrating on small-plot field experiments that evaluate innovative genotypes, agronomic practices, pesticides, and other methods of plant protection. Adopting a 'learn-by-doing' approach, it presents these methods through a selection of relevant examples and case studies, accompanied by a series of hands-on exercises for students. The R statistical software is used as the primary tool for data analysis, and the book includes various reproducible R code snippets. While it does not strive for completeness, this book is finely tailored for a 6 ECTS introductory course in biometry, comprising approximately 54 hours of instruction, targeted toward master's or PhD students in agriculture and biology. Additionally, it will be beneficial for practitioners seeking foundational knowledge before embarking on field research careers in agriculture. No prior knowledge of statistics is required, though a basic understanding of R is assumed; an introductory appendix on this statistical software is provided for complete beginners. The book covers the following topics: (i) the design of experiments and the primary types of layouts for field research, including completely randomized designs, block designs, split-plot, strip-plot, and various other designs; (ii) descriptive statistics such as mean, median, variance, standard deviation, and the Pearson correlation coefficient; (iii) simple cause-effect models and model fitting techniques, encompassing one-way and multi-way ANOVA, simple linear and nonlinear regression, and an introduction to mixed models; (iv) stochastic models including the Gaussian density function and Monte Carlo simulation; (v) an introduction to inference and hypothesis testing; (vi) the evaluation of fitted models; and (vii) linear and nonlinear combinations of model parameters. This book is linked to an R package called 'statforbiology,' which includes a variety of functions and datasets, as well as a dedicated website. Both the package and the website are free to use and are licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. The website is regularly updated with new case studies and periodically revised to reflect corrections and enhancements, ensuring it remains current in light of R's rapid evolution.