Development of Appropriate Participatory On-Farm Trial Designs for Sustainable Precision Agriculture Systems

Development of Appropriate Participatory On-Farm Trial Designs for Sustainable Precision Agriculture Systems

Summary

This research is successfully underway. There are seven farms from Illinois, Indiana, Kentucky, Arkansas, and Canada collaborating in on-farm research, plus data from Western Illinois University and University of Arizona USDA-ARS are being used. All farms have conducted at least one year of research with farmer-chosen treatments. Most farms have at least one dataset analyzed and farm management decisions made for 2006. One-on-one personal interviews of farmer collaborators are underway to form a case study of the process. The graduate student is making presentations at local grower groups, state, and national research workshops concerning on-farm experimentation and appropriate spatial analysis.

Objectives/Performance Targets

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.

Intermediate outcomes:
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.

Accomplishments/Milestones

After the first two years 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 2006 season that will not only be logistically easier to implement at the farm level but also be more conducive to spatial analysis. One farmer-collaborator presented the findings to other farmers during Purdue’s 2005 Top Farmer Crop Workshop and is scheduled to co-present with the graduate student at the 2006 MapShots Winter Conference in Indianapolis, IN.

Short-term outcomes:

* Identification of alternative experimental designs conducive to on-farm planned comparisons.

Traditional and alternative farm-level experimental designs have been implemented on farmer fields. These datasets are currently being analyzed so that results can be communicated with farmer-collaborators before 2006 management decisions are made. Some farm management decisions are made prior to harvest including hybrid seed purchases, thus final decisions on hybrids must be made even before the end of harvest to secure discounts and reserve limited supplies.

On the basis of 2004 and 2005 experiences, more practical experimental designs will be used in the 2006 season that will be easier to implement at the farm level and easier to analyze. Benefits of each experimental design are being documented from both farmers’ and analysts’ perspectives. Some of the problems cited for designs derived from small plot experimentation such as split-planter trials are that the analyst may be required to count passes if the design is not electronically recorded with software and GPS and that mathematical problems arise from defining which observations are considered neighbors for purposes of spatial analysis. In addition, the spatial variability is not overcome even with the many replications from split-planter trials. Split-field designs seem to work the best, but some opposition by traditional field scientists has emerged due to the lack of replication, which is deemed by some to be universally required.

The graduate student has been invited to conduct workshops for local farmer peer groups, Purdue Cooperative Extension Service Agricultural and Natural Resources meeting, plus cotton researchers conducting field-scale experiments via Cotton Incorporated in two national locations. These workshops are on experimental designs and spatial analysis for conducting successful on-farm research and tailored to the specific audiences of farmers, consultants, or researchers.

* Farmer-collaborators provided with the opportunity to experience the advantages of appropriate on-farm trial designs.

The Farmer Advisory Panel met on November 13-14, 2005 and : effectively became a peer group bouncing ideas off one another. (See the November Newsletter for the Site-Specific Management Center http://www.purdue.edu/ssmc or the November Monthly Update for the Top Farmer Crop Workshop http://www.agecon.purdue.edu/topfarmer/). Two farmer-collaborators have switched their long term split-planter trial designs to split-field designs, with one farmer questioning his prior farm management decisions, i.e. reduced confidence in what he once thought he had a good understanding.

* 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 under field-scale conditions. These statistical models will be evaluated in this analysis to demonstrate the potential erroneous decisions that the farm manager would make if traditional analysis were used rather than spatial analysis. Hence, the question of what is the cost of a wrong decision will be addressed. Statistical diagnostics are used to ascertain which models fit the data and to correctly specify the experimental model. Spatial statistical methods used in spatial analysis include: general spatial model, spatial regimes, spatial error model, spatial lag model, cross regression, and geostatistical methods.

Intermediate outcomes:

* Farmer-collaborators are able to make better decisions from participatory research. FAP

* Farmer-collaborators are empowered to use local information instead of depending on external information sources for large geographic regions.

Welfare economics theory states that if a technology increases productivity uniformly across all farms, then consumers are the only gainers and that producers are most likely to be at best no better off than they were. We also know that when the demand for a product or commodity increases, producers are better off than they were and consumers are indifferent. Conversely, when individual producers have the opportunity to increase their local production via better use of technology or information, then they are at a relative advantage to those producers who do not exert the effort to adopt the technology or make full localized use of the information. This is analogous to site-specific crop management, where an individual farmer has the opportunity to increase site-specific production by making optimal localized decisions, thus making oneself better off. Consumers again are clearly better off in this case.

