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

Project Overview

Project Type: Graduate Student
Funds awarded in 2003: $10,000.00
Projected End Date: 12/31/2007
Region: North Central
State: Indiana
Graduate Student:
Faculty Advisor:
James Lowenberg-DeBoer
Purdue University

Annual Reports


  • Agronomic: corn, cotton, rice, soybeans


  • Crop Production: foliar feeding
  • Education and Training: decision support system, demonstration, extension, farmer to farmer, focus group, networking, on-farm/ranch research, participatory research, workshop
  • Farm Business Management: whole farm planning, budgets/cost and returns
  • Pest Management: field monitoring/scouting
  • Production Systems: holistic management
  • Soil Management: soil analysis


    Comparisons of alternative on-farm trial designs indicated that limited replication were sufficient for making production decisions if the appropriate spatial statistics were used to analyze the data. Five farms from Illinois, Indiana, Kentucky, and Canada collaborated in on-farm research. Select farmers received a spatial analysis report of their on-farm trials over the three-year project period and all farmers were the subject of a case study. Case study results indicated that farmers who received a spatial analysis of their on-farm trials were more confident in their data and production decisions than before participating in this research of on-farm trial spatial analysis.


    Precision agriculture is information technology applied to agriculture, using technologies such as global positioning systems (GPS) and geographic information systems (GIS) to apply appropriate rates of inputs at specific locations. Precision agriculture technologies have spread rapidly in the Midwestern United States with 36% of corn and 29% of soybean area harvested in 2001 and 2002, respectively, with a yield monitor (Griffin et al., 2004). Numerous results on the profitability of precision agriculture have been reported (see Lambert and Lowenberg-DeBoer, 2000 for an exhaustive review).

    Several studies used spatial statistics to analyze data from conventional designs. Bongiovanni and Lowenberg-DeBoer (2002) studied corn response in on-farm data, showing that explicitly modeling the spatial error structure identified patterns lost in typical analysis when spatial correlations are ignored. Hurley et al. (2001) uses a similar spatial regression method. Lambert et al. (2002; 2004) showed spatial econometrics (Anselin, 1988) and spatial regression using geostatistics (Cressie, 1993) yielded similar results.

    The fundamental idea of on-farm trial design is that under certain circumstances the statistical model can be mathematically identical for data from numerous strips and/or subplots, and for data from a smaller number of non-replicated blocks. With the spatial error model, the same information could be extracted from a simpler design with treatments in larger non-replicated blocks by explicitly modeling the spatial error structure, instead of eliminating it.

    Precision agriculture technologies such as GPS, yield monitors and other sensors provide many low-cost geo-referenced observations per acre. Because of the increased ease of gathering data, precision agriculture renewed interest in field-scale on-farm trial comparisons (Urcola, 2003) although experimental designs and analyses were developed for 20th century technology (Nielsen, 2000; Brouder and Nielsen, 2000; Nafziger, 2005). Instantaneous yield monitors have provided opportunities for this type of research to be implemented without interfering with other field operations. Farmers have incentive for conducting their own on-farm trials. On-farm field-scale trials provide farmers opportunity to
    1) verify university Extension recommendations and industry claims under a wide range of production conditions,
    2) to verify if small-plot experiment station research applies to field scales under local environmental and management practices,
    3) gain local information to fine tune production practices, and
    4) test equipment, systems or technology packages.

    Farmers conduct field-scale on-farm comparisons of new products and alternative systems with a range of experimental designs including large non-replicated blocks such as split-field, strip-trials and split-planter trials. While data from strip and split-planter trials has some value to classically trained agronomists and statisticians, unreplicated comparisons are often dismissed as unreliable sources of information because of lack of power in determining treatment differences. Evidence from Indiana suggests that unreplicated split-field comparisons are the most common design used by farmers especially for farmers with yield monitor technology (Urcola, 2003).

    Project objectives:

    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 include:

    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, improving confidence in production information.

    Reduction of over-application of inputs, reducing pollution.

    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.

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.