Many famous psychologists have been attributed as saying that the best predictor of future behavior is past behavior; and, that perspective doesn’t only resonate with human nature.

It aligns well with nature and how to map the unpredictability of each season to identify key trends and align variables to understand how that may impact future successes. In doing so, it can help to inform future action, whether it’s analyzing how products were used and the impact to the environment or crop growth over time. Or, it can be used to identify key trends to related to factors like water use and climate change, and how that has impacted yields.

Beyond providing insight and incremental benefits for in-the-field management strategies, historical data sits at the core of innovation for agriculture – particularly within agronomy, which faces challenges given the seasonality of research, site-specificity of results, and unpredictability of key variables, like weather.

Challenging Existing Paradigms with Historical Ag Data

Doing things the way they’ve always been done offers comfort and reduces the risk for error – after all, it means the practice is well understood and known. However, in evaluating some of the biggest innovations in agronomy, they focused on challenging traditional practices and theories.

For example, the widespread use of synthetic fertilizers was once considered the most effective approach for boosting crop yields. However, historical data analysis revealed that continuous and excessive fertilizer use could lead to soil degradation and nutrient imbalances. This finding prompted a paradigm shift towards more sustainable and balanced nutrient management practices, such as precision fertilization and integrated soil fertility management.

Similarly, data showcases that widespread use of synthetic herbicides over the past 70 years has led to the evolution of herbicide resistance in hundreds of weeds. With this data in hand, the industry is focusing on how to leverage technology – like remote sensing – to create more targeted weed management strategies.

By examining long-term climate records, researchers have observed shifts in temperature and precipitation patterns, prompting the need for adaptation strategies. Historical data has been instrumental in identifying suitable crop varieties for changing climates and developing climate-smart agricultural practices.

And, evaluating historical data has been instrumental to identifying more sustainable and resilient farming systems based on studies of past agricultural systems. This has helped define many of the sustainability initiatives in agriculture today; helping to pave the way for a more productive future for our world.

Digging Into the Archives: How Does It Impact the Future?

The Morrow Plots – which were established in 1876 in Illinois – are the world’s second-oldest continuous field experiments (and oldest in America). These plots focused on crop rotation, inputs, and fertility.

While researchers involved in these plots didn’t have access to the technology that we had today, their practice was similar to how we conduct research today – and, thanks to their meticulous work and documentation, soil samples for these plots still exist today.

Likely the world’s oldest (and largest) soil archives collection, the University of Illinois Urbana-Champaign holds record of over 8,000 soil samples dating back to 1862 – including those from the Morrow Plots. Researchers have described this as a road map to identify how the soil has changed; a quest they will embark on in the coming years to understand soil health and how it has changed over the past century and a half.

Since much of this work still needs to be completed, it’s hard to say what will be revealed through the analysis.

Will it showcase that nutrient imbalances in certain areas have existed for long period of times?

Given changing land use and human populations in certain areas, will researchers be able to understand how that impacts the pH balance of soils – and what does that mean for neighboring farmland?

Will we be able to track the impacts of changing weather patterns and what that means to soil quality and health?

And, what can we learn from the profiles of these soil samples, given changing conditions? How can they inform current growing practices and help us innovate for the decades to come?

This historical data can be used in a few different ways. For one, it can be helpful to track variables over extended time to identify gradual shifts and emergence of new patterns, as well as getting a deeper understanding of the cumulative effects of management practices, environmental factors, and climate changes.

And secondly, like the studies noted above, it can help to inform trend analysis to see how the changes in soil samples may correlate with trends in crop yields, pest and disease outbreaks, and other important parameters. In doing so, historical data like these soil archives can detect anomalies, predict future outcomes, and assess the effectiveness of interventions.

Looking Back to See the Path to the Future

Much like uncovering 8,000 mason jars with soil samples in a dilapidated barn, it’s likely that there’s some historical data sitting on the proverbial shelf somewhere that can be immensely valuable.

As we’ve discussed, perhaps the data can help to identify variables over time or even identify key trends based on an agricultural practice or how a product is used. Or, it can be useful to inform future data collection programs – helping to create a historical story about what’s happening in a plot or field.

Regardless of how the data can be used, uncovering what’s available and the story that it may tell – and how it complements existing data collection programs – serves as the first step. A first step that’s never too late to take; instead of giving into the fear of losing a year, know that historical data can help validate data collection practices and mean that you don’t have to wait another year to make changes – giving you the opportunity to dig deeper into what’s working in your research today, while helping you create a path forward to get the right data to validate outcomes.