Development of Appropriate Participatory On-Farm Trial Designs for Sustainable Precision Agriculture Systems
This research project is successfully underway. There are seven farms from Illinois, Indiana, Kentucky, and Arkansas collaborating in on-farm research, and we are using data from University of Arizona USDA-ARS. The seven farms have conducted at least one year of on-farm planned comparisons with farmer-chosen treatments. One of the farm datasets has been analyzed and subsequent farm management decisions made for 2005, while other datasets are in the process of being analyzed. Additional farms are being identified to serve as collaborators. The researchers are making presentations at local grower groups concerning on-farm experimentation.
Short-term outcomes include:
Farmer-collaborators receive assistance in farm management decision making.
Identification of alternative experimental designs conducive to on-farm planned comparisons.
Farmer-collaborators provided with the opportunity to experience the advantages of appropriate on-farm trial designs.
Identification of appropriate statistical analysis methods appropriate for research using precision farming technologies.
A renewed relationship among farmers, researchers, and extension developed.
Farmer-collaborators are able to make better decisions from participatory research.
Farmer-collaborators empowered to use local information instead of depending on external information sources for large geographic regions.
Farmer-collaborators are able to decide whether industry claims are true, which should improve confidence in production information.
Reduction of over-application of inputs, which will reduce pollution potential.
Strengthened community relationships will emerge from cooperation between farmer-collaborators and universities.
Long-term systemic changes:
More farmer-collaborators conduct on-farm trials.
More farmers conduct on-farm research in partnership with universities.
Reduction of reliance upon generalized recommendations based upon large geographical scales.
Increased confidence in farm management decisions based on localized information.
After the first year of on-farm planned comparison research, the farmer-collaborators and researchers gained experience in what works for this type of research. Challenges from both field implementation and computer analysis were overcome. From these experiences, more practical experimental designs will be used in the 2005 and 2006 seasons that will not only be easier to implement at the farm level but also be more conducive to analysis. One farm had planned comparisons from previous years that fit research criteria and when researchers analyzed the data, the farmer offered to present the findings to other farmers during Purdue’s Top Farmer Crop Workshop.
* Identification of alternative experimental designs conducive to on-farm planned comparisons.
Traditional and alternative farm-level experimental designs have been implemented on farmer fields. Currently, these datasets are being analyzed so that results can be returned to the farmers before 2005 management decisions are made. Benefits of each experimental design are being documented from both farmers’ and analysts’ perspectives.
The graduate student has been invited to present information to local grower groups about experimental designs for conducting successful on-farm research. Further, the researchers have made personal farm visits to each of the farms in the North Central Region.
On the basis of 2004 experiences, more practical experimental designs will be used in the 2005 and 2006 seasons that will not only be easier to implement at the farm level, but the data will also be easier to analyze.
* Farmer-collaborators provided with the opportunity to experience the advantages of appropriate on-farm trial designs. Work is on-going.
* Identification of appropriate statistical analysis methods for on-farm research using precision farming technologies.
Several spatial and traditional analysis methods are being used in on-farm collaborator datasets to compare the differences regarding benefits and ease. From theoretical simulations, several methods have been proven to be superior to others and will be evaluated in this analysis. Statistical diagnostics are used to ascertain which models fit the data and to correctly specify the experimental model. Statistical methods used in spatial analysis include: spatial error model, spatial lag model, cross regression, and geostatistical methods.
* Farmer-collaborators are able to make better decisions from participatory research.
* Farmer-collaborators are empowered to use local information instead of depending on external information sources for large geographic regions.
After reviewing their experimental results, the farmer-collaborators decided to make a change in their 2005 soybean plant population management. Instead of planting the soybean seeding rate recommended for large geographic regions, they are going to plant uniform population rates appropriate for their farm. These rates are substantially lower than the rates recommended for their region.
* Farmer-collaborators able to decide whether industry claims are true, which should improve confidence in production information.
Work is on-going.
* Reduction of over-application of inputs will reduce pollution potential.
Work is on-going.
* Strengthened community relationships will emerge from cooperation between farmer-collaborators and universities.
Farmer-collaborators and researchers contact each other directly at this point. In the future, it is expected that local extension staff will become a larger part of the relationship.
Long-term systemic changes:
* More farmer-collaborators conduct on-farm trials.
* More farmers conduct on-farm research in partnership with universities.
* Reduction of reliance upon generalized recommendations based upon large geographic scales.
* Increased confidence in farm management decisions based on localized information.
These long-term objectives will be evaluated toward the end of this project.
Impacts and Contributions/Outcomes
The planned comparisons the farmers chose to use were important to the farmers and were intended to answer existing production questions. Several farmers were eager to get their yield data to the researcher for analysis and subsequent decision making as quickly as possible to begin planning for the 2005 growing season. Wintertime decisions include which varieties to order.
One farmer-collaborator will reduce soybean seeding rates from 130,000 seeds per acre to 100,000 seeds per acre on the higher productivity soils, which should lead to reduced costs of production and improved profitability. On the relatively poor soils, seeding rates will remain at the rate recommended of 130,000 seeds in order to achieve the maximum profit possible. This farm will be ordering the appropriate amount of seed for the farm, which is a substantially smaller amount that would have been ordered if this experiment hand not been conducted.
The graduate student was invited to present information at a local grower group on March 21, 2005 describing how on-farm experiments can be conducted so that growers will have increased confidence in their research results. As a result of this research, we expect renewed grower interest in conducting on-farm experiments. It is anticipated that this interest will not be limited to the initial farmer-collaborators, but will extend to a wide range of producers in the North Central Region and beyond.
In addition to the increased production efficiency that will result from the on-farm experimentation for participating farms and their associated grower groups, a renewed relationship among innovative growers and universities will be created. Many farmers are already contacting the researchers directly, which creates renewed opportunities for local extension staff to work with these farmers.
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