Everyday you see, read or hear about farming practices that hold promise for increasing the profitability of your farming operation. The question you typically have is, "Will it work on my farm?"



David L. Varner, University of Nebraska-Lincoln Extension educator, offers the following discussion and guidelines for finding out through on-farm research:



"Will it work on my farm?" To answer this question, you can conduct an experiment on your farm in a manner to produce reliable, credible information. Typically, experiments are conducted with the producer's equipment, on the producer's land and using the producer's management practices.



The question of whether research results are relevant to the producer's soil types and management strategies is answered immediately. Best of all, the producer decides what topic to research.



Applying one treatment to half a field and another treatment to the other half is not scientific and may result in decisions based on poor information. Certain experimental procedures should be applied to obtain reliable information on alternative practices on your farm.



Following these procedures will ensure that you, your neighbors, business associates and others can rely on your results and will see the value of conducting scientifically based on-farm research. You can easily conduct valid, field-scale on-farm research using your equipment, on your land, employing your operation's management practices.



On-farm research has three basic components:


  • formulating a hypothesis;

  • testing the hypothesis with experimentation; and

  • drawing a conclusion based on the data.



  • Formulating a hypothesis



    Develop a well-defined research question that can be answered with data that you can collect from the field. Try to keep the question focused on one practice. Will changing this practice, e.g., decreasing planter speed, affect crop performance and profitability?



    Testing the hypothesis



    Decide what treatments to compare, design the field layout of the experiment and determine what you will measure. Two experimental layouts are typically used in on-farm research. The proper design is determined by the number of treatments necessary to test the hypothesis. Two-treatment experiments use the paired-comparison layout. Experiments with additional treatments can be designed using the randomized complete block layout.



    By using either of these layouts, you can measure differences between treatments at a given level of probability. Randomization and replication are important components of a well-designed, on-farm research experiment. Randomization ensures that one treatment is not inadvertently favored over another treatment. Replication reduces the possibility that results are due to chance rather than the treatment. These two factors separate demonstration plots from on-farm research experiments, which can be used to make valid conclusions and ultimately wise business decisions.



    Information such as row, planter, combine, sprayer and fertilizer rig and other relevant implement widths must be known prior to the comparison design. Incorporating this data into the experimental layout will improve the efficiency of managing the experiment as treatments are applied and harvest is conducted.



    Be sure to mark the treatment locations well with field flags, wooden stakes, global positioning system equipment or a combination of these tools. Draw a sketch of the experiment layout for reference at harvest time. Buffer areas between treatments are important to ensure that treatments do to not influence each other. Including a field border/buffer is important to eliminate influence from compacted end rows, fence line grasses, or field roads.



    Harvest weights may be collected using a properly calibrated weigh wagon or yield monitor. Moisture and test weights should be collected for each grain yield treatment measured.



    In addition to crop yield, grain moisture and test weight, you may want to collect additional data relevant to your on-farm research experiment hypothesis. Examples of this data include soil fertility, plant height, insect thresholds, weed densities, planting and harvest populations, and grain protein analysis. This data can be statistically analyzed if collected using the same procedure used for harvest.



    Keeping a diary of crop and weather conditions throughout the growing season is an invaluable resource when the time comes to draw conclusions from the experiment. A photographic record of observed differences during the growing season also may be useful.



    Drawing a conclusion



    Data collected from a scientifically designed on-farm research experiment can be statistically analyzed and interpreted to determine whether real differences are present among treatments. It's difficult to draw conclusions by simply looking at the raw data. Statistical analysis determines probabilities that the differences were caused by treatments versus chance (random variation or error).



    Once yield and/or other treatment differences are determined, you should focus on economics. What is the cost:benefit ratio? Is the advantage of a given treatment worth the cost of implementing it? Non-tangible benefits such as improved soil quality and environmental improvement also should be considered. Conclusions should generally be drawn from comparisons repeated in more than one location and/or year.



    More information regarding on-farm research protocol and results of numerous on-farm research comparisons conducted by Nebraska producers can be found at the UNL Extension On-Farm Research Web site.



    SOURCE: Crop Watch from University of Nebraska Institute of Agriculture and Natural Resources Cooperative Extension.