Field trials are an essential component of developing agricultural inputs. They offer real-life insights into a product’s performance and how it functions within complex agricultural environments. This is especially true for managing crop protection efficacy and safety trials. These trials can be complex and require managing technicians, tasks such as treatment applications or planting, and analyzing the data after it has been collected or entered. Unlike lab trials, field trials are subject to unpredictable variables such as climatic conditions or low pest populations that could compromise trial success.
Developing a new molecule from start to finish can be an extensive process. Even if this research shows a lot of promise in lab applications, these molecules may interact differently in less controlled environments such as field applications. For example, products that work well as a standalone, may be antagonistic to other products growers typically use within the same timeframe. Understanding these risks and variables is why field research is so crucial.
Effective management in field trials is paramount because the data integrity hinges on well-thought-out and executed research. Quality research leads to quality data which can then be used for decision making and model creation. Field trial management is not only important for organizations researching their own products but for growers as well. Growers are constantly looking for science-based solutions to the complex problems they face in the field.
One major benefit of the technological advancements we have today is big data. With the aid of sensors, data platforms, and extensive research we can create more data layers than ever before. These layers help us understand more about what is happening in the field and the response connections to both biotic and abiotic factors.
Big data will continue to play a larger role in agriculture as it helps piece together multi-dimensional datasets that look at time, location, treatment, and results to generate simulation models. With more data comes higher computational opportunities.
Best Practices in Field Trial Management
There are many factors to consider when setting up and designing a field trial. Unlike lab trials, there are many unpredictable factors in field research. Abiotic variables such as soil composition and changing weather conditions may impact the success of crops. Biological factors such as unintended pests may also create unpredictable variables that impact data and research.
For this reason, trials need to be executed at multiple locations. These locations not only act as a replicate resource but also show how a product may interact with crops in different environments. Some crop protection products may have different interactions with different soil types, subspecies, or environmental factors like the weather. Crop protection field trials are fundamental to gathering data and understanding these different interactions.
Within a field, treatment replicates will likely have varied results. This is why the trial design is important. Trial design not only factors in the appropriate amount of replications and randomization but must also consider the capacity for gathering data efficiently and effectively, understanding the risk of contamination, and field history.
Traditionally, data collection is performed by field technicians, with data entry occurring manually at a different time. Data collection and data entry are both time-consuming tasks that add up. This is why researchers try to design trials that can be conducted as efficiently as possible.
The Agronomic Trail Management system is a field trials data tool that makes many tasks in research seamless. Researchers can design trials, assign tasks, and communicate all on a single platform. Real-time data collection and recording reduces the time to analytics.
A crucial research aspect is protocol standardization. When managing multiple trial locations for the same project, it’s important for congruency. Application methods, timings, and data collection must all be executed in the same manner. Minor variations in methods may lead to compromised data. With Agmatix any modification or update to the protocol is promptly reflected across all associated trials, guaranteeing standardized procedures and consistency throughout. With secure cloud-based AI technology, organizations can store data and access it safely as needed. This allows organizations to collaborate more effectively and query data or run comparison analyses no matter which trial it is associated with. They can also conduct the necessary audit trails for regulatory purposes.
Integration of Big Data in Crop Protection
Field trial software streamlines the process of analysis and report generation. With streamlined processes, researchers can focus more on the research outcomes and spend less time doing tedious tasks.
During crop protection efficacy trials, things may not go as planned. Data that doesn’t fit the hypothesis becomes ever more useful as researchers can disseminate what happened and what they can expect from similar situations in the field. This capability is especially important for field trials of crop protection products. With changing environments and pest dynamics, understanding the different interactions crop protection products have in different regions, at different rates, and at different times is important for understanding how to set up growers for success when choosing that product.
With the advent and implementation of sensors in agriculture, growers and researchers can track real-time data on the ground. These sensors may indicate soil moisture levels, and weather conditions, as well as sensors that monitor the crops themselves. This is yet another layer of data that can be compiled for researchers, agronomists, and growers. While these datasets might seem overwhelming for analysis through traditional means, AI-powered software can efficiently process and analyze them. By leveraging advanced algorithms, our agricultural data analysis tool can ingest massive datasets, conduct in-depth analysis, and facilitate drawing conclusions using smart recommendations and statistical widgets.
Agricultural Research Challenges and Solutions
In research data is everything. Risks are always associated with sharing data. Whether that data is being shared internally or externally, knowing who has access and who doesn’t is important for organizations in protecting their sensitive information. When data is sent without encryption or authorization locks, it runs the risk of ending up in the wrong hands.
We live in a technological world where data security is top of mind. Having a dependable system that protects data while still supporting ease of use when sharing or accessing is key. Regulatory compliance requires data to be stored for a certain amount of time. Storing this data on a secure platform ensures accessibility without compromising security.
With the Agmatix Agronomic Trial Management tool, share data with authorized users knowing that your data is protected. With cloud-based AI technology, sharing data is made easy and can be accessed from any authorized mobile device. With the capability to load legacy files onto the platform, organizations can preserve historical data while integrating new research into the platform seamlessly. This not only benefits the organizations that utilize this tool but also agronomists and growers who have access to new technology in the field more quickly thanks to organizational efficiency and security.
Using the Agmatix Platform
With increased efficiency and accuracy, Agmatix creates agro-informatics tools that enhance the capabilities of research teams. Managers can track and send tasks to stay current on what is happening in the field. Data is visible in real-time, and pre-built widgets enable them to perform no-code analytics.
Managers across the organization can access authorized data and get real-time information on pending and completed tasks, data, and communication efficiently with one another. Organizations will reduce the time it takes to navigate the research and regulatory process so they can go to market sooner and bring new tools to growers and agronomists.
Conclusion
For a successful trial, collaboration, standardization, and task management are key in ensuring high-quality data. Having the ability to design trials, create tasks, and communicate all on a single platform gives research teams the tools they need to be efficient and effective. Conditions change in the field and research teams need to stay nimble while also performing time-sensitive tasks as needed.
Tools such as the Agronomic Trial Management tool will be indispensable for researchers performing field experiments on crop protection products, thus helping research teams learn more about their products in realistic application uses. Big Data will change the collaborative efforts of researchers, agronomists, and growers for the better. With information sharing, the agriculture industry will be able to combat big issues, such as invasive species and the precision application of pest control products.
