The methods and tools for agricultural research data analysis are advancing rapidly. The increased adoption of AgTech is considered an essential step to address global food security whilst meeting sustainability goals. Agriculture contributes to numerous sustainable development goals however accepted methods for linking research outcomes to sustainability impacts are missing.
Yet the promise of new technologies does not always carry over from trials to real-life conditions, and the diffusion of many technologies, varieties, and chemistries remains limited. Improved agriculture analytics is needed to make accurate predictions at the farm level that can lead to more cost-effective, sustainable, and environmentally sound agricultural production. However, the quality of the information obtained from a trial is only as good as the rigor applied and the interpretation of the results.
So, what are the challenges in agriculture analytics, and how can cloud-based tools help in planning field trials, collecting, disseminating, and analyzing data? Challenges include the cost of running agronomic trials which add considerably to the costs of bringing new products to market. A well-planned trial increases the likelihood of success and is easier to implement, manage, and report. However, trials require a lot of dedication and the investment of time and money.
Along with financial aspects, agricultural field trials face additional critical challenges, including the complexities of data movement and sharing, communication hurdles, legacy system limitations, and issues in both short-term and long-term data collection and analysis, all of which profoundly affect efficiency, data integrity, and the effective adoption of new technologies. Let’s dive deeper into these challenges and explore their implications.
Legacy Solutions Bring Complexity to Agricultural Research Data Analysis
The intricate layers of planning, data collection, analysis, and presenting the results bring in complexities, especially for Ag Input companies and Contract Research Organizations (CROs). This complexity is accentuated when the weight of legacy solutions and practices is added to the mix.
The digital revolution has transformed most industries and can transform agricultural research data analysis. Yet the adoption of new technology within the agricultural sector often lags other industries and some practices in agronomic research still rely heavily on legacy solutions. These methods, while tried and tested, often act as bottlenecks in the swift progression of trial management.
Long production cycles within farming and the need to consider the long-term environmental and sustainability impacts of farming methods and systems means that agricultural research is a long-term activity. Many field trials often last years. There is a need for a long-term understanding of farm production systems, technologies, and varieties, and a need to integrate and analyze older data.
Data Movement and Sharing
Transferring information between systems and stakeholders using older methods is tedious and can lead to potential data loss or misinterpretation. Traditional methods of moving and sharing data, like on-prem systems, are not just time-consuming but fraught with risks. Lost or corrupted data can set back trials by months.
New methods such as data-driven decentralized breeding (3D breeding) can scale up varietal testing in larger sets of environments. This approach leverages extensive datasets and collaborative networks of breeders to enhance the precision and efficiency of crop improvement by analyzing genetic, environmental, and historic performance data. Furthermore, by targeting farmers’ evaluations, 3D breeding can significantly speed up genetic improvement. This can contribute to closing the gap between expected and realized gains in improved crop technologies.
Communication and Collaboration
Legacy means of communication, whether it be through fragmented email chains or paper correspondences, are slow and prone to miscommunication. Legacy methods do not have the immediacy and efficiency of modern communication tools. This hampers swift decision-making and on-the-fly adjustments.
New cloud-based agronomic field trial data collaboration tools offer a seamless way to share information with colleagues in different locations, and institutions in real-time.
Data-driven approaches build on larger volumes of data and flexible analyses that link different datasets across domains and disciplines. The use of large datasets and computational methods has transformed the practice of crop breeding.
Data Collection
The age-old practice of using pen and paper is not only time-consuming but is also prone to human error. New agronomic data collection tools offer real advantages. Think about the contrast between jotting down observations with a pen on paper versus instantly inputting and syncing data via a mobile app.
The former can be inaccurate, inefficient, and doesn’t allow real-time collaboration. Compare this with a mobile app where data is uploaded in real-time with minimal inaccuracies.
Analysis
Post-trial data wrangling on Excel sheets feels archaic and can be prone to human error. Agricultural field trial data analysis is a time-intensive process that often muddies the clarity required to draw precise conclusions.
A data-driven approach can analyze complex interactions, allowing crop breeders to deal with trade-offs in improving a larger set of complex traits while maintaining diverse breeding populations. This potentially helps combine production traits with quality, use, and environmental traits, guiding genomic selection toward the establishment of multiple allele pools targeting local needs.
Cloud-Based Solutions Move the Starting Line Forward
Agronomic trials are generating more and more data. For example, novel sensor technologies have drastically increased the amount and diversity of phenotypic data from agronomic field trials. Data from agronomy experiments are typically collected and stored in multiple minimally documented computer files.
Additional information is also often entered and archived in field books or diaries. This means that data manipulation is generally cumbersome and error-prone, and data loss is frequent.
However, cloud-based solutions are changing the game for agronomic research. Planning crop trials and the analysis is easy when all data is in a single place. Instead of disparate pieces of information scattered across different platforms or tools, all data is aggregated and stored in a single, accessible place. Holistic cloud-based systems make this seamless integration possible, reducing the friction often associated with trial planning and execution.
- Unified Data Storage: Having all your data in one accessible place means no more searching through piles of paperwork or different systems. Everything is instantly accessible, making the decision-making process more agile.
- Holistic Approach: Agronomic cloud-based solutions and tools provide an all-encompassing platform where data collection, analysis, and communication can happen seamlessly. This integration reduces friction, ensuring trials progress smoothly.
Agmatix: The Dawn of a New Agronomic Tomorrow
The Agronomic Trial Management solution by Agmatix is not just another tool; it’s a paradigm shift within agriculture data analysis. Its capabilities allow data to cascade smoothly across systems and locations, offering:
- Advanced Data Collection Benefits: Emphasizing real-time data collection tools ensures timely and accurate information, minimizing lags and errors. With tools tailored for next-gen field trials,
Agmatix ensures that data is collected accurately, efficiently, and in real-time. Agmatix’s Agronomic Trial Management solution ensures data moves without hitches, from collection to analysis, ensuring decisions are based on the most current data. - Legacy Trial Data Integration: Agricultural research software from Agmatix allows teams to integrate older trial data, ensuring continuity and a comprehensive understanding. Recognizing the value of historical trial data, Agmatix facilitates the smooth integration of this data, ensuring a richer database for analysis.
- Strengthened Collaboration: Whether it’s between departments, with CROs, or between CROs and their clients, seamless collaboration is facilitated by the efficient sharing of data. Whether it’s inter-departmental collaboration or communication between CROs and their clients, Agmatix provides tools that foster seamless teamwork.
- Operational Efficiency: A 360-degree view of trials, tasks, workloads, budgets, and real-time data visualization streamlines processes and ensures everyone’s on the same page. From overseeing trials, managing tasks, and monitoring workloads and budgets, to real-time visualization of data, Agmatix brings operational efficiency to the forefront.
- Enhanced Data Analysis and Presentation: Armed with tools designed for agri big data analysis, Agmatix simplifies the complex task of drawing insights from vast data sets. This ease of analysis ensures that the results are not only accurate but also presented in a manner that’s easy to understand and act upon.
With sophisticated algorithms, data interpretation becomes sharper, enabling better and more timely decision-making. Plus, presenting this data in intuitive, visually appealing formats makes stakeholder communication more impactful. - Insightful Connections: Agmatix stands out in allowing single and cross-trial analysis. The capability to connect insights for both single and cross-trial analysis ensures a broader perspective, which is invaluable in agronomic research. This interconnected approach paves the way for insights that can be pivotal for success.
Agmatix turbocharges the trial process and agricultural research data analysis. The platform not only facilitates faster data collection and analysis but also ensures a quicker time to market for new varieties and products. As the adage goes, time is money. In the world of agronomic research, Agmatix proves that this has never been truer.
For insights into how agriculture data analysis is shaping the future, click here.
