Demonstration of a practice or product implies that no scientific analysis is done on the resulting information. A non-replicated, side-by-side trial is a common example of this approach.
Demonstrations have value and can help with decision-making but are not as convincing as a replicated trial.
Seaman A. Knapp, a key figure in the history of technology transfer to farmers, is the father of the farm demonstration, which he developed and promoted in the United States during the 1880s until about 1910.
“What a man hears, he may doubt; what he sees he may also doubt; but what he does, he cannot doubt,” Knapp said.
Agriculture and farmers have changed dramatically since then, but there is still value in this approach.
Growers often evaluate a new practice by applying it to a small field and comparing the results with nearby fields, or by splitting a field and applying the new practice on one side and their normal practice on the other.
Likewise, growers and industry reps sometimes place a strip of a new hybrid, herbicide, fungicide or fertilizer in a field to compare it with the rest of the field. These are called demonstrations, and they allow a local comparison of how a practice looks.
This can be an important first step, but problems occur when producers want more than a look. It is simply not possible to reliably compare yields and other quantitative data without a scientific approach.
Growers often bring me yield maps of fields on which they have used a new practice. There are often obvious impacts from these tests, but good, accurate numbers about how much impact the procedure made are impossible to extract.
Local growers have often expressed concern about adopting equipment and technologies that have not been tested in their local agri-climatic conditions and cropping systems
Our cropland areas contain highly variable soil, topography, climatic conditions and cropping systems, which make testing and transfer of new farming technologies difficult.
As a result, design of on-farm testing is a critical first step in accurate field comparison of management options.
Modern technology such as GPS, auto-steer and yield monitoring allows growers to easily conduct simple experiments on their farm and obtain accurate results. They can use accepted methods of on-farm testing to achieve experimental precision comparable to those of intensive university and government research trials.
A large reservoir of data can be collected when these tests are pooled with similar experiments performed by other local growers.
As well, producers can calculate the probability of receiving economic returns from a procedure using current prices and data developed from the tests.
However, farmers should ask the following questions before setting up an on farm test:
- Are they willing to make time for this?
- Do they have access to good equipment for the things they want to test?
- Do they have a good way to measure yields?
- Do they have the time at harvest to take the yields?
- Is there a local resource person, such as government specialists or input agronomists, who they can work with on this?
- Should they consider teaming up with other growers to replicate the trial across a number of locations?
- Should they look for outside funds, such as the ADOPT program in Saskatchewan, to help pay for project expenses?
Farmers should also know what they want to learn, which can take more thought than it might appear.
To do this, determine what you really need to know before you can make a decision about whether to adopt a new practice or product. Design the test to provide that information and avoid comparing too much.
For example, producers could limit the scope of their testing to compare only one new fungicide to an existing fungicide and an untreated check.
The following steps are often taken when laying out a scientific on-farm test:
- Choose an area in a field where long, side-by-side plots can be placed with the expectation that whatever is being measured should be nearly equal.
- Assign the treatments to the plots randomly, such as with a coin toss. This must be done to ensure the layout is unbiased. It might seem obvious, but there are many ways to consciously or unconsciously give an advantage to one of the practices being compared.
- Repeat these processes so there are at least three replications. They could be next to each other, in different areas of the field or even in different fields.
The best results occur when each replication is positioned so that variations in the field, such as high and low areas, soil variations, field borders and fertilizer overlaps, will be encountered equally by each strip in the replication.
Plots should be as long as possible with a minimum length of 200 metres. The width will depend on the width of seeding, spraying and harvesting machinery.
Make a map of the field and plot locations, and keep notes on what you observe throughout the trial year. After conducting all field operations, such as spraying post emergence herbicides, mark the plots with stakes tall enough to be easily found at harvest time.
Record data measurements separately for each strip.
At harvest, ensure that the yield monitor has been properly calibrated.
If weigh wagons or scales are being used, cut a full header width out of the centre of each plot and weigh the grain from each plot separately. As well, measure the length of the harvested strip and header width to calculate the area of each plot.
Portable truck scales or weigh wagons typically provide two to five kilogram accuracy per load cell.
As a result, proportional error due to weighing equipment accuracy will be greater with smaller plots, particularly if yields are low. You may want to collect grain samples for moisture content, test weight and quality measurements.
Once you have collected your data and run statistics to determine if the recorded differences are statistically significant, the final step is to run an economic analysis.
Using your costs and prices, calculate the return for each of the treatments to see if there is an economic advantage.
One way to do this is with a simple program called Agstats02, which will run statistics on on-farm tests. For more information on Agstats02, check out pnwsteep.wsu.edu/agstatsweb/index.html.