After reviewing their experimental results, one farmer-collaborator decided to make a change in their 2005 and 2006 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. In addition, they are testing this on multiple fields, row spacing, and maturity groups.

One farmer-collaborator examined two corn hybrids on several fields in 2004. The optimal chose determined from a traditional analysis changed depending upon field, but the spatial analysis provided a consistent result of no statistical difference between hybrid yields in any field or topography.

* 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.

The graduate student and major professor were invited to present “Effectively Using GPS
in Management” to the 2005 Purdue Extension Agricultural and Natural Resources training. During the workshop, the discussion revealed that some extension agents were performing rudimentary spatial data analysis of their client’s on-farm comparisons. The agents became encouraged to become more aware of their clients’ data analysis needs and the pitfalls and limitation of rudimentary analysis. The discussions also lead to the importance of erroneous data filtering requirements. One agent subsequently attended the Yield Monitor Data Analysis workshop on November 14, 2005.

Farmer-collaborators and researchers contact each other directly at this point. In the future, it is expected that local extension professionals will become a larger part of the relationship. Due to the yield monitor data analysis workshop on November 14, 2005, farmer advisory panel (FAP) members contact each other directly to gain feedback into their decisions and identify issues and potential solutions.

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 and 2006 growing seasons. Wintertime decisions include which varieties to order. Farm management decisions are more urgent than in years past, with substantial price discounts available for hybrid purchases prior to harvest and the need to reserve certain hybrids, many hybrid and planting decisions are made before harvest is complete.

The farmer advisory panel (FAP) met on November 13 and 14, 2005 in conjunction with the Yield Monitor Analysis (YMA) Workshop. The YMA Workshop included suggestions on the appropriate experimental designs, precision agriculture data collection, data cleaning, spatial data analysis, and farm management decision making. Eleven farms attended the YMA by invitation. Three farmer-collaborators representing the FAP were in attendance. The meeting the evening of the 13th provided feedback and direction to the graduate student’s research from the FAP members.

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 recommended rate 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. Reduced seeding rates will increase timeliness during the planting season by reducing downtime for filling planters, thus increasing yields by planting in the optimal time period.

The graduate student presented 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.

This became evident when Cotton Inc. invited the graduate student to conduct half day workshops in conjunction with other conferences. The first was June 29-30, 2005, at Florence, SC in conjunction with the Southern Conservation Tillage Conference. The second was November 15 and 16, 2005, in Austin, TX in conjunction with the Cotton Precision Agriculture Conference. Both workshops were aimed at PhD cotton field researchers who are interested in conducting field-scale research.

The graduate student has also been invited to present “New Methods in On-farm Research” with a member of this project’s Farmer Advisory Panel at the 2006 MapShots Winter Conference for users of MapShots software.

The graduate student and major professor were invited to present to Purdue University Agriculture and Natural Resources Extension in-service training entitled “Effectively Using GPS in Management” to approximately 40 agricultural county agents November 2005.

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. The relationships originated from the research-farmer, i.e. graduate student and major professor with farmer-collaborators and farmer advisory panel (FAP). Extension’s interest grew once they learned of the relationship and are becoming involved, and Extension is fostering relationships with new farmer-collaborators from their own experience.

A procedure for analyzing yield monitor data has been developed into a protocol to allow other researchers, consultants, and farmers who wish to conduct their own data handling and spatial analysis, entitled “Yield Monitor Data Analysis: Data Acquisition, Management, and Analysis Protocol”. This protocol is what we have decided works best for us and is available on the Site-Specific Management Center website at http://www.purdue.edu/ssmc.

An initial analysis of a field-scale study was conducted on an existing dataset from University of Arizona USDA-ARS. Although the study was conducted by USDA and university researchers, it was carried out in a manner consistent with how a farmer would. Even though the dataset was not an on-farm farmer-managed study, it was a field-scale experiment and used as the first dataset analyzed by the graduate student using spatial analytical procedures. The study was presented at the 2005 Beltwide Cotton
Conferences, New Orleans, Louisiana – January 4 – 7, 2005, and the paper is available on the website at http://www.cotton.org/beltwide/ or from the authors.

From the techniques developed for site-specific data analysis gained from this study, two additional graduate student projects, one PhD and one MS, are utilizing these techniques for their research. Both projects are using precision agriculture data to analyze split-field or paired-field controlled drainage field-scale experiments in cooperation with farmer-collaborators. The graduate student of this project has provided guidance in the technical aspects of data gathering, data handling, and data analysis.

Collaborators: