Like new cars are taken out for test drives before purchase, new adjuvant products go through field studies to see how they perform in a realistic environment before they are finalized. Once extensive efforts towards the development of a new product are finalized, it is time to take it for a “spin.” Field studies are a way to bridge theory and cropping systems.
Field studies are not limited to academic institutions only. Agricultural holdings and farmsteads utilize them to test new concepts. At Exacto, we use these studies to test the performance of newly developed formulations in food and ornamental crops. It all starts with a hypothesis, in other words, a question that can be answered simply and effectively. For example, we may want to understand how an oil-based adjuvant tank mixed with glufosinate will impact the control of giant ragweed (Ambrosia trifida). Understanding these critical factors of field study design can help end-users realize the value of this type of research:
Understanding the field’s history is the first and foremost important factor when designing a field study. Once a study objective has been determined, choosing a location that limits variability such as soil texture, fertility, or pest pressure, is essential to eliminate data bias. For example, giant ragweed (picture 1) has a strong dominance over parts of Exacto’s local site. Therefore, the strategy is to use post-emergence herbicide and adjuvant trials to effectively manage broad leaf weeds. In comparison, studies focused on grassy weeds may be perfectly placed in areas where giant ragweed has not established itself. When possible, place studies in the locations that equally account for the variability within each strip. Also, if crop yield is a critical assessment of the study, avoid areas of known high or low productivity to reduce bias in a treatment.
Picture 1: Abdullah Albasri, Exacto Research Specialist in a highly infested giant ragweed area in our Sharon research field.
Upon location determination, decide each strip’s dimensions (plot or replication of each treatment within the field). The measurements for each strip must match throughout the study so that evaluations are done fairly (picture 2). Overall, a well–replicated study will have 3-4 replications that are randomized within the study area. Replications help to reduce data bias due to the lack of homogeneity.
Picture 2: Adjuvant study layout with treatments randomized within the area. Each flag indicates the location of the two center rows, where treatment assessment occurs.
It‘s challenging to identify a field location without variability (picture 3). While it may be a result of mother nature, researchers must be prepared for the challenges imposed on the study to minimize data skewness. Lastly, a control treatment must be included to compare the other treatments. Control treatments provide a baseline and are commonly defined as the standard practice utilized in a grower‘s field. In our case, the controls are typically sprayed without an adjuvant in the tank mix. A key component of herbicide-adjuvant studies is the rate as to which the active ingredient is used. While growers must always use herbicide labels to determine how much product to put in the sprayers, researchers need to consider a lower rate. A good rule of thumb is half of the amount recommended by the label. By doing so, weed control will be less effective which allows for adjuvants to show off whether they made a difference in weed control.
Picture 3: The research plot in Sharon, WI, shows uneven weed pressure.
Planting border rows (picture 4) between treatments minimizes the confounding effects of an adjacent treatment. Border rows should be considered any time an application is presumably going to impact crop growth or health. For example, border rows are highly important for field studies involving pesticide application technology where the off-target movement (drift) generated by foliar applications is tested. While it increases the area necessary to conduct a study, border rows are a convenient and easy method to minimize the confounding influence of a neighboring treatment. For example, we commonly use a 4-row treatment strip for the test applications, but only evaluate the middle two rows to avoid border effects.
Picture 4: Young corn field (~20 days after emergence) highly infested with foxtail. The wooden stake indicates the two center rows of a treatment area, where evaluations will occur. One row on each side of the center rows is the designated border row.
Careful and consistent management practices are pivotal in a successful field study. Holding variables constant, except for the variable of interest, will allow for direct result comparisons among treatments. In that way, you can compare “apples to apples” since the only factor changing in the study is the factor being tested. Any circumstances that change over time will cause difficulties in interpreting the results.
Another key component of a field study is a clear schedule and method for collecting data. Researchers record observations throughout the growing season to fully understand the story being told. In herbicide-adjuvant studies, the two weeks that follow application are paramount to draw conclusions. Treatment efficacy is the most crucial factor in herbicide-adjuvant research. Therefore, a visual assessment of the control percentage inflicted by the treatment is recorded relative to an untreated weed on a pre-planned schedule. Due to the subjectiveness of the damage scale, it’s recommended that evaluations are done by a single individual using an untreated weed as a reference. Careful assessments of weed species present in each plot are then recorded along with any other relevant observations about the study performance. Collecting images is also helpful for assessment verification, drawing conclusions, and accounting for variability within the study.
Picture 5: One plot within a soybean study. The dying weeds within the two center rows were the subject of a herbicide-adjuvant study, where weed damage was visually evaluated on a scale of 0-100 % control.
Conducting the study is only half of the battle. It is equally important to apply the correct method to analyze the data and carefully interpret the results. Given that at least three replications are present, statistics can be used to determine if variations are attributed to the treatment or factors not controlled by the study. Statistics enable a strict process to compare treatment results. The term p-value is common terminology used at the benchmarks of 0.05 or 0.10 (5 or 10% of significance level). For example, if a study uses the p-value of 0.05, we are 95% confident that the differences observed in the study originated from the treatments. Least Square Differences (LSD) are also frequently used to explain field data to determine the smallest difference allowable between two treatments to detect significant differences. Even if treatments are not statistically different, the data is still valuable as it explains that treatments did not result in changes and additional strategies need to be considered. Simply knowing this is beneficial to understand more about the conditions and performance of a given field and aids future management decisions. No difference is just as valuable as a measurable difference in herbicide adjuvant performance.
A field study results in an understanding of the role the adjuvant can play with active ingredients within different environmental conditions. Adjuvants are not created equally and understanding their performance is key for optimizing pesticide applications. Field studies highlight the differences between the various adjuvant products on the market and supports the decision-making process for our end users: growers. A well-designed and executed field study creates trust in products and shows the added value they deliver. With increasing weed resistance and the lack of novel active ingredients in the market, optimizing the effectiveness of existing pesticides is becoming even more critical. Adjuvants can help to enhance the effectiveness of main active ingredients, reducing weed survival, and therefore limiting herbicide-resistant development.
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