The Fundamentals of Field Trial Management
A field trial in its simplest form is an experiment that evaluates an agricultural practice. Field trials are field-based experiments conducted on growers’ land, providing real-life results, compared to those that are in laboratory settings.
While there are elements of field trials that are able to be manipulated by researchers in terms of the experimental design and management (i.e., irrigation needs, fertilizer applications), there are other factors that are out of their control, like weather events.
Conducting field trials allows researchers to assess the success, or limitations, of a new crop variety, fertilizer rate, cover crop usage, irrigation, timing of planting, pest control and much more.
These assessments are used for regulatory approval as well as the marketability of new products in different geographies and crop conditions.
What are the Components of Field Trial Management?
Field trials can be managed in the following areas: experimental design, data collection, data analysis and the application of results.
Experimental design is how an experiment is organized and carried out so that the results can be interpreted in a statistically meaningful way. The elements of experimental design that are extremely important in field trials are the purpose or question, control and treatment groups, and the trial layout or field design.
Researchers must have a clear purpose, or question, when conducting field trials. This is the first component of field trial management and informs every single element hereafter. Without this, it’s difficult to know how to structure the other aspects of the experiment because the objective is not clear.
Control and Treatment Groups
Within the experiment, there needs to be some sort of standard, or baseline, that allows researchers to evaluate if there is an effect on crop performance. The control group provides this “typical” response to either no treatment or treatment in which the effect is already known. Alternatively, a treatment or experimental group receives the treatment whose effect the researchers are interested in measuring. Both groups are extremely important.
It’s nearly impossible to find a field that is uniform regarding slope or soil characteristics. For that reason, researchers often section off areas, creating blocks, to capture the variability that is present. Within each of these blocks, the experimental treatments are randomized and replicated. This allows for comparisons within and across blocks, reduction in experimental error, and more sound statistical evaluations.
Data collection focuses on not only the processes of actually collecting data but also on the management and storage of data.
Standard operating procedures, or SOPs, are guidelines that can be used to outline how treatments are handled in field trials, how data are collected, and any other tasks that could be interpreted or conducted differently by people. These instructions help minimize errors and create efficiency in field trials.
Data Management and Storage
Using technology for the management and storage of data will help immensely. Data can be entered directly into a system (either automated or manually input) or transferred immediately to cloud-based storage to minimize data loss. Stored data can be assessed for quality and integrity, highlighting for example data points that are not within an “acceptable” range for a given measurement. Legacy data that have been properly stored and harmonized can also be leveraged for cross-trial analysis.
Data analysis typically happens after the conclusion of a field trial but relies on everything leading up to this point to be accurate and successful. Collected data are used to test the research hypothesis using statistical analyses. Relationships between factors can be mapped and quantified, and serve as the basis for more advanced predictive modeling. Data analysis will always be restricted by your computing resources, and you need to consider the experimental design for proper statistical analysis.
Along with the large storage often needed for field trial datasets, a computational system of a similar quality is essential. In order to conduct statistical tests and other analyses with data, you don’t want the computing capabilities of your technology to hold you back in your field trials.
Consideration of Experimental Design
With the correct experimental design and statistical analysis, researchers can address the research hypotheses. Among others, researchers can identify and isolate the effects of natural variation that occur in the field, and determine if the differences between treatments are real.
The final step of field trial management is inference and knowledge transfer. The research conclusion - accepting or refuting the underlying hypotheses - allows for informing future practices or approaches. This step is what drives the advancement in agricultural practices, products and ideas.
The Challenges in Field Trials
- With large experiments and multiple researchers, standardization of protocols and data collection is essential. These discrepancies can create confusion, misnoted data, misunderstood notes, and result in inability, or underperformance, when it comes to aggregating it all in the end.
- There are numerous challenges in field trials — personnel management, finances, equipment maintenance, in-field maintenance, data collection, and data analysis. Without a knowledgeable and prepared team, one person can be left with the responsibility of wearing many hats. When it comes to agricultural research, timing is extremely important and there isn’t much room for error in tasks such as data collection or fertilizer application.
- When collaborating with other people, or having multiple people collecting data, the preservation and integration of data from various sources can be challenging. When there are differences in measurement units, missing information, and formats used, the compilation of data can be frustrating and hinder potential success. Digital data entry helps eliminate errors from manual pen and paper collection and record sharing.
- Poor planning and implementation of statistical experimental designs are some of the most common challenges in field trials. For example, failing to leverage legacy field trial data or replicate trials can lead to biased and weak conclusions. Also failing to communicate the trial workflow with other collaborators can result in differences in planting dates, management approaches, missed data points and other factors that could end up affecting the end results of the field trial.
Resources and Approaches for Field Trial Management
Conducting and managing field trials can be a time-consuming and tedious process. But with the advances in technology in recent decades, there are systems and applications that can aid with trial design and planning, data collection, quality control, storage and analysis.
For example, a field trial management system can provide the infrastructure necessary to assist with trial design and planning, data collection, organization and analyses. A single system can help address several of the challenges in field trials and provide the necessary infrastructure for future trials. Mobile applications extend trial management with flexible data entry and task assignments at field level. Another potential resource could be to collaborate with others that have nearby fields. This could be to add an additional field trial test location for your experiment or use data that they may have collected from previous trials that are the same as your own.
Field trials are extremely vital parts of the agricultural research process and the findings are what continue to drive improvements. There are several challenges within each of the components (e.g., experimental design, data collection, data analysis and the application of results), but they can be approached with recent technological developments.
Ron Baruchi is the president and CEO of Agmatix. With over 20 years of experience in the technology sphere, Ron has taken this experience to the agricultural sector. Passionate about using data to solve complex problems, he has used his expertise in technology with Agmatix to improve crop yields and quality while limiting environmental impact